90 results about "Nonlinear deformation" patented technology

The invention relates to the technical field of tunnel engineering, and discloses a tunnel multi-field coupling nonlinear deformation analysis method and system.The method comprises the steps that geological environment information of a tunnel area is collected, a multi-source physical field boundary condition model is built based on collected data, and a heat-seepage-stress-time four-field coupling control model is built based on the collected data; specifying a nonlinear response model for the geological medium to truly reflect the stress-strain behavior of the geotechnical material; solving a coupling equation set, and optimizing the four-field coupling control model; introducing measured data to correct the model; the system comprises a geological data acquisition module, a boundary condition modeling module, a coupling model establishment module, a numerical solution module, a measured data correction module and a visual output and control interface module. According to the method, high-fidelity prediction and evolution analysis of the deformation behavior of the tunnel under the complex geological condition are realized based on comprehensive acquisition of the geological environment, multi-field boundary modeling, control equation construction and numerical calculation.

The invention is suitable for the technical field of earth surface monitoring, and provides an earth surface deformation monitoring method and system based on a time sequence InSAR, and the method comprises the following steps: carrying out the SAR image screening and interferogram generation, and collecting corresponding meteorological data and thermal infrared data; performing space-time adaptive atmospheric phase correction, taking the air pressure vertical gradient and the temperature anomaly as driving factors of atmospheric delay, and separating an atmospheric phase through a space-time weighted model; dynamic deformation modeling is carried out, and deformation is decomposed into linear deformation and nonlinear deformation; the method comprises the following steps: taking a mining area road network as a geometric constraint, unwrapping a coherent region by adopting a minimum cost flow algorithm, converting an unwrapping phase into sight-line-direction deformation, and calculating horizontal and vertical deformation components in combination with InSAR sight-line-direction deformation and a digital elevation model. The method adapts to a complex deformation mechanism of a mining area by capturing linear and nonlinear deformation. Through sight line deformation and DEM geometric projection, vertical and horizontal deformation separation is realized, and the deformation direction is determined.

The invention discloses a wind turbine blade semi-coupling aeroelastic modeling method considering nonlinear deformation, and belongs to the technical field of fan simulation calculation. In combination with calculation parameters used by three-party software, establishing a blade structured load model and a blade correction aerodynamic model, calculating an aerodynamic load and a structural load, and superposing: superposing the aerodynamic load, the gravity load and the centrifugal force load of the blade as an external load of a nonlinear structure control equation of the blade, and taking the external load as a calculation parameter; according to the method, the blade nonlinear structure model and the semi-coupling simulation process of the three-party wind turbine simulation software are constructed, and under the same simulation environment and load conditions, the blade nonlinear structure model and the three-party wind turbine simulation software can be subjected to semi-coupling simulation, so that the blade nonlinear structure model and the three-party wind turbine simulation software can be subjected to semi-coupling simulation calculation. The higher-precision non-linear motion response characteristics of the super-long flexible wind turbine blade are calculated, and a good effect can be achieved in the aspects of initial structure design and response evaluation of the large wind turbine blade.

The invention discloses a GNSS (Global Navigation Satellite System) nonlinear deformation correction method based on a full-spectrum three-dimensional thermoelastic model. The GNSS nonlinear deformation correction method comprises the following steps: carrying out discrete Fourier decomposition on a temperature time sequence of each grid point; carrying out space spherical harmonic expansion and coefficient projection on each decomposed frequency; based on a space spherical harmonic expansion and coefficient projection result, considering the periodicity of the temperature, and performing sine / cosine representation of a frequency-by-frequency temperature item; performing spherical harmonic representation and time expansion on the deformed displacement field corresponding to the periodic temperature based on the spatial spherical harmonic expansion and coefficient projection result; solving a deformation equation in spherical coordinates in a Love number form to obtain an expression of a complex Love number; based on the complex spherical harmonic coefficient and the complex Love number of the temperature, full-spectrum synthesis of three-component displacement at the spherical surface is carried out; and performing internal consistency and external dispersion verification based on a full-spectrum synthesis result.

The invention discloses an intelligent detection method and system for a flexible circuit board based on machine vision, and relates to the field of circuit board detection.The method comprises the steps that firstly, through a global coarse registration step, rigid transformation such as translation, rotation and zooming of the whole to-be-detected FPC image is rapidly corrected, and preliminary alignment with a standard template is achieved; then, local fine registration is introduced for nonlinear deformation such as local stretching and wrinkles caused by the characteristics of the FPC material, so that the fine and non-uniform local dislocation is accurately sensed and compensated, and nearly pixel-level accurate matching between the to-be-detected image and the template is realized. On the basis of high-precision alignment, differential attention deficit segmentation is carried out, so that false differences caused by inaccurate deformation registration can be effectively eliminated, real defects can be accurately identified, the false alarm rate is remarkably reduced, and the reliability and practicability of a detection scheme are greatly improved.

The invention relates to the technical field of semiconductors, and discloses a semiconductor defect and yield Map-based automatic calibration method, which comprises the following steps of: acquiring wafer surface data in real time through a multi-modal sensor, analyzing station process parameters, and generating a time-space aligned wafer holographic data set; performing nonlinear deformation compensation based on the wafer holographic data set to generate a dynamic Die grid; utilizing a multi-task deep reinforcement learning model to predict initial Offset parameters of the defect Map and the yield Map; and performing global optimization on the initial Offset parameters through a quantum annealing algorithm, and outputting an optimal Offset. Data are collected through a multi-mode sensor, space-time alignment is carried out through Kalman filtering, a wafer holographic data set of space-time alignment is generated, the data space-time unification is achieved, the wafer state is accurately and completely reflected, and a high-quality data basis is provided for defect analysis and yield evaluation. The problem that data of the multi-modal sensor are inconsistent in time and space and are difficult to effectively fuse is solved.

The invention discloses a rapid deformation reconstruction method for a large-displacement flexible beam structure, and belongs to the field of structural health monitoring. On the basis of a co-rotation coordinate method, the local displacement is obtained after rigid body displacement is removed from the total displacement of the flexible beam structure. Local displacement obtained through a co-rotation coordinate method is substituted into an iFEM formula based on a small deformation hypothesis, and the deformation reconstruction problem under the geometric nonlinear condition is converted into the problem that the mean square error sum of theoretical strain and actually measured strain is minimum by searching an optimal node displacement parameter to achieve square functional optimization. And solving the optimal solution of the functional by adopting an optimization algorithm. In addition, a simplification strategy which ignores a high-order small quantity is adopted, so that the whole nonlinear deformation reconstruction method remarkably improves the calculation efficiency on the premise that the calculation precision is not sacrificed. According to the method, the node displacement parameter enabling the functional to be minimum can be rapidly solved, and rapid and high-precision deformation reconstruction of the large-displacement flexible beam structure is achieved.

The invention relates to the technical field of intelligent printing quality detection, in particular to an intelligent printing quality detection method based on image recognition. The system comprises an image preprocessing module, a physical model construction module, a deformation iterative calculation module, an energy decoupling analysis module and a closed-loop feedback control module. The method comprises the steps of generating a feature point matrix and a to-be-detected feature field by acquiring standard and real-time image data; the core of the method is to construct a virtual tensor spring network model, map image differences into an external acting force field, and solve deformation potential energy distribution by using finite element iteration; a Laplace operator is adopted to execute energy decoupling, low-frequency isotropic stress and high-frequency topological potential energy singular points are separated, and a residual thermodynamic diagram is generated according to the singular points to position defects and adjust transmission tension; according to the method, the transformation from pure image comparison to physical potential energy evolution is realized, the mutual exclusion limitation of deformation tolerance and detection sensitivity is broken through, and the nonlinear deformation interference in the high-speed transmission of the flexible material can be effectively filtered.

The invention discloses an enhanced Swinin-Unet liver tumor segmentation method fusing blood vessel guidance and adaptive deformation attention. Through combination of elastic B-spline registration and affine transformation, nonlinear deformation is compensated, a multi-modal image space alignment error is reduced, and tumor boundary blur and feature dislocation are reduced. Topological features are extracted in combination with a blood vessel reconstruction module, and model attention is effectively guided. The encoder adopts a convolution pool and a deformable attention SADR down-sampling, and a lightweight deformable attention module is integrated through jump connection, so that effective multi-scale feature fusion and space alignment are realized, and the model perceptibility is enhanced. The graph theory model is embedded into liver lobe partition and blood vessel curvature constraint, and it is ensured that the segmentation result is strictly matched with blood vessel blood supply topology. In order to solve the class imbalance problem, an FA loss function is provided and combined with a structure correction algorithm (SGDR), so that incomplete segmentation and false positive artifacts are effectively reduced. Experimental verification on a LiTS data set shows that the method is superior to an existing method in liver and tumor segmentation, and Dice coefficients reach 93.38% and 91.39% respectively.

The invention provides a nonlinear finite element discrimination method for a critical stable section of an underground cavern, and the method comprises the steps: determining a rock mass strain softening parameter according to a rock mass mechanical test in actual engineering; performing secondary development on a rock mass constitutive model based on the rock mass strain softening parameters; based on the rock mass constitutive model after secondary development, a nonlinear finite element initial model for solving the critical stable section of the underground cavern is constructed; the nonlinear finite element model is solved, and the overall tangent rigidity of the model in the calculation time step is obtained; based on the overall tangent stiffness, calculating to obtain a minimum characteristic value of an overall tangent stiffness matrix; and based on the minimum characteristic value, distinguishing the critical stable section of the underground cavern. The method directly and quantitatively reflects the essential characteristics of rigidity loss and bearing capacity reduction of the whole cavern structure system after a complex nonlinear deformation path including strain softening is considered, so that the critical instability moment and the potential instability section are accurately captured.

The invention relates to an image registration method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a first image and a second image obtained through image collection of a target physiological region, the first image data being a three-dimensional image, and the second image being a two-dimensional image; and determining a deformation field describing a mapping relation between the first image and the second image, and performing nonlinear deformation on the first image according to the deformation field to obtain a deformed image. And projecting the deformed image to obtain a two-dimensional third image capable of measuring a registration effect with the second image. According to the embodiment of the invention, through the architecture for image deformation and projection and image registration based on the deep learning automatic learning deformation field, the elastic registration between the images can be simply, quickly and accurately realized. Meanwhile, the image registration mode is realized by depending on a software algorithm without depending on the improvement of a hardware structure, so that the cost is low.

The invention discloses a landslide deformation prediction method and system combining multi-source data and residual optimization, and belongs to the technical field of landslide deformation monitoring. According to the method, multi-scale spatial-temporal characteristics are extracted through a CvT model to realize preliminary prediction, an xLSTM model with GNSS as external guidance is innovatively introduced to carry out dynamic time sequence modeling and compensation on prediction residual errors, and finally a high-precision displacement prediction value is output through residual error optimization. According to the method, dynamic deep fusion and closed-loop correction of InSAR large-range monitoring and GNSS high-precision data under a deep learning framework are realized, and the key technical bottlenecks of system error accumulation, insufficient multi-source data collaboration and limited nonlinear deformation modeling precision in the existing method are effectively solved; the accuracy, the stability and the physical credibility of landslide prediction are remarkably improved, and the method has outstanding technical advancement and practical value.

The invention relates to the technical field of image processing, in particular to a positioning method for lamination of multi-layer flexible materials based on machine vision, which comprises the following steps: acquiring images of a first flexible material to be laminated and a second flexible material serving as a reference; contour geometry and surface texture features are extracted in parallel for the image, and a first feature map and a second feature map with the features as nodes and the spatial topological relation and gradient mutual information as weighted edges are constructed; calculating an initial nonlinear deformation field of the first flexible material; utilizing the initial nonlinear deformation field guide graph matching algorithm to obtain rough matching feature point pairs; and further solving to obtain a final non-rigid transformation model, and generating positioning parameters including non-uniform scaling and local distortion to guide fitting operation. According to the invention, the quality and efficiency of the flexible material laminating process can be improved.

The invention relates to the technical field of machine vision and optical measurement mechanics, and discloses a deformation identification and measurement method based on machine vision and deep learning, and the method comprises the following steps: S1, system calibration and base library construction; s2, offline training of the hybrid model; s3, real-time decoupling and coefficient regression are carried out; and S4, final assembly and physical quantity calculation of the deformation field. According to the method, a deformation field is decoupled into a global nonlinear deformation field () and a residual deformation field () through S3, and the global nonlinear deformation field () and the residual deformation field () are superposed. The physical significance and the stability of a deformation main body are ensured by utilizing the physical deformation base library constructed in S1 and model reconstruction; meanwhile, a luminosity residual image () generated in the S3 is used for driving module reconstruction, local high-frequency disturbance which is not covered by a physical model is accurately compensated, a high-dimensional deformation field is decoupled into low-dimensional global physical prior regression and residual correction, and efficient real-time measurement is achieved; meanwhile, numerical difference is replaced by analytic derivation, so that the signal-to-noise ratio and the measurement precision of the strain field are remarkably improved.

The invention discloses a ground-air-pipe multi-dimensional coupling PCCP pipeline health monitoring system and method, and belongs to the technical field of underground pipeline safety monitoring, and the system comprises an InSAR air monitoring unit, an artificial corner reflector array and a distributed optical fiber sensing unit. The PCCP pipeline is monitored from the air through the InSAR technology, and ground surface settlement rate abrupt change points and non-linear deformation areas are extracted through InSAR time sequence analysis; an adjustable artificial corner reflector is arranged along a vertical projection area of the pipeline, so that the phase incoherence problem of the InSAR technology in pipeline monitoring is improved, and the InSAR phase stability is enhanced; distributed optical fibers are pre-buried in the PCCP manufacturing stage and used for collecting pipe body strain data in real time, an abnormal space correlation map is constructed in combination with InSAR time sequence analysis data, and the hidden damage position and probability of an underground pipeline are deduced and inversely deduced through Bayesian causality. Omni-directional and multi-dimensional health monitoring from the ground and the air to the pipeline is achieved, the monitoring precision and reliability are effectively improved, and a powerful guarantee is provided for safe operation of the PCCP.

The invention relates to the technical field of underground engineering, and discloses a construction safety assessment method, system, equipment and medium for a shield tunnel, and the method comprises the steps: quantifying a construction load and a foundation coefficient in a current construction step, inputting the quantified construction load and foundation coefficient into a nonlinear mechanical solving framework composed of a longitudinal multi-ring model and a single-ring variable model, and carrying out the processing. The longitudinal deformation quantity of each beam unit is obtained, when the longitudinal deformation quantity does not meet the preset requirement, the quantification process of the longitudinal deformation quantity is repeatedly executed until the preset requirement is met, and the bending and shearing modes of each beam unit are determined according to the single ring deformation model so as to evaluate the safety state of the current construction step; the current construction step is updated to iteratively execute the evaluation process of the safety state in the updated current construction step until all construction steps are completed, and a safety evaluation result of the shield tunnel under the action of the construction load is obtained; and the nonlinear deformation response in the longitudinal direction of the shield tunnel is accurately solved, and comprehensive and accurate safety evaluation of the shield tunnel is realized.

This invention proposes a cross-coordinate system road network data fusion method based on hierarchical matching and non-rigid registration, belonging to the field of road network technology, and solves the problems of computational complexity and poor versatility of existing methods. The method includes: acquiring and preprocessing benchmark and target road network data; estimating global transformation relationships by calibrating feature points, and initially aligning the benchmark road network to the target coordinate system to obtain a global region; constructing a fixed image of the target road network and a moving image of the benchmark road network within the global region; using algorithms such as B-spline free deformation that can model local nonlinear deformations to perform non-rigid registration on the fixed and moving images, optimizing the cost function to obtain pixel-level deformation fields; geometrically correcting the benchmark road network based on the deformation field, transferring the target road network attribute information, and summarizing and outputting a fused dataset. This method improves local matching accuracy and versatility through a hierarchical strategy and pixel-level registration, overcoming the influence of differences in road segmentation.

The invention discloses a mechanical driving system and method for facial expressions, and belongs to the technical field of bionic robots. The system comprises a stress response unit, a mechanical chaos engine and an expression execution mechanism. The stress response unit adopts a design structure that an arc-shaped elastic sheet is in bridge connection with an elliptical ring, converts external mechanical force into nonlinear deformation and realizes automatic resetting; the mechanical chaos engine takes mechanical tolerance as an internal random source, realizes multi-physics field coupling and chaos amplification through a three-stage driving structure, and drives an expression execution mechanism composed of a gradient hardness material, an asymmetric groove array and an intercommunication ring. According to the method, by sensing external force input, a chaos engine is excited to generate unpredictable, natural and rich facial random expressions. Controllable expression of emotional tendency is achieved through a pure mechanical structure, and static and dynamic expressions reflecting the emotional change process can be output. The system is simple and compact in structure, the method randomness is reliable, and the method is suitable for the fields of robots, intelligent toys, emotion counseling, man-machine interaction and the like.

The application discloses a structure nonlinear deformation reconstruction method and system based on isogeometric and reduced basis, and the method comprises the following steps: measuring initial strain data of a ship plate structure surface, constructing a plurality of four-node inverse shell elements of the ship plate based on the initial strain data; assembling the plurality of four-node inverse shell elements to obtain a whole stiffness matrix; reconstructing the whole stiffness matrix based on the measured strain data of the ship plate to obtain a linear reconstruction result of the ship plate; correcting the linear reconstruction result based on the reduced basis, and fusing the correction increment to the linear reconstruction result for nonlinear iteration to complete nonlinear deformation reconstruction. On the basis of the principle of isogeometric analysis, the application combines the inverse finite element method to establish a four-node inverse shell element, realizes high-precision ship structure displacement reconstruction through a small number of strain measuring points, and helps designers to intuitively understand and deeply understand the mechanical properties of the structure and to monitor the safety of the structure in real time.

A method for calculating the in-plane nonlinear deformation of an arch structure with axial defects in a Cartesian coordinate system is presented. This method is based on two states of the arch structure's curve element in the Cartesian coordinate system: an ideal axis and an actual axial defect. It derives the changes in the arc length and curvature of the curve element before and after deformation, obtaining expressions for the nonlinear compressive and bending strains in the in-plane of the arch structure with axial defects in the Cartesian coordinate system. The method also derives the nonlinear equilibrium differential equations in the in-plane of the arch structure with axial defects in the Cartesian coordinate system, obtaining solutions that satisfy the mechanical and geometric boundaries of the arch structure with axial defects. Finally, the method derives the nonlinear equilibrium governing equations in the in-plane of the arch structure with axial defects, ultimately obtaining the solution to the nonlinear mechanical problem in the in-plane of the arch structure with axial defects in the Cartesian coordinate system. This method can obtain the essence of the nonlinear mechanical problem of the arch structure with axial defects in the Cartesian coordinate system, filling the gap in the nonlinear strain-displacement expression for the in-plane of the arch structure with axial defects in the Cartesian coordinate system.

The invention discloses a small-scale deformation InSAR time sequence inversion method and system based on multiple stable points, and the method comprises the following steps: S1, carrying out the small baseline interference processing of multi-temporal synthetic aperture radar data, and screening high-coherence pixel points, S2, recognizing a stable point candidate set with dense spatial distribution; s3, constructing an initial atmospheric delay phase model covering a full interferogram range; s4, obtaining an interferometric phase after atmospheric correction; and S5, inverting the deformation rate and the deformation time sequence parameters of the InSAR again. According to the method, dense stable points are screened based on a deformation rate threshold value, and a space atmosphere model is constructed, so that the problem that atmosphere delay and nonlinear deformation are difficult to separate in InSAR time sequence analysis is effectively solved, and high-signal-to-noise-ratio and high-precision time sequence monitoring of small-scale weak deformation of the earth surface is realized.

This invention discloses an adaptive adjustment device and method for tool holes in a high-precision circuit board alignment system. The device includes an upper camera group, a lower camera group, and multiple PIN adjustment units. The upper camera group acquires images of a target on film, while the lower camera group acquires images of the tool holes on the circuit board. The theoretical coordinates of the current part number are loaded, and the dual cameras are simultaneously triggered to acquire images and calculate the actual offset vector of each tool hole. When the offset exceeds a preset threshold, the theoretical position of the tool hole is used as the control point. A nonlinear deformation field is reconstructed using the radial basis function of a thin-plate spline, and the displacement compensation amount of each PIN adjustment unit is calculated based on the deformation field, driving each PIN unit to move to the target position. The flexible conical PIN head is coated with a PEEK coating and, in conjunction with a force sensor, achieves adaptive force-controlled insertion, automatically correcting the center offset. This method can accurately compensate for the nonlinear deformation and temperature drift of the circuit board, reduce tool hole wear, and improve changeover efficiency and alignment accuracy.

The invention provides a calibration torque sectional type dynamic correction method and device based on an angle sensor, and relates to the field of torque measuring equipment calibration, and the method comprises the steps: constructing a plurality of sectional correction functions corresponding to a plurality of load intervals through torque load time-varying regularity analysis, so as to generate a correction function load mapping relation set; after the real-time torque measured value and the real-time inclination angle are received, the real-time torque measured value is adopted to traverse a plurality of load intervals in the correction function load mapping relation set, and a real-time correction function is positioned and called; and a real-time torque correction value is calculated and output by inputting the real-time inclination angle and the real-time torque measured value into the real-time correction function. The technical problem that torque calibration full-range systematic errors are caused by moment arm inclination nonlinear deformation in the prior art is solved. The technical effects of effectively eliminating systematic deviation caused by nonlinear change of the inclination of the force arm and realizing closed-loop correction of the torque error of the torque measurement equipment are achieved.

The invention discloses a slope body strength change monitoring method for a waste slag field side slope, and relates to the field of slope body strength change monitoring. The method is based on a strength reduction method, combines a three-dimensional data grid and monitoring data fusion, dynamically evaluates the slope strength change, and identifies the slope instability risk in real time through an intelligent early warning mechanism. According to the method, the stress-strain relation of the soil body is dynamically described by adopting the nonlinear constitutive model on the basis of the strengthening strength reduction method in combination with geological data and considering the nonlinear deformation characteristic of the soil body, the strength reduction coefficient is adjusted in real time in the monitoring process, and the accuracy of slope stability evaluation is improved.

The invention belongs to the technical field of open-pit coal mine slope disaster risk intelligent monitoring and early warning, and discloses a multi-source data fusion-based open-pit coal mine slope disaster risk diagnosis method, which comprises the steps of performing sequential deformation risk diagnosis based on a slope radar / GNSS (Global Navigation Satellite System); crack feature risk diagnosis based on unmanned aerial vehicle images; and carrying out regional risk diagnosis based on PS-InSAR and an entropy weight method. The problems that an existing single monitoring means is rigid in threshold value and insufficient in data utilization are solved, and a crack risk diagnosis model dynamically adaptive to rock mass nonlinear deformation is constructed; according to the method, the problems of low unmanned aerial vehicle crack identification precision and disjunction of geographic coordinates can be solved, and accurate quantification and position matching of crack characteristics are realized; according to the method, the problems of PS-InSAR region early warning physical constraint deficiency and multi-factor weight subjectivity are solved, deep fusion of slope radar / GNSS, unmanned aerial vehicle images and PS-InSAR multi-source data is realized, a three-level diagnosis chain of'time sequence anomaly-crack feature-region risk 'is formed, and the comprehensiveness and reliability of risk diagnosis are improved.

In some examples, a computing system generates a linear model representing an estimate of a shape space using a first set of registered 3D digital shapes registered to a shape template. The computing system determines a nonlinear deformation model for the shape space using a second set of registered 3D digital shapes registered to the shape template. The computing system creates an initial registration to the shape space for an unregistered shape using the linear model. The computing system predicts an updated registration based on the initial registration using the nonlinear deformation model. In response to determining a shape distance between the updated registration and the unregistered shape being below a threshold value, the computing system adds the updated registration to the first set of registered 3D digital shapes to obtain an updated first set of registered 3D digital shapes.

The invention discloses a deformation error compensation method for a dilator in total knee arthroplasty, and aims to solve the problem of measurement errors caused by deformation of a cantilever structure. The method comprises the following steps of: firstly, performing parameterized dynamic modeling and simulation on an external execution mechanism through finite element software, calibrating a model by combining actual measurement data of a physical prototype, and constructing a high-precision deformation data set; then, the data set is used for training a BP neural network optimized through a genetic algorithm, and the network takes the applied force and the displacement measurement value which are collected in real time as input and outputs a real displacement value after compensation. And finally deploying the trained lightweight network model in an embedded processing unit of the spreader to realize real-time error compensation. According to the method, complex nonlinear deformation errors are effectively compensated, the average absolute error is reduced to 0.056 mm, the real-time response delay is smaller than or equal to 5 ms, the method is remarkably superior to a traditional linear method, more accurate gap measurement is provided for an operation, and the method can be further used for forming closed-loop control and improving the intellectualization and precision of the operation.

The invention discloses an anti-deformation self-adaptive lifting system. The anti-deformation self-adaptive lifting system comprises a guide component, an actuating mechanism and a sliding assembly. The sliding assembly comprises an integrally-formed sliding block, a plurality of elastic arms capable of retreating in the radial direction are formed by the sliding block through a slotting structure, and at least two elastic arms in different directions share a containing cavity in the sliding block. When the guide component generates nonlinear deformation due to stress, the elastic arm absorbs the size change through mechanical retreat displacement, and positive pressure increment between the sliding block and the rail is limited, so that resistance is reduced, and stability is improved. Through the space nested design, the screw rod is arranged in the containing cavity in a penetrating mode, decoupling absorption of multidirectional deformation is achieved while the structure is kept extremely compact, and the technical problem of clamping stagnation and jumping of a long-stroke guide pipe is solved.

The application discloses a kind of wire hanging frame wire classification hanging intelligent management method and system, it is related to wire hanging management field, comprising: to the wire image is three-dimensional reconstruction processing, judge the similarity between wire image and standard wire, analyze wire surface crack;Wire surface crack is combined with wire physical property parameter, simulate the nonlinear deformation path of wire under different current intensity, obtain the heating deformation process of wire;According to heating deformation process, establish linear influence judgment model, obtain the causal relationship between wire deformation and current intensity;Based on causal relationship, evaluate the crack propagation trend of wire material, predict the required duration of wire insulation pyrolysis due to deformation under the premise of target current;Based on the required duration prediction result, the wire is classified, and the matching of the wire and the hanging position is realized.The application can assist field operation personnel to optimize hanging according to risk level, improve hanging safety.

This invention discloses an inverse consistency printed image registration method based on adaptive perception. First, a printed image registration dataset is constructed, and the images are normalized. A deep learning network is then constructed for non-rigid registration of printed images. Next, the forward deformation field from the reference image to the image to be registered is predicted. This forward deformation field is input into a local adaptive perception module to obtain an optimized deformation field that highlights the true local nonlinear deformation. Finally, the registration network is optimized end-to-end based on a joint loss function, and the optimized deformation field is used to perform a spatial transformation on the printed image to be registered, outputting a registration result aligned with the reference printed image. This invention achieves accurate modeling of local nonlinear deformation in printed images, thereby improving the accuracy and stability of printed image registration and providing a reliable foundation for subsequent high-precision defect detection tasks.