267 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 discloses a real-time automatic focusing method based on interference fringe phase extraction, and the method specifically comprises the steps: importing an initial interference image, setting a focusing state position as an initial phase shift value, and carrying out the zero setting of the initial phase shift value; when the detection system runs, keeping interference image video stream input, and calculating a phase shift value and a phase value in real time through an improved iterative algorithm; and the calculation result is converted into a defocusing value, and the PZT controller is controlled to complete focusing. According to the automatic focusing method provided by the invention, the defocusing amount is obtained according to the phase movement of the interference fringes, a high-precision displacement measurement method is introduced into the automatic focusing technology, and compared with a traditional microscopic focusing method, the focusing precision is improved in a breakthrough manner, and the method has very high innovativeness; based on an interference image method, phase information closest to reality is extracted through iterative operation, real-time processing is continuously carried out through video streams of interference images, the operation speed is high, and real-time focusing can be completed in the scanning process of a detection system.

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 provides a minimum cost flow phase unwrapping method and device based on phase gradient rate assistance. The method comprises the steps that an SAR data set and DEM data of a to-be-monitored area are acquired; based on this, a differential interferogram and a coherence map are generated; respectively calculating a shortest time baseline phase gradient rate and a full time baseline phase gradient rate based on the differential interferogram; constructing a Delaunay triangulation network based on the differential interferogram and the coherence graph, and determining a rotation-free constraint and a design matrix; on the basis of the coherence graph, the shortest time baseline phase gradient rate and the full time baseline phase gradient rate, performing weight determination on each triangular arc segment in the triangulation network to obtain a weight corresponding to each triangular arc segment; and based on the non-rotation constraint, designing a matrix and weights corresponding to the triangular arc segments to solve the integer ambiguity, and completing a phase unwrapping task based on the integer ambiguity. According to the embodiment, the absolute phase of the large-gradient deformation area is obtained, so that high-precision deformation monitoring of the earth surface is realized.

The invention discloses an MT-InSAR deformation monitoring method in combination with reservoir bank landslide deformation spatial-temporal characteristics. The method comprises the following steps: S1, selecting SAR data; step S2, constructing small baseline set InSAR data, including SAR image registration, interferogram combination, differential interference processing, phase filtering, coherence estimation and interferogram optimization combination; s3, based on the dual-polarized SAR intensity image, performing earth surface classification in combination with random forest classification, gradient and direction constraint and a deep neural network; then adaptive homogeneous filtering is carried out on the interferometric phase based on a secondary classification result; s4, combining a plurality of Sigmoid functions and a linear creep and periodic function to obtain an adaptive deformation model of the reservoir bank landslide; s5, performing deformation parameter nonlinear search by using a genetic algorithm to determine an optimal parameter set; on the basis of low-frequency deformation, atmospheric delay and decoherence noise are separated through low-pass filtering of a space domain and high-pass filtering of a time domain, and finally a deformation time sequence and an average deformation rate are recovered.

A scanning imaging method includes splitting an optical beam into a reference beam and a scanning beam, generating an interference map, and processing the interference map to produce a reconstructed image of a sample. Generating the interference map includes, for each of a plurality of sections of the sample, generating a respective interference-map element of the interference map by: (i) illuminating the section with the scanning beam to generate a plurality of scattered beams, each scattered beam corresponding to a respective spatial frequency of the scanning beam, (ii) attenuating the reference beam, (iii) generating a plurality of interference signals at least in part by interferometrically combining the plurality of scattered beams and the attenuated reference beam while modulating a phase difference between the reference beam and the plurality of scattered beams, and (iv) detecting the plurality of interference signals to yield the respective interference-map element.

The invention belongs to the technical field of terrain remote sensing monitoring, and particularly relates to a surface deformation monitoring method based on an intermittent stacking time sequence InSAR (Intermittent Synthetic Aperture Radar), which comprises the following steps of: firstly, acquiring a plurality of SAR images, high-precision terrain data and orbit data of a target area in a lifting orbit within a period of time; performing geographical registration, interference combination selection, space-time filtering and phase unwrapping on the SAR image to generate a time sequence interferogram; calculating an annual average deformation rate of each pixel point in the interferogram, and generating an annual average deformation rate result of the target area; and finally, generating a suspicious landslide polygon according to an annual average deformation rate result, and performing visual interpretation and field investigation to obtain a real area of the landslide polygon. According to the method, result missing caused by the intermittent coherence characteristic of the pixel points can be avoided, the pixel selection has higher latitude, higher coverage can be achieved, the requirement for computing power is remarkably reduced, and a deformation rate result in a low-coherence environment can be rapidly obtained.

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 relates to an interferometer, in particular to a method for monitoring imaging position deviation of a Doppler differential interferometer, which is used for overcoming the defect of image surface drift of the Doppler differential interferometer caused by a thermal drift effect. According to the Doppler differential interferometer imaging position offset monitoring method, interference pattern signals are extracted and expanded, a groove distribution model D (x) and a scale signal distribution model I (x) are constructed, model parameters are optimized through multiple calibration interference patterns, and simulation scale signals are generated; and in actual measurement, the first pulse position a0 is optimized by minimizing an error between a simulation scale signal and an actually measured scale signal, so that accurate monitoring of the imaging position offset of the Doppler differential interferometer is realized.

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 present invention relates to a method for the computer-aided generation of a data-driven model for the computer-aided processing of digital SAR data from a repeat-pass SAR system configured to determine surface deformations. The digital SAR data consist of a plurality of interferograms (Γ), each of which is obtained from a first SLC acquisition (s1) of the SAR system at a first time point and a second SLC acquisition (s2) of the SAR system at a second time point later than the first, and a Doppler center frequency (fdc), an interferometric phase (φ̂), and a coherence (P), all of which are obtained from a respective pair of first and second SLC acquisitions (s1, s2). The data-driven model is configured to generate a zero-Doppler phase (φzd), which represents the phase contribution associated with the deformation causing a zero-Doppler LOS change.to determine an along-track phase (φat), representing the phase contribution associated with deformation in an along-track direction, and an estimate of an associated along-track surface motion (uat). The generation of the data-driven model comprises the following steps: - Generating a synthetic training dataset (TDS) comprising a variety of interferograms (Γ), each formed by a pair of a first and a second SLC capture (s1, s2), by inducing a model-based and external 3D deformation into a given first SLC capture (s1) to obtain the associated second SLC capture (s2), which relates to a time point after a deformation event, and by applying a reverse approach,which is specified by a given coherence (ρ) and a noise-free interferometric phase (φtheo) after the deformation event; - Training a neural network (NN) with the training dataset (TDS), wherein the neural network (NN) comprises an input for inputting the plurality of interferograms (Γ) and an output for outputting the along-track phase (Φat), the zero-Doppler phase (φzd) and the associated along-track surface motion (uat) for the plurality of interferograms (Γ), wherein the trained neural network (NN) represents the generated data-driven model.

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 present invention discloses a holographic grating self-calibration method, which relates to the field of optical technology, including collecting interference image data and performing preprocessing, extracting phase data of the interference image data after preprocessing; generating a phase map based on the phase data to perform holographic grating self-calibration, and evaluating and adjusting the result of the holographic grating self-calibration; constructing a visual interface to display the holographic grating self-calibration in real time, implementing secure encryption on the phase data, and storing, collecting and analyzing the generated data. The present invention collects interference image data and performs preprocessing, extracts phase data of the interference image data after preprocessing, and generates a phase map based on the phase data to perform holographic grating self-calibration, which greatly simplifies the calibration process, improves the convenience of operation and the extraction accuracy of phase data, and significantly improves the calibration accuracy and stability of the holographic grating.

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.

The invention provides a compensation device and system of an LCoS chip, and relates to the technical field of display. The device comprises a wavefront measuring module, a processing module and a micro-control module, the wavefront measurement module is used for measuring an interference pattern generated by the LCoS chip to be measured and measuring interference fringes generated when the LCoS chip loads different test gray-scale graphs; the processing module is used for processing the interference pattern to obtain initial surface type data of the LCoS chip, and processing the interference fringes to generate a gray scale-phase response curve of the LCoS chip; and the micro-control module is used for obtaining a voltage-phase compensation curve of the LCoS chip according to the initial surface type data and the gray scale-phase response curve, so that the phase of each pixel point on the LCoS chip in the compensated LCoS device is compensated based on the voltage-phase compensation curve, the accuracy of a compensation result is ensured, and the phase modulation uniformity of the LCoS chip is 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 winding interference phase-based creep type landslide hidden danger intelligent identification method and system, and the method comprises the steps: firstly obtaining multi-scene time continuous winding interference pattern data, and carrying out the up-sampling of the data to a fixed input size of 128 * 128 through bilinear interpolation; then, sine and cosine functions are utilized to map the wrapping interferogram, a sine phase and a cosine phase are generated, and phase information is further enriched. And through a set mapping function, converting the phase data into an RGB color space so as to realize multi-dimensional expression of the data. In the feature extraction and fusion stage, a multi-branch network architecture sharing weight is adopted, independent feature extraction is performed on each RGB channel, and the same-layer features are integrated through a feature fusion mechanism to extract high-dimensional semantic information. And then, feature information is recovered step by step through a decoder, classification mapping is completed, and finally a prediction result of the single-scene interferogram is obtained.

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 belongs to the technical field of channel allocation, and particularly relates to a full-duplex D2D (Device-to-Device) communication channel allocation algorithm based on novel graph coloring, which comprises the following steps of: constructing a bidirectional interference graph containing all potential interference links; expressing a channel allocation problem as an interference minimization graph coloring problem; distributing N independent communication channels for all cellular user equipment; a greedy graph coloring algorithm is adopted, and single colors are distributed to the D2D vertexes which are not colored one by one; if the number of D2D vertexes in a certain color set is increased or decreased, the interference level is evaluated again; and traversing each color, allocating a single color to the remaining D2D vertexes, terminating the algorithm after all D2D vertexes are traversed, and generating a conflict-free channel allocation scheme. According to the invention, the channel allocation can be optimized, and the interference in the system can be effectively managed and reduced, so that the access rate of the user and the overall spectrum efficiency are improved.

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 belongs to the field of synthetic aperture radar interferometry, and discloses a macro map No.1 distributed wheel formation InSAR satellite elevation inversion method, which can effectively improve the elevation inversion precision of a digital elevation model (DEM) of a complex terrain. The method comprises the following steps: firstly, establishing an equivalent phase center, and simplifying an original receiving and transmitting bistatic imaging geometric model of a cosatellite into a self-transmitting and self-receiving single-base multi-pass InSAR geometric model; then, registering the main and auxiliary images based on a single-base multi-pass InSAR geometric model, generating three main and auxiliary image pair interferograms with different baseline lengths, and performing flat-ground phase removal processing by combining parameters such as orbit information and the like; performing multi-baseline phase unwrapping on the interference pattern after the flat ground phase is removed, and recovering an absolute terrain phase; and finally, converting the unwrapped phase into a target scene elevation, and generating a target area DEM after geocoding processing.

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.