224 results about "Interference graph" patented technology

The invention discloses a precise interference avoidance method and device in a radio system, and relates to the field of signal processing, and the method comprises the steps: constructing a sensing matrix through sensing node data, and capturing a transient interference signal through aperiodic scanning; performing tensor decomposition on the signal data to extract time domain, frequency domain, space domain and modulation domain features, constructing a dual-mode spectrum analysis model, reconstructing an instantaneous spectrogram by using compressed sensing, and predicting an interference mode through LSTM; after the instantaneous spectrogram and the predicted interference graph are fused, threat assessment is carried out through a multi-stage interference classification model; according to the interference category and the threat level, beam forming is optimized, adaptive null is generated, a power density optimization model is constructed, and the transmitting power is dynamically adjusted; an anti-interference frequency hopping sequence is generated based on a chaotic mapping algorithm, and spectrum camouflage and tracking interference resistance are realized. The method has the advantages that accurate identification and dynamic avoidance of interference are realized through multi-dimensional perception, intelligent prediction and adaptive beam forming, and the interference avoidance capability of a wireless system is improved.

The invention provides a mining area deformation monitoring and risk assessment method, which comprises the following steps of: carrying out registration, de-leveling, filtering and phase unwrapping on acquired image data through InSAR (Interferometric Synthetic Aperture Radar), generating a coherence graph, a filtering interference graph and an unwrapping phase graph, carrying out phase decomposition by using a singular value algorithm, separating a deformation phase, an atmospheric delay phase and noise, and carrying out risk assessment. The method comprises the steps of expanding discrete deformation points into a continuous deformation field through Kriging interpolation, carrying out deformation grading by taking a deformation rate, an accumulated settlement amount and a deformation gradient as core indexes, identifying a deformation high-risk area, arranging measurement robot monitoring points in the high-risk area, and constructing a risk assessment model in combination with real-time deformation data of a measurement robot and geological parameters. And evaluating the risk grade of each part of the mining area through the dynamic risk index model, and triggering graded early warning. According to the method, the InSAR and measurement robot technology is combined to solve the data fusion problem of large-range deformation monitoring and local high-precision monitoring, and the dynamic risk assessment precision under the complex terrain of the mining area is improved.

The invention relates to a method and system for measuring the three-dimensional morphology of a metal hydrophobic surface based on white light microscopic interference, and belongs to the field of precision measurement, and the method comprises the steps: firstly, measuring a sample, scanning the surface of the sample, and synchronously collecting a white light interference image sequence containing surface contour information; adopting a bicubic interpolation algorithm to carry out interpolation processing on the acquired white light interferogram group, and constructing a high-resolution interference image sequence; analyzing the time-frequency domain interference signal through fast Fourier transform, effectively inhibiting noise interference in combination with a filtering technology, and accurately extracting an envelope curve of the interference signal; and carrying out Gaussian fitting on the envelope peak by adopting a least square method and positioning a peak value coordinate, and finally reconstructing a high-precision three-dimensional surface topography. According to the method, through advantage complementation of the bicubic interpolation algorithm and the fast Fourier transform method, the transverse resolution is remarkably improved while the longitudinal resolution is kept, and an effective technical means is provided for accurate characterization of the three-dimensional morphology of the metal hydrophobic surface structure.

The invention discloses a roadbed settlement and landslide hidden danger identification method and system along a traffic corridor, and the method comprises the steps: obtaining a time sequence satellite-borne data set of a target region, and carrying out the format conversion of the data format of the time sequence satellite-borne data set; generating an interference pair network based on the time baseline threshold; generating a filtered differential interference image set; performing differential interference phase unwrapping processing on the filtered differential interference image set to obtain an unwrapped differential interference image set; performing atmospheric delay phase correction on the unwrapped differential interference image set to obtain an unwrapped interference image set after atmospheric correction; calculating to obtain the annual average settlement rate of the linear traffic corridor area and a time sequence accumulated deformation result; based on the annual average settlement rate of the linear traffic corridor area, the spatial settlement gradient field information of the linear traffic corridor area is extracted, the settlement rate and the spatial settlement gradient field information meeting the hidden danger judgment standard are output, the roadbed settlement and landslide hidden danger extraction and recognition result of the target area is obtained, and the road area detection efficiency is improved.

The invention provides a light source control method and system based on data analysis, and the method comprises the steps: carrying out the multi-channel spectral characteristic collection of an LED light source through a spectrum analyzer array and an optical fiber probe, obtaining a spectral response curve and an interference image, carrying out the multi-scale analysis processing, and generating a multi-dimensional data tensor; performing interference mode and defect signal decoupling analysis on the multi-dimensional data tensor to obtain an interference mode feature and an interference-defect discrimination result; performing spatial distribution analysis on the interference sensitivity of the wafer surface by adopting preset kernel density estimation and a graph neural network according to the interference mode characteristics and the interference-defect judgment result, and generating an interference sensitivity distribution matrix; according to the interference sensitivity distribution matrix, the current waveform and light beam phase distribution of the LED light source are accurately modulated, intelligent control over the LED light source is achieved, the influence of multi-mode interference on semiconductor detection is effectively eliminated, and the accuracy of nanoscale defect detection is improved.

The invention discloses an interference fringe period determination method and apparatus, and an electronic device. The method comprises the steps of obtaining an interference image obtained by performing transverse scanning on a standard sample piece by a transverse scanning interference measurement system; gradient energy of the interference image in different directions is calculated, and the direction where the maximum gradient energy is located is determined as the interference fringe direction of the transverse scanning interference measurement system; performing region division on the interference image along a direction perpendicular to the interference fringes to obtain a plurality of sub-regions; determining a spectrum distribution diagram of the interference image according to the gray data of the plurality of sub-regions; acquiring a peak position of the spectrum distribution diagram, and determining an interference fringe period of the transverse scanning interference measurement system according to the peak position; the direction of the maximum gradient energy value is determined as the interference fringe direction of the transverse scanning interference measurement system, so that the reliability of region division of the interference image is ensured; the interference fringe period is determined according to the peak value position, so that the interference fringe period is automatically, accurately and quickly determined.

The invention discloses a coherent interference-based coaxial transceiving line scanning three-dimensional profile measurement system and method. The system comprises a low-coherence light source; the beam splitter is used for collimating light emitted by the low-coherence light source and then dividing the light into two paths; the measuring light is focused into a linear light beam through the cylindrical lens and is emitted to the surface of the to-be-measured sample, and the measuring light reflected by the surface of the to-be-measured sample returns to the beam splitter along the original path; the reference light is emitted to a reference mirror with a certain inclination angle and is reflected back to the beam splitter through the reference mirror; the measuring light reflected by the surface of the sample to be measured and the reference light reflected by the reference mirror are subjected to beam combination interference through a beam splitter and are imaged to enter a camera for interference pattern acquisition; the signal processing system is used for processing the interference patterns to obtain height distribution of the to-be-measured sample on a line field, and three-dimensional profile measurement of the to-be-measured sample is realized by transversely moving or transversely scanning the to-be-measured sample and overlapping the interference patterns measured at different positions. Therefore, high-efficiency measurement can be realized while the micro-nano precision is kept.

The invention discloses a random two-step inclined phase shift wavefront detection method based on deep learning, and solves the problems of harsh requirements on a measurement environment and large measurement errors in the prior art. The method comprises the following steps: carrying out analogue simulation to obtain two interferogram databases; establishing a neural network model, and dividing the interferogram database into a training set and a test set; inputting the training set into the neural network model for training to obtain a trained neural network model; inputting the test set into the trained neural network model for testing, and outputting a wavefront phase calculation value; calculating an error between the wavefront phase calculation value and the wavefront phase simulation value until a new neural network model is obtained; and obtaining multiple frames of interference fringe patterns of the to-be-detected optical element, inputting the interference fringe patterns into the new neural network model, and outputting a real wavefront phase of the to-be-detected optical element, thereby realizing random two-step inclined phase shift wavefront detection based on deep learning. The method is simple in operation and high in measurement precision.

The invention provides a memory allocation method and device, electronic equipment, a storage medium and a chip. The method comprises the following steps: extracting tensor data of each tensor in a kernel program; constructing a control flow diagram according to the kernel program; allocating first address information in a static random access memory for each piece of tensor data; determining an active path of each tensor according to the control flow graph, the active path comprising a first node involving the tensor; an interferogram of the tensors is constructed according to the active path, each second node in the interferogram represents a different tensor, and edges between the second nodes represent an interference relation between the tensors; the first address information of each piece of tensor data is adjusted according to the interferogram, the second address information of each piece of tensor data is obtained, and in different steps of the kernel program, if no interference relation exists between the two tensors, the storage areas of the two pieces of tensor data are reused. According to the invention, the memory reuse rate can be improved.

A method for determination of a parameter of interest in a manufacturing process. For a measurement structure including a first grating with a first pitch and a second grating with a second pitch, a sensitivity of a Moiré interference pattern component is determined. The sensitivity is determined with respect to the parameter of interest in the manufacturing process. The measurement structure is evaluated, based on the sensitivity, for use in measuring the parameter of interest in the manufacturing process. A method of measuring the parameter of interest based on the measurement structure is described. A method of generating parameters of the measurement structure based on evaluation of the measurement structure for measurement of the parameter of interest is also described.

The embodiment of the invention provides a light field space coherence length measurement method and system, and relates to the technical field of optical measurement. The method may first obtain a training set and train a measurement model using the training set. And obtaining an interference fringe image through numerical simulation generation or experimental acquisition, and inputting the interference fringe image as an input image into the measurement model so as to output a spatial coherence length prediction value of the input image through the measurement model. Wherein the measurement model is a convolutional neural network model for performing feature extraction by adopting a multi-layer cascaded convolutional pooling structure, and the features can be mapped into a spatial coherence length predicted value through a full connection layer. According to the method, one-step measurement from the original interference image to the coherence length can be realized, a tedious fringe contrast calculation process is avoided, the influence of experimental noise on measurement precision is reduced through depth feature extraction, the whole prediction process can be completed within millisecond-level time, and the measurement efficiency of the light source space coherence length is improved.

The invention discloses a network optimization InSAR large gradient deformation phase unwrapping method based on edge detection, and the method specifically comprises the steps: 1, carrying out the differential interference processing of an original image, and obtaining a differential interference pattern; 2, Gaussian filtering is carried out on the differential interferogram, and the gradient magnitude and the gradient direction of each pixel in the image after Gaussian filtering are calculated; a gradient magnitude image is obtained; step 3, performing non-maximum suppression on the gradient magnitude image, and completing preliminary extraction of edges; 4, setting double thresholds to further screen edge points, and finally completing edge extraction; 5, generating a coherence graph based on the differential interferogram, extracting coherence points, and generating an unwrapping network based on the extracted coherence points and the edges extracted in the step 4; and step 6, phase unwrapping is carried out based on the unwrapping network. The method has certain universality for monitoring application scenes with large-gradient characteristic deformation, such as landslide, mining and the like, and can provide important technical support for geological disaster prevention and control.

The invention relates to the technical field of spectral analysis, in particular to a passive Fourier infrared spectrometer-based temperature drift correction method, which comprises the following steps of: acquiring original interferogram data and temperature sensor data of a passive Fourier infrared spectrometer; and screening features to construct a training sample set, and training a convolutional neural network to obtain a spectral temperature drift correction model of a specific environment temperature. Taking a specific model as a benchmark, inputting adjacent temperature combinations into a benchmark network to calculate a loss value, judging whether the loss value is applicable or not, and obtaining a spectrum temperature drift correction model in a wider temperature range. And inputting temperature and spectrum data in real time, and outputting the corrected light intensity-wavelength to realize temperature drift correction. Through the prediction network fusion strategy and the dynamic time warping algorithm, the calculation amount and the sample data amount of model training are reduced, the adaptability and generalization ability of the model to data under different temperature conditions are improved, and accurate temperature drift correction is achieved.

The invention discloses a laser shearing speckle interference phase unwrapping method based on deep learning. According to the method, eight speckle patterns before and after deformation are used as input, multi-scale feature extraction is carried out through layer-by-layer convolution and down-sampling of an encoder, features are sent to an attention fusion module, response weights of channels and spatial positions are adaptively adjusted through channel attention and position attention, then the response weights are input into a decoder, and jump connection with an encoding end is combined, so that multi-scale feature fusion is realized. And step-by-step reconstruction of multi-scale features is realized, and a phase diagram is output. Performing comprehensive constraint on prediction and reference phases in the aspects of numerical deviation, structural consistency and gradient smoothness and updating network parameters by adopting composite loss formed by mean square error, mean absolute error, structural similarity, gradient loss and out-of-plane displacement calculation items; therefore, an unwrapping result which is globally continuous and has clear and stable local details is obtained, and a feasible way is provided for intelligent processing and automatic analysis of the laser shearing speckle interference image.

The invention relates to an automatic correction method and device for an aspheric mirror interference measurement installation and adjustment error, which can realize automatic, accurate and rapid automatic correction of the installation and adjustment error. The method comprises the following steps: (1) building a virtual interferometer; (2) making a data set; (3) neural network construction and training: constructing a classification neural network, inputting an interference pattern and outputting an installation and adjustment error category, and performing network training by using the data set obtained in the step (2), so that the neural network has the installation and adjustment error detection capability; (4) assembly and adjustment error prediction: obtaining an interferogram under the current assembly and adjustment in an actual interference light path, inputting the interferogram into the trained neural network, and completing the prediction of an assembly and adjustment error category; (5) correcting the installation and adjustment error: according to an installation and adjustment error prediction result obtained in the step (4), controlling a corresponding mechanical part to correct the installation and adjustment error; (6) if the neural network detects that the installation and adjustment error does not meet the requirement, executing the step (4); otherwise, executing the step (7); and (7) ending.

The invention discloses an iron tower PS pixel point simulation and differential interference deformation monitoring method and system based on three-dimensional point cloud data, and the method comprises the steps: obtaining the point cloud data of an iron tower body structure under a radar coordinate system through a radar distance Doppler positioning method through the three-dimensional point cloud data of the iron tower body structure; identifying pixels containing the iron tower point cloud data in the SAR image as iron tower PS pixel points; constructing a radar scattering weight model based on elevation information of the iron tower point cloud data, and obtaining an iron tower PS pixel point scattering intensity value through weighted summation in combination with a weight adjustment parameter; constructing a complex value of the point cloud data of the iron tower according to the interferogram parameters and a radar scattering weight model, and obtaining a simulation interference elevation phase of the PS pixel point of the iron tower by using complex field summation; differential interferometric phases of iron tower PS pixel points in the interferogram are obtained through difference; and performing statistical analysis on the differential phase map of the iron tower PS pixel point set region in the time sequence interferogram to obtain a deformation result.

Techniques for reducing interference graph generation time may include obtaining a data flow graph representing a computational flow. For each memory object in the data flow graph, a memory object live interval can be added to a vector of intervals. The memory object live interval indicates a last-use of the memory object and a first-definition of the memory object. The vector of intervals can be converted into a binary tree of interval nodes. For each interval node in the binary tree, an earliest-first-definition value is determined for the sub-tree rooted at the interval node, and is associated with the interval node. The binary tree can be queried for interferences of a memory object, and memory allocation can be performed for the computational flow based on the interferences.

The invention provides a wide-area landslide identification method and system based on phase gradient stacking and deep learning, and the method comprises the steps: obtaining an SAR image set covering a research region at different time periods, and obtaining a differential interferogram through a D-InSAR method; an IPGS method is adopted to obtain a gradient stacking graph of the differential interferogram, and the gradient stacking graph is cut to obtain a cut picture; performing landslide sample labeling on the cut picture through the auxiliary data and the optical image to obtain a labeled sample; training the improved YOLOv7 model by adopting a labeling sample to obtain a landslide target recognition model; and inputting the to-be-identified image of the research area into the landslide target identification model, and outputting a landslide identification result of the research area. According to the invention, the advantage of rapidly and accurately acquiring the deformation gradient signal by the improved phase gradient stacking method is combined with the deep learning model to overcome the defects in the prior art, so that the efficient and accurate wide-area potential landslide automatic identification method is provided.

The invention discloses a step height and flatness measuring method based on a laser interference method, which comprises the following steps of: determining that a calibrated step is put into a laser interference measuring device, moving a reflector distance to obtain an interference image, and processing the interference image to obtain a step surface phase distribution diagram. Determining the position of a calculation point according to a step boundary, calculating a step measurement result, optimizing device parameters according to the step measurement result and step calibration value deviation to obtain optimal device parameters, and constructing a step device parameter library; and according to the step parameters of the to-be-measured step, matching the optimal device parameters in the step device parameter library to obtain reference device parameters, adjusting the laser interference measurement device, and measuring the to-be-measured step by using the adjusted laser interference measurement device to obtain the step height and the step flatness. The method can timely and accurately measure the height and flatness of the step, and has important significance for promoting the development of a high-precision measurement technology in the field of micro-nano manufacturing.

The invention discloses an object tiny deformation measurement method and system based on interference fringe analysis, and belongs to the technical field of optical measurement. The method comprises the following steps: acquiring an interference fringe image and probe position and direction data; carrying out phase demodulation and sensitivity vector calculation on the surface of a measured object; obtaining the local phase number of each probe according to the interference image; calculating sensitivity vector data through a geometrical relationship; obtaining global phase and sensitivity data, obtaining initial full-field phase data through a weighted fusion method based on a geometrical relationship between a sensitivity vector and a deformation direction, calculating a consistency error, judging whether the consistency error exceeds a threshold value or not, if so, updating a deformation direction estimation value, and executing fusion processing again; and solving and obtaining three-dimensional deformation field data through a linear relation until the error meets the requirement or reaches the maximum number of iterations. According to the invention, the reliability and early warning capability of structural safety assessment in long-term monitoring can be improved.

The measurement of overlay using a microscope-based sensor is described. This provides, among several advantages, the ability to measure alignment and overlay in a single system. The signal from the sensor is an intensity-modulated fringe pattern (e.g., an interference pattern). The sensor is configured to generate an intensity-modulated fringe pattern based on diffracted radiation received from a first metronome mark on a first layer of the pattern substrate and a second metronome mark on a second layer of the pattern substrate. The intensity-modulated fringe pattern is filtered for the expected fringe period. The overlay value is determined using the amplitude, phase, and / or other parameters of the fringe in the filtered fringe pattern.

One embodiment provides a method comprising obtaining a Fourier transform of an interference pattern, obtaining a central band component from the Fourier transform of the interference pattern, obtaining a sideband component from the Fourier transform of the interference pattern, obtaining a central band inverse Fourier transform image by performing an inverse Fourier transform of the central band component, obtaining a sideband inverse Fourier transform image by performing an inverse Fourier transform of the sideband component, and determining a fringe height reduction factor, the fringe height reduction factor corresponding to an interference fringe height reduction due to motion blur in the interference pattern, based on a comparison between the sideband inverse Fourier transform image and the central band inverse Fourier transform image.

The application discloses a bidirectional incremental intelligent merging method in distributed storage fault recovery, and relates to the field of distributed data management. The method comprises the following steps: receiving local and remote incremental operation sequences, mapping the operation sequences to a multi-dimensional space-time coordinate system, and generating a multi-dimensional space-time operation point set; based on the multi-dimensional space-time operation point set, constructing a geometric causal cone for each operation point, and constructing a causal interference spectrum by analyzing the interference between the geometric causal cones; according to the causal interference spectrum and the multi-dimensional space-time operation point set, searching for a space-time folding path to avoid or minimize causal conflicts, and obtaining an optimal space-time folding scheme; projecting the high-dimensional path in the optimal space-time folding scheme into a one-dimensional execution sequence, and executing to achieve a consistent data state. By improving the conflict analysis and solution to a high-dimensional geometric space, the application realizes deep semantic understanding and flexible avoidance of operation conflicts, and improves the accuracy and automation level of data merging.

The present application relates to the field of optical interferometry, and aims at the phase distortion problem in off-axis interferometric quantitative phase imaging, and provides an interferometric microscopic phase distortion elimination method based on PACUNeXt3+ network. The method comprises the following steps: 1. using Zernike polynomials and test target pictures to simulate and generate a data set; 2. establishing and training a PACUNeXt3+ neural network model; 3. inputting the interference graph I or of the sample to be measured into the trained neural network, and outputting the background interference graph I' r corresponding to the sample without sample information; 4. using the two interference graphs I or and I' r to reconstruct the sample phase distribution φ o (x, y) without phase distortion. The present application has high precision and speed, can eliminate the secondary or high-order phase distortion in interferometric quantitative phase imaging, and has great application prospect in the field of phase imaging.

The utility model discloses a system for measuring the half-wave voltage of a phase modulator. The system comprises a laser generation module, an optical coupler, a first phase modulator, a second phase modulator, a first Faraday mirror, a second Faraday mirror, a signal generator, a photoelectric detector and an oscilloscope. The utility model discloses a system for measuring the half-wave voltage of a phase modulator, which is characterized in that a high-frequency periodic square wave electric signal is loaded to a second phase modulator through a signal generator, and the phase drift amount caused by unstable factors such as a light source, an optical device and an external environment can be regarded as perturbation in a small time period; the half-wave voltage of the second phase modulator is obtained by accurately calculating the variable quantity of the number of the peak (valley) values of the sine (cosine) waveform, so that the judgment on the light intensity extreme point of an interference pattern is avoided, the measurement precision is improved, and the key core application in the fields of quantum communication and quantum calculation is realized.

The present invention discloses a centralized intelligent spectrum management method for an unmanned system cluster network. This method constructs a weighted interference graph model and, based on node measurement information and communication performance feedback, employs reinforcement learning methods such as heuristic algorithms and probabilistic rules. This method can autonomously optimize and adjust the network's operating channels, reducing frequency conflicts within the system and the adverse effects of external interference. This method uses the communication performance actually perceived by communication nodes as feedback to guide spectrum decisions, adapts to complex and dynamic spectrum and wireless channel environments, and supports large-scale unmanned system network communication spectrum management. This method allocates channels for service data networks based on reinforcement learning algorithms such as probabilistic rules, and for wireless access networks based on reinforcement learning algorithms such as heuristic algorithms. This method has learning capabilities and low algorithmic complexity.

A system includes an illumination system, an optical system, a detector system, and a processor. The illumination system directs a first illumination beam and a second illumination beam toward the target structure, and directs a first reference beam toward the detector system. The optical system directs the first scattered beam and the second scattered beam from the target structure to the detector system. A detector system captures the interference pattern and outputs a measurement signal. The first scattered beam, the second scattered beam, and the first reference beam generate an interference pattern. The processor analyzes the measurement signal to determine a characteristic of the target structure.

The invention relates to an InSAR interferogram quality discrimination method based on deep learning, and the method comprises the steps: carrying out the preliminary decomposition of an InSAR interferogram through a multi-scale wavelet transform method, and carrying out the feature extraction, and obtaining a nonlinear feature vector; carrying out deep learning processing on the nonlinear feature vector by adopting a convolutional neural network to obtain interference weight distribution under multi-factor coupling; based on the interference weight distribution, obtaining superposition influence parameters of topographic relief and atmospheric interference, and determining a quantization level of deviation increase; and performing pattern recognition on the quantization level with the increased deviation through a support vector machine classifier to obtain a stable and corresponding quality judgment result. According to the method, the quality judgment accuracy, stability and robustness under the complex terrain and the atmospheric environment are remarkably improved, the reliability of geological disaster early warning is enhanced, and the method has important practical value.

The invention provides a double-state interference morphology measurement method and system for a discontinuous precision surface, and belongs to the technical field of optical precision measurement. Based on a Fizeau interferometer, interference images in a focusing state and an out-of-focus state are respectively acquired, two-dimensional structure information and height information of a sample with a discontinuous precise surface are respectively extracted, and two-state data fusion is carried out, so that three-dimensional morphology reconstruction is realized. The method breaks through the limitation of the optical diffraction limit on the resolution, avoids the failure problem of a traditional phase unwrapping algorithm at a discontinuous boundary, remarkably enhances the environmental interference resistance, improves the calculation efficiency, is suitable for high-precision surface measurement of a vacuum chuck and the like with a large number of discontinuous characteristics, and has a wide application prospect. And a reliable solution is provided for high-end manufacturing quality control.

The invention provides an interferogram phase demodulation method, device and system, and relates to the technical field of optical detection, and the method comprises the steps: obtaining first phase shift amounts corresponding to at least three interference images, and the phase shift between the adjacent interference images is an inclined plane; determining reference phases corresponding to the at least three interference images according to the first phase shift amount; determining second phase shift amounts respectively corresponding to the at least three interference images according to the reference phase; the difference value between the first phase shift amount and the second phase shift amount is calculated, and whether the difference value between the first phase shift amount and the second phase shift amount is smaller than a preset threshold value or not is judged; if not, determining the reference phase again according to the second phase shift amount; and if yes, outputting the reference phase as the target phase, so that the demodulation precision can be improved by assuming the phase shift between the adjacent interference images as the inclined plane and solving the phase shift amount and the target phase through the iteration method, and the method can be applied to a vibration test scene.