687 results about "Displacement field" patented technology

The invention discloses a loess tunnel surrounding rock deformation monitoring method, and relates to the technical field of civil engineering and geological engineering, and the method comprises the steps: integrating a spiral winding type optical fiber sensor, a double-cavity humidity compensation vibrating wire sensor and a microseismic array, and capturing surrounding rock strain, vibration and geological activities; the vibrating wire sensor suppresses humidity interference through a silicone oil damping medium and self-adaptive excitation frequency, and the optical fiber sensor is fixed through a pre-embedded silica gel sleeve to adapt to surrounding rock deformation; edge computing nodes are deployed on the inner wall of the tunnel, an FPGA chip and a lightweight GRU model are integrated, optical fiber strain, a micro-seismic energy spectrum and laser point cloud displacement field data are fused in real time, the strain gradient is analyzed, and a crack propagation probability cloud picture is generated; redundant optical fiber link switching is combined with a six-degree-of-freedom mechanical arm to realize breakpoint self-repairing, and a self-cleaning air curtain is integrated to inhibit dust adhesion; and constructing a geological parameter library based on a BIM-GIS fusion platform, driving a finite element-discrete element coupling model to dynamically update boundary conditions, and predicting surrounding rock deformation and collapse risks.

The invention discloses a landslide hidden danger point displacement monitoring system applying multi-source data fusion, and the system comprises a multi-source sensing unit which comprises a spaceborne radar sensing module which is used for obtaining large-range ground surface deformation data through a synthetic aperture radar; the earth surface deformation sensing module is used for monitoring earth surface displacement in real time through a double-frequency GNSS receiver and an optical fiber grating sensor; the underground stress sensing module is used for acquiring the stress change of an underground rock-soil body through a micro-seismic monitoring array and a distributed optical fiber sensor; and the edge calculation unit comprises a dynamic weight fusion module which is used for dynamically distributing the weight of the multi-source sensor according to the geological state and generating high-precision fusion displacement field data. According to the invention, through multi-source data fusion, model parameter real-time updating and a closed-loop optimization mechanism, bottlenecks of a traditional monitoring system in aspects of data quality, model precision and response efficiency are broken through, and data support is provided for accurate early warning and intelligent emergency of landslide disasters.

The invention discloses a bridge health prediction device based on a Beidou system and a monitoring analysis method, and relates to the field of bridge health monitoring. The method comprises the following steps: S1, deploying monitoring points at key structure nodes of a bridge, and collecting three-dimensional position information and multi-source data by using a Beidou system and a multi-type sensor; s2, constructing a time-varying displacement field and a continuous deformation tensor, fusing stress and strain to construct a structural fatigue factor, and introducing environmental disturbance to realize dynamic correction of the fatigue factor; s3, a health state function is constructed based on the stress response, the displacement gradient and the deformation tensor, a damage mapping function is constructed in combination with accumulated deformation and a historical threshold value, and a damage thermodynamic diagram is generated; and S4, finally fusing fatigue, health and damage indexes to evaluate bridge risks, and performing dynamic early warning. Through fusion of mechanical tensor features and environmental disturbance modeling, a multi-index linkage fatigue and damage assessment mechanism is constructed, and the precision, robustness and global perception ability of bridge health monitoring are improved.

The invention provides a bridge structure monitoring method and device based on a microwave deformation radar, and relates to the technical field of bridge structure monitoring, and the method comprises the steps: obtaining the multi-point three-dimensional displacement data of a bridge structure through the microwave deformation radar, carrying out the thermal expansion pseudo displacement compensation and multi-point space smoothing through combining with temperature information, and obtaining a displacement field after environment correction; secondly, extracting a vertical component and separating the vertical component into a static deformation component and a dynamic vibration component by adopting variational mode decomposition; further analyzing and identifying a decoupling region through a time window coherence coefficient, performing recursive quantitative analysis, bispectrum analysis and energy distribution entropy calculation on a dynamic signal of the region, and constructing a high-order damage sensitive feature set; and finally, the dynamic characteristics and the static curvature change are fused to form a comprehensive degradation degree index, the deviation degree is judged according to working condition classification and the mahalanobis distance, and multi-dimensional and cross-working-condition degradation identification and risk early warning of the bridge structure are achieved.

The invention relates to the field of thin-wall structure displacement field monitoring, and discloses a coupled functional unilateral inverse finite element cylindrical thin-wall structure displacement field monitoring method, which comprises the following steps of: performing discretization by adopting a four-node inverse shell unit, and arranging a sensor at a geometric center position of each unit; based on the basic mathematical model of the inverse finite element, establishing an overall pseudo-stiffness matrix and an overall pseudo-force matrix; and calculating to obtain a displacement field of the structure by solving a linear equation set formed by the overall pseudo-stiffness matrix, the overall pseudo-force matrix and the global displacement vector. According to the method, the structural displacement field is calculated by using the strain information measured at one side, and the global deformation is accurately and reversely deduced under the condition that only the local strain data is obtained by combining the advantages of finite element analysis and inverse problem solution. By optimizing local strain data solution, high cost and low efficiency of global strain measurement in a traditional method are avoided, and the method has remarkable practical advantages.

A solid structure deformation and damage analysis method based on a quasi-bond finite element method comprises: carrying out geometric modeling on a target structure body, and distribution and subdivision to generate a grid of a traditional finite element method; dividing the target structure body into a finite element region and a quasi-bond region, and calculating a finite element region system stiffness matrix and a quasi-bond region system stiffness matrix to obtain an overall stiffness matrix of the target structure body; setting a boundary condition for the target structure body, applying an external load, and calculating a system force matrix under current load and boundary state; and judging a quasi-bond breakage condition according to a node trial displacement, then calculating a node displacement of the structure body and an equivalent damage parameter at each node, and outputting cloud charts of a displacement field and an equivalent damage field.

The invention relates to a thermal coupling over-reduced order prediction method and system for a multi-component assembled nonlinear system. The method comprises the following steps: collecting multi-scale physical field data; constructing an intrinsic orthogonal decomposition basis function space of a temperature field and a stress field, and establishing a double-field coupling constraint equation; constructing contact thermal resistance parameterized proxy models of a cylinder contact area, a bolt area and a free deformation area by adopting a domain discrete empirical interpolation method; constructing a parametric intrinsic mode tensor network to obtain a decline model which is used for realizing real-time reconstruction of a mode basis function through acquired tensor slices in an online stage; performing dynamic inversion based on a modal basis function reconstruction result, and outputting a predicted transient displacement field, a predicted temperature gradient field and a predicted contact stress field; and obtaining real-time parameters, and calculating a residual error with a corresponding prediction result so as to dynamically update the primary function and interpolation point distribution. Compared with the prior art, the real-time prediction of the transient thermal coupling of the multi-component contact system is realized on the premise of ensuring the precision.

The invention relates to the technical field of goods shelf detection, in particular to a self-detection method and device for goods shelf settlement, and provides the following scheme: obtaining a top view image through an image sensor arranged right above the top of a goods shelf, dividing the image into a plurality of grid units, and positioning a rectangular geometric shape by utilizing Hough transform; and screening a plurality of to-be-detected areas in combination with the edge features. For an area to be measured, homographic registration and ortho-rectification are carried out based on a reference image, a displacement field is obtained by adopting sub-pixel-level dense registration, and a geometric parallax component field corresponding to imaging parameters is obtained through robust estimation. And under the hypothesis of small deformation, inverting the parallax into a pixel normal distance, and carrying out weighted aggregation on the local region to obtain a local distance measurement result. And by iteratively combining adjacent grids, determining a settlement area boundary, and finally outputting a settlement detection result. Millimeter-level settlement quantification can be realized under a single-frame image, hardware transformation is avoided, and the method is suitable for automatic detection and long-term monitoring of multi-specification goods shelves.

The invention provides a slope displacement monitoring data processing system based on an unmanned aerial vehicle laser radar, and relates to the technical field of data processing, and the system comprises the steps: carrying out the spatial interpolation processing of a topographic feature data set, and constructing a digital topographic surface model; the displacement field calculation module is used for performing iterative optimization based on a digital terrain surface model through spatial similarity analysis and fusion with a gradient descent algorithm, calculating a slope surface displacement vector field, identifying a potential sliding surface and a deformation abnormal region, and generating a displacement field calculation result; and the evaluation module is used for inputting a displacement field calculation result into a risk evaluation model and carrying out slope stability quantitative evaluation through a multi-source data fusion analysis platform. According to the invention, the practicability and operability of the monitoring result are improved.

The invention relates to the technical field of geological disaster monitoring, and discloses a landslide dynamic simulation monitoring and early warning method based on multi-source sensing fusion, and the method comprises the steps: collecting multi-source data of a landslide body through a plurality of heterogeneous sensors, and enabling the multi-source data to be in space-time alignment after preprocessing; building a multi-parameter fusion model fusing a displacement field, a mechanical field and an environment field based on the preprocessed data, enabling the multi-parameter fusion model to output a deformation rate and a stability coefficient, and dynamically adjusting the weights of the displacement field, the mechanical field and the environment field according to a landslide evolution stage; predicting a future deformation trend of the landslide mass in combination with a geological structure and historical data, comparing a stability coefficient with a dynamic safety threshold to judge a risk level, and generating early warning information; and dynamically correcting a reference weight coefficient in the model based on the deviation between the monitoring data and the model output, so that the model is adaptively optimized. The problems of single monitoring dimension and static model solidification in the prior art are solved, and accurate and adaptive monitoring and early warning of the risk state of the landslide mass are realized.

The invention discloses a simulation method and system based on multi-field coupling and dynamic grid processing, and belongs to the field of computational simulation, and the method comprises the steps: building and solving a coupling physical field model: carrying out finite element spatial discretization on a solving domain, building and solving a coupling partial differential equation set which at least comprises a temperature field, a displacement field and a stress field, and carrying out the finite element spatial discretization; obtaining numerical solutions of the physical fields in the current state; carrying out progressive unit removal based on a physical criterion; and finally, grid data structure updating and solver synchronization are executed. According to the method, the metal material can be gradually removed when the physical quantity reaches the critical condition, and the grid change is dynamically and robustly processed in the process.

The invention relates to the technical field of digital image correlation DIC measurement, in particular to a DIC displacement field dynamic correction method and system based on a lightweight convolutional network, and the method comprises the steps: 1, collecting a structure surface image sequence, and carrying out the preprocessing; 2, calculating an initial displacement field of the preprocessed image sequence through a traditional DIC algorithm, extracting key feature information, and taking the key feature information as input data for dynamic correction; 3, constructing a lightweight convolutional neural network model, transmitting the extracted input data to the lightweight convolutional neural network model for recognition, designing a mixed loss function for training, and learning a mapping relation between an initial displacement field and a real displacement field; and 4, correcting the initial displacement field through the trained lightweight convolutional neural network model, and directly outputting corrected high-precision displacement field data. By using the function fitting capability of the neural network, high-precision displacement field correction is realized under limited computing resources, and the measurement precision is improved.

The invention relates to the technical field of geotechnical engineering disaster monitoring and early warning, and discloses a stratum rainfall seepage deformation coupling numerical simulation method and system and a storage medium, and the method comprises the steps: building a three-dimensional geologic model reflecting soft and hard rock interbed characteristics; converting the on-site rainfall data into hydraulic boundary conditions in real time; calculating a rock mass shear expansion volume change rate, determining a chemical damage acceleration factor, and updating a total damage variable; reconstructing a permeation tensor representing the dominant flow channel according to the space gradient of the damage variable; solving a fluid-solid coupling equation based on the permeability tensor, obtaining pore water pressure and calculating a displacement field; building a likelihood function by using field monitoring data to perform inversion correction on model parameters; and evaluating the stability based on the corrected calculation result and outputting an early warning signal. By constructing a mechanical coupling mechanism and a dominant flow channel model, precise simulation of a landslide evolution process in a complex geological environment is realized.

The invention relates to a method for predicting soil displacement caused by double-line shield construction based on number-object fusion. Comprising the steps of establishing a finite element numerical model based on shield parameters and geological data; numerical model parameters are corrected through actual monitoring data, the double-line shield distance, the advancing speed difference and the soil displacement field evolution law are analyzed, and dynamic visual prediction of the displacement field at different construction stages is achieved. According to the method, a two-way feedback mechanism of numerical simulation and engineering actual monitoring data is constructed, a three-dimensional dynamic coupling analysis method is innovatively provided, and the problem of insufficient prediction precision caused by dynamic change of soil parameters and a double-line construction coupling effect in traditional construction is solved. Through iterative optimization of a digital model and measured data, the maximum displacement error of a soil body is further reduced, the stratum standard-exceeding deformation risk is predicted in advance, the quantitative evaluation problem of the stratum disturbance superposition effect in double-line shield construction is effectively solved, and intelligent decision support is provided for tunnel construction safety control in a complex urban environment.

The invention relates to the technical field of medical elastography and material multi-scale mechanical measurement, in particular to an elastography method and system based on an instantaneous scattering displacement field, and the method comprises the steps: driving a passive / active method scattering displacement field excitation device through a frequency domain controllable sine wave signal, and generating a scattering displacement field on a target material, an instantaneous displacement field is obtained through high spatial resolution measurement, then sliding window local interception, two-dimensional autocorrelation calculation and nonlinear fitting based on the Rayleigh / shear wave spatial autocorrelation theory are performed on the single-frame instantaneous displacement field, shear wave velocity distribution is obtained, and then the viscoelasticity modulus of a target area is inverted to achieve comprehensive characterization of the viscoelasticity of the material. According to the scheme, the single-frame scattering displacement field is excited and collected, and the elasticity inversion is performed based on the instantaneous displacement field, so that the dependence of traditional elasticity imaging on high-frame-rate equipment is broken through, the material viscoelasticity evaluation can be efficiently completed at low cost, and the application prospect in the fields of clinical diagnosis and mechanical measurement is wide.

The invention belongs to the technical field of underwater sound, and discloses an underwater target sound scattering modeling method and system based on a physical information neural network. The method comprises the following steps: establishing a loss function of a physical information neural network by establishing an underwater target acoustic-structure coupling mathematical model; a decoupling parallel dual-network architecture is established, a fluid domain neural network is used for learning and outputting a scattering sound pressure field of a fluid domain, an elastomer domain neural network is used for learning and outputting a displacement field of an elastomer domain, and the two networks realize physical coupling by sharing a sampling point and a corresponding boundary condition loss function at an acoustic-structure coupling boundary; and generating training data, performing iterative training on the decoupling parallel physical information neural network, and calculating a scattering sound pressure field outside the target and a displacement field inside the elastomer by using a trained model. According to the method, the physical information neural network is innovatively applied to the underwater target sound scattering field, and a new thought and a new way are provided for accurately forecasting the underwater target sound scattering field.

The invention provides a rotary table bearing-torque motor thermal vibration coupling modeling method, and relates to the technical field of machine tool reliability analysis. Comprising the steps of calculating bearing restoring force, torque and elastic rigidity; giving a dynamic balance equation of the main shaft system; unbalanced magnetic pulling force and unbalanced mass excitation are calculated; a displacement field is solved, and load changes caused by the displacement field are transmitted to the thermal model; the contact force borne by the bearing is updated by considering the load effect related to displacement, and the friction heat generation of the bearing and the heat generation of each part of the motor are calculated; calculating thermal deformation and thermal displacement of the bearing; thermal resistance among nodes is calculated, and a temperature field equilibrium equation is given through a thermal resistance network to achieve rapid thermal-mechanical coupling analysis; the thermal equilibrium equation is solved to calculate temperature response; importing the latest temperature into the kinetic model; lubricating grease viscosity and bearing thermal deformation are updated; and repeating the steps to finally obtain the main shaft displacement field and temperature field response. The method is high in accuracy, high in universality, low in test cost and high in test efficiency.

The invention discloses a numerical simulation method and system for corrosion expansion cracking of reinforced concrete based on near-field dynamics, and relates to the technical field of numerical simulation of civil engineering materials. Discretizing a concrete calculation area into a near-field dynamic material point model to form a discrete model; applying boundary conditions to the discrete model; establishing a mapping relation between the material point damage degree and the erosion coefficient; acquiring chloride ion concentration distribution and oxygen concentration distribution of the discrete model at the current time step, and judging whether the chloride ion concentration of the surface of the steel bar reaches a blunt removal threshold value or not; if the reinforcing steel bar is blunt, obtaining a displacement field of the model at the current time step and the damage degree of each material point, and updating the erosion coefficient of each material point in the model; according to the method, a crack path does not need to be preset, initiation and expansion of complex cracks can be naturally described, the problem of singularity of a crack tip and the problem of convergence do not exist, damage behaviors such as brittle fracture and peeling of concrete can be simulated, and extra constitutive adjustment and model adjustment are not needed.

The invention relates to a complex medium inversion imaging method and system guided by a physical information neural network, and the method comprises the steps: constructing a two-dimensional grid model of a block structure with different medium distributions, obtaining out-of-plane displacement data at grid points through staggered grid finite difference, and forming a wave field full-waveform data set; constructing a physical information neural network, wherein the physical information neural network comprises a first feed-forward network for modeling a mapping relation of a displacement field of the sample, a second feed-forward network for modeling a velocity field of the sample, and a residual network; based on the wave field full-waveform data set, training a physical information neural network by taking minimization of compound loss as a target; and taking the space-time coordinates of the sample as input, and utilizing the trained physical information neural network to obtain predicted displacement and sound velocity of the corresponding position of the space-time coordinates, so as to realize inversion imaging of sound velocity medium distribution. According to the method, the ultrasonic propagation model of the complex medium can be quickly and accurately established under the condition that a small amount of marked data is used.

The invention relates to the technical field of filter film testing, and discloses filter film thermal deformation testing equipment and a testing method. The method comprises the following steps: based on a test environment, carrying out a temperature cycle test on the filter film, and collecting a triple data set at the same time; performing image preprocessing and displacement field construction on the triple data set, and decomposing a displacement field into a linear combination of a standard deformation mode to obtain a thermal deformation response function; constructing a hierarchical probability graph model based on a thermal deformation response function, and extracting thermal deformation feature structural representation through variational inference; and performing deviation compensation on the structural representation of the thermal deformation characteristics to obtain environment self-adaptive thermal deformation characteristics, and calculating a thermal deformation reliability evaluation result comprising a thermal expansion coefficient matrix, a thermal deformation anisotropy index and thermal ripple strength. And comprehensive evaluation and reliability analysis of the thermal deformation characteristic of the filter film are realized.

The invention discloses a power transmission tower deformation monitoring method and device and a computer program product, and the method comprises the steps: obtaining a multi-angle collection image of a random speckle pattern on the surface of a power transmission tower; constructing an image sequence based on the multi-angle acquired images, reconstructing a three-dimensional displacement field on the surface of the power transmission tower by using a stereoscopic vision reconstruction algorithm, and exporting a strain tensor field; performing time sequence processing on the strain tensor field under a plurality of time nodes through a strain evolution analysis model of a continuous time sequence, and extracting a strain change trend of each preset structure part; carrying out space consistency mapping and weighted interpolation processing according to the strain change trends of the plurality of preset structure parts, and calculating to obtain the overall structural dependent variable of the power transmission tower; and S5, in combination with historical strain data and the current overall structural dependent variable, identifying an abnormal deformation area of the power transmission tower by comparing the spatio-temporal evolution mode of the strain tensor field. The method has the effect of improving the accuracy of deformation monitoring of the power transmission tower.

The invention discloses a functional gradient piezoelectric material optimization method based on a physical information network and deep regression. The method comprises the following steps of: 1, constructing a physical information neural network embedded with a piezoelectric constitutive relation; 2, developing a deep regression network based on symbolic regression, wherein the deep regression network is used for converting the piezoelectric phase volume fraction distribution predicted by the neural network into an analyzable mathematical expression; step 3, carrying out joint optimization on the two networks by adopting an alternate iteration strategy; under the condition that parameters of the deep regression network are kept unchanged, a physical information neural network is trained preferentially to meet the precision requirements of a displacement field, a stress field, potential distribution and a piezoelectric phase volume fraction; and then fixing the physical information network, optimizing the expression generation capability of the deep regression network, and finally analyzing the piezoelectric phase volume fraction expression. According to the method, the physical information neural network and the depth symbol regression technology are fused, so that multi-physical field collaborative optimization of component distribution of the functionally graded piezoelectric material is realized.

The invention provides a plate forming control method and device based on deep learning, and relates to the technical field of stamping control, and the method comprises the steps: training an original CNN model through a sample data set, and obtaining an intermediate CNN model; the sample data set comprises plate static data and springback data; the plate static data comprises initial size parameters and material parameters; the rebound data comprises a first displacement field after the pressure head is evacuated and the plate is rebounded under different punching depths; determining displacement field compensation values corresponding to the different stamping depths based on a first displacement field error corresponding to the experimental data and the simulation data before the rebounding of the plate under the different stamping depths and a second displacement field error after the rebounding of the plate; adjusting the parameters of the intermediate CNN model by using the displacement field compensation value until the prediction error of the intermediate CNN model is less than a preset threshold, and obtaining a target CNN model; and the plate static data of the to-be-processed plate and the target forming effect are input into the target CNN model, and the target stamping depth is obtained.

The invention relates to the technical field of simulation, and particularly discloses a finite element simulation method for a steel-concrete mixed structure, and the method comprises the steps: building a finite element model; selecting material parameters and defining a constitutive model; dividing a finite element grid; setting load and boundary conditions; taking the optimized material constitutive parameters as material definition parameters of a formal finite element model; evaluating the critical bearing capacity and ductility performance of the structure; and extracting a displacement field, stress distribution and a load-displacement curve of the structure, and identifying typical mechanical response characteristics of the hybrid structure in elastic, elastoplastic and descending stages. By optimizing constitutive model parameters, high-precision finite element simulation of the whole process from elasticity to damage of the steel-concrete mixed structure is achieved.

The invention relates to the technical field of hydraulic engineering safety monitoring, and discloses a dam safety studying and judging method based on monitoring data multi-physics field simulation, which comprises the following steps: S1, multi-source data acquisition and time-space alignment; s2, dynamically updating the numerical model; s3, safety evaluation and early warning decision making; s4, performing multi-source risk coupling analysis; and S5, issuing the early warning information in a multi-mode manner. According to the method, time-space reference unification of multi-source monitoring data is achieved through feature point matching and sliding window cross-correlation analysis, a self-adaptive Kalman filtering algorithm is adopted to dynamically invert permeability coefficients and elastic modulus parameters, and boundary conditions of a finite element model are adjusted in combination with real-time water level changes; the dynamic simulation precision of a seepage field-displacement field-stress field coupling model is improved, the static evaluation limitation of a fixed threshold value method is broken through through a three-dimensional time-varying safety envelope surface and a Bayesian network grading early warning decision tree, and the risk prediction capability in the flood routing process is enhanced through multi-parameter joint probabilistic reasoning.

The invention relates to a three-dimensional structure displacement stress field prediction method based on a point cloud neural operator, a medium, equipment and application. The method comprises the following steps: constructing a neural operator model for predicting a structure physical field; collecting geometric feature data of the three-dimensional structure and corresponding working condition data; training the neural operator model by using the acquired data and the query position of the internal space of the three-dimensional structure; inputting geometric feature data and working condition data corresponding to the three-dimensional structure into the trained neural operator model, and querying any spatial position in the three-dimensional structure to obtain a displacement predicted value and a stress predicted value at the corresponding position; media and equipment are realized based on the method and are applied to jet engine support displacement stress field prediction. The method can be directly used for a non-parametric complex three-dimensional structure, continuous prediction of a displacement field and a stress field of any spatial query position in the structure is achieved, the structure response prediction efficiency is remarkably improved, the calculation cost is reduced, and the method has good engineering application value.

The invention relates to the field of constructional engineering and structural design, and discloses a fabricated steel structure thermal expansion analysis and calculation method based on a finite element method.The fabricated steel structure thermal expansion analysis and calculation method comprises the following steps that fractal parameters are inversely optimized by constructing a thermal-force coupling equation and introducing an adjoint equation, errors of a temperature field and a displacement field are iteratively calculated, and the fractal parameters are updated; the convergence condition is met; the adjoint equation comprises an adjoint equation of a temperature field and an adjoint equation of a displacement field, and is used for calculating the gradient of the fractal parameter and performing back propagation optimization; in each iteration, recalculating the fabricated steel structure thermal expansion analysis model based on the updated fractal parameters, and judging whether a convergence condition is met or not; and finally outputting the optimized temperature field, displacement field and fractal parameters through repeated iterative optimization. According to the method, the calculation efficiency and optimization precision of thermal expansion analysis can be improved, the safety, stability and reliability of the steel structure under the thermal action are ensured, and the method is suitable for automatic analysis and optimization of a large-scale structure.

The invention relates to a dam slope deformation monitoring method based on fusion of a measuring robot and a GNSS (Global Navigation Satellite System). The method comprises the following steps: performing signal synchronization and delay correction on a dual-mode observation pillar of a GNSS and a prism target of a measurement robot, and constructing a space-time reference network; controlling the measurement robot to emit an anti-interference laser pulse, counteracting environment vibration, and outputting a vibration compensation coordinate set; obtaining a dam slope displacement field through variable weight adaptive fusion processing; performing encryption scanning on an abnormal area exceeding a first deformation threshold in the displacement field to generate a polarization interferometric phase diagram, forming an adaptive data stream through three-stage dynamic classification coding, calculating a reference point drift component in combination with a dual-mode observation pillar, and extracting a net deformation set; and finally, mapping the net deformation set to a BIM model, constructing a color temperature gradient three-dimensional deformation situation map, triggering early warning for an area exceeding a second deformation threshold value, and generating a three-dimensional early warning map. According to the method, high-precision space-time unification of multi-source data can be realized, and the monitoring consistency and the anti-interference capability in a complex environment are remarkably improved.

The invention discloses a multispectral satellite cloud picture prediction method based on motion stripe decoupling. The method comprises the following steps: carrying out normalization preprocessing on multi-channel satellite observation data; utilizing a motion branch model to extract motion features based on a displacement field, iteratively updating a prediction frame in an autoregressive distortion-correction pipeline, and keeping physical consistency in combination with atmospheric dynamics and smoothness constraint; a texture branch model is utilized to sequentially pass through a high-fidelity encoder, long memory state space modeling and a high-fidelity decoder, time sequence texture features are extracted, and cloud picture details are kept; the motion features output by the motion branches and the texture features output by the texture branches are input into a gating fusion module, adaptive weighting of the features is achieved through convolution and a gating mechanism, and fusion features are output; and carrying out reverse normalization processing on the fusion features to obtain satellite cloud picture prediction results at a plurality of moments in the future. According to the method, the spatial texture fidelity of cloud picture prediction can be improved while the physical interpretability is ensured, and high-precision satellite cloud picture prediction is realized.

The invention relates to the technical field of medical image processing, in particular to an endoscopic surgery video real-time structure analysis method and system, and the method comprises the steps: carrying out the frame-by-frame semantic segmentation of real-time video data, and generating a segmentation mask of each frame of image; calculating a comprehensive quality score of the target frame based on the segmentation masks of the target frame and the previous frame of the target frame; analyzing a motion amount index between adjacent frames; generating the current length of a sliding time sequence window according to the comprehensive quality score and the exercise amount index, and fusing multiple frames of segmentation masks in the sliding time sequence window to generate a reference mask; extracting a key point from the reference mask, and obtaining a displacement vector of the key point from a previous frame of the target frame to the target frame; generating a pixel-level displacement field according to the displacement vector, deforming the segmentation mask of the previous frame of the target frame to the target frame, and generating a prediction mask of the target frame; and performing superposition display on the prediction mask and the image of the target frame. According to the scheme, the time sequence consistency and stability of the video semantic segmentation result can be enhanced.