506 results about "Displacement field" patented technology

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 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 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 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 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 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 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.

The invention relates to the technical field of structural deformation shape reconstruction, in particular to a novel inverse finite element deformation reconstruction method based on virtual deformation field physical constraint. According to the method, a virtual displacement field obtained by sparse strain through inverse finite element reconstruction is used as a strain extrapolation physical constraint; and an iteration thought is adopted, extrapolated strain and measured strain are used for inverse finite element deformation reconstruction again, a new virtual displacement field is obtained, and therefore strain extrapolation can be restrained again. And continuously iteratively reconstructing virtual deformation and extrapolation strain in this way, and finally reconstructing real structural deformation. And determining the accuracy rate of the deformation characterization values based on a plurality of actually measured actual deformation characterization values and a plurality of deformation characterization values determined by the extrapolation strain data, and determining the deformation reconstruction state based on the accuracy rate. According to the method, the strain measurement points are extrapolated through iteration in the deformation reconstruction process, and the virtual displacement field is used as physical constraint, so that the accuracy of deformation reconstruction is improved.

The invention relates to the technical field of slope monitoring, and discloses a slope slippage identification method and system for unmanned aerial vehicle inspection. According to the method, an unmanned aerial vehicle carries a laser radar to collect three-dimensional point cloud data of a slope, and an initial spatial distribution field is generated through spatial gridding preprocessing; establishing a slope space reference field model, and performing space consistency correction on the initial space distribution field to obtain a corrected displacement field at multiple time points; the unmanned aerial vehicle is controlled to periodically inspect according to the displacement safety threshold, and actual displacement is obtained by combining a ground displacement sensor; when the deviation between the grid unit displacement amount and the actual displacement amount exceeds a preset tolerance, extracting a historical displacement feature vector and dynamically adjusting a displacement value, and generating an updated displacement field; and inputting the updated displacement field into the space reference field model for repeated routing inspection until all the deviations are smaller than a preset tolerance. The method improves the accuracy and reliability of slope slippage identification, and is suitable for various slope monitoring scenes.

The invention relates to the technical field of laser cladding repair, and provides a positioning and clamping system and method for repairing the surface of a large working roll of a steel mill based on laser cladding, a three-field coupling prediction model of a temperature field, a stress field and a displacement field is established, and a partial least squares regression algorithm is combined with a Kalman filter to realize multi-step prospective prediction. According to the predicted thermal deformation, self-adaptive compensation control is implemented through a three-layer cascade compensation mechanism composed of a hydraulic drive, a piezoelectric ceramic driver and a laser head adjusting mechanism. The system adopts a time domain, frequency domain and time-frequency domain joint analysis method to extract vibration characteristics, vibration types are classified and identified through a support vector machine, and an active-passive hybrid suppression strategy is adopted for different vibration sources. High-precision positioning and stable clamping of the large working roller in the laser cladding repairing process are achieved, and the thickness uniformity and the surface quality of a cladding layer are remarkably improved.

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 relates to the technical field of intelligent scheduling, in particular to a steel structure weld fatigue cracking real-time monitoring system based on machine vision, which is characterized in that an image acquisition module acquires a high-stability image sequence in a strong vibration environment; the displacement field reconstruction module adopts a frequency domain-space domain combined decomposition technology to isolate environmental noise and material non-uniformity interference in a breakthrough manner, and an anti-noise deformation field and a stress distribution diagram are synchronously output through dual-channel parallel processing; a damage response module accurately locks sub-pixel-level crack initiation coordinates based on strain energy accumulation rate mutation, and dynamically predicts a yield risk area in combination with a displacement gradient diffusion model; the early warning module intelligently triggers graded response according to the crack position and the space coupling state of the risk area, performs positioning maintenance in a low risk area and automatic strengthening in a high risk area, and outputs heat treatment scheme coordinates when no crack exists; the system solves the problems of false detection caused by vibration noise and lagging response of single-parameter monitoring, and active protection from damage initiation to failure blocking is realized.

According to the cold region slope multi-field coupling effect simulation test device and method, the device is composed of a freeze-thaw environment test box, a slope model, an anchor rod axial loading system, a crack water injection assembly and a multivariate data monitoring system, and the multi-physical field coupling effect of freeze-thaw cycle, crack water permeation and long-term creep can be simulated. By dynamically adjusting the temperature gradient and graded loading parameters, data of a stress field, a temperature field and a displacement field can be collected in real time, the influence of different freeze-thaw conditions and creep drawing loads on a slope damage mechanism can be analyzed, a key test means is provided for revealing the strain rate effect, the aging deformation characteristics and the instability criterion of the slope in the cold region, and the test efficiency is improved. The method has an important guiding value for stability evaluation and disaster prevention and control of the cold region slope engineering.

The invention relates to the technical field of medical data processing, and provides a medical image data processing method based on deep learning, which comprises the following steps of: acquiring data according to an acquisition template, extracting a pulse time sequence by self-adaptive threshold peak detection, calculating an instantaneous phase according to linear interpolation, calculating a statistical magnitude, comparing a quantitative index with a preset threshold value, and calculating a pulse time sequence according to the statistical magnitude. Judging a steady state by combining a peak loss rate and an abrupt change detection rule, and calculating phase consistency between channels for verification; the method comprises the following steps of: splitting acquired data according to a concept entity to form a data relation model, implementing rapid global rigid estimation and applying affine transformation, estimating a pixel-level displacement field by adopting a pyramid dense optical flow network, applying the displacement field to an original pixel, and performing time domain fusion by taking optical flow confidence and a registration residual error as weights; and cutting the short-time image stabilization sequence after registration compensation, and outputting a pixel-level risk thermodynamic diagram, a candidate focus list and each output confidence interval by taking a hybrid network of a convolution front end and a space-time Transform backbone as a prediction model.

The invention discloses a thermal-mechanical-metallurgical coupling numerical simulation method for low alloy steel welding and additive manufacturing, which comprises the following steps of: S1, constructing a three-dimensional model of a test piece according to the geometric size and the size of a heat affected zone of the test piece after welding or additive manufacturing, performing grid division to form grid units, and endowing the grid units with material attributes; s2, endowing the grid units with thermophysical attributes, introducing an external heat source model, solving the change of a temperature field along with time by using a finite element method until all the grid units are cooled to a set environment temperature, and exporting a temperature field model; s3, simulating a phase change process under any thermal process; and S4, calculating a stress-strain field and a displacement field by taking the temperature field model in the step S2 and the phase fraction model in the step S3 as driving external force. The temperature, phase fraction, stress-strain and displacement changes in the low alloy steel welding and additive manufacturing process can be predicted and analyzed faster and more comprehensively, the design period is shortened, and the economical efficiency of the implementation process is promoted.

The invention relates to the technical field of defect detection, and particularly discloses a high-precision defect detection system based on image difference and threshold processing, which comprises a registration image acquisition module, a fixed mask conversion module, an image difference detection module, a correction difference graph generation module and a defect set output module, the method comprises the following steps: firstly, completing affine registration by shape features, changing a fixed mask into a dynamic mask, and performing edge robustness in the mask to obtain a preprocessed image; local movement difference is carried out on the preprocessed image and the multiple reference images, fusion of a reference domain and a time domain is carried out to generate a difference response image, and candidate areas are extracted in combination with a dynamic mask; estimating and correcting a displacement field in the candidate range, synchronously mapping to obtain a correction difference graph and a correction mask graph, and determining a peripheral rejection area; according to the method, false alarm and missing detection are effectively reduced, and the method is suitable for online high-precision detection.

The invention relates to the technical field of data processing, discloses a coupling calculation method for seepage deformation analysis of a dam body-dam foundation of a high earth and rockfill dam, and aims at solving the problem that a data acquisition method of an existing terminal device is poor in accuracy and safety. Carrying out grid division on a solid domain and a reservoir water external flow field area; performing transient calculation on the reservoir water external flow field area based on a computational fluid mechanics method, and solving an RANS equation to obtain flow field evolution data of the reservoir water external flow field area; the pressure field is applied to the surfaces of the dam body and the dam foundation, seepage-deformation coupling calculation is conducted on a solid domain based on a Biot equation in a u-p form, and displacement fields, pore pressure fields and effective stress fields of the dam body and the dam foundation are obtained; and post-processing the calculation result, and extracting response features. The accuracy of high earth and rockfill dam body-dam foundation seepage deformation analysis coupling calculation is improved, and the method is particularly suitable for high earth and rockfill dams with dam foundation defects.

This invention provides a method and system for monitoring surrounding rock deformation in underground coal mines based on three-dimensional point clouds, relating to the field of coal mine safety monitoring. The invention includes the following steps: installing a reference target in the monitoring area and collecting point cloud data; preprocessing the point cloud data, performing coarse registration using the reference target and fine registration using the iterative nearest point algorithm, and generating a triangular mesh model using the Poisson surface reconstruction algorithm; parameterizing the triangular mesh model of the monitoring area into a two-dimensional analysis mesh, calculating the three-dimensional displacement vector of each node, and establishing a displacement field matrix; calculating the displacement gradient tensor based on the displacement vector, deriving the Green-Lagrange strain tensor, and obtaining the full-field strain distribution; recording the strain value of each mesh node, and determining whether an instability warning is triggered based on the relationship between the strain value and the warning threshold; this method can achieve high-precision monitoring of surrounding rock deformation in underground coal mines.

The invention relates to the technical field of intelligent manufacturing and precision machining, and discloses a skylight guide rail clamp clamping force intelligent regulation and control method and system.The skylight guide rail clamp clamping force intelligent regulation and control method comprises the steps that multi-dimensional sensing data of a skylight guide rail clamp is obtained and preprocessed; acquiring a force displacement data pair and identifying a flexibility coefficient of the guide rail; the elasticity modulus is inversely calculated through the flexibility coefficient, and temperature correction is carried out; calculating theoretical stress distribution and a displacement field, and fusing and correcting the theoretical stress distribution and the displacement field with measured data of the sensor; evaluating stress safety, deformation stability and vibration stability; the current clamping force distribution is adjusted; the clamping force adjustment amount is calculated and converted into a control instruction for adjustment, the guide rail is machined, and quality detection is conducted; updating the quality prediction model, predicting tool wear and calculating compensation; according to the invention, through an online learning and adaptive optimization mechanism, the system can continuously learn and evolve and automatically adapt to changes of production conditions.

The invention relates to the technical field of dam safety monitoring, and discloses a multi-source remote sensing data deep learning dam slope monitoring method, which comprises the following steps: resampling each mode to a common ground grid, and calculating robust statistics and a time stability agent on grid and block scales; determining a reference image according to the block-level robust score, and adaptively setting a local displacement search range with a high-intensity centroid difference; evaluating the discrete candidate displacement in a grid neighborhood by using a median absolute difference to obtain a local displacement field and a residual error; three types of subitems are constructed based on robust noise, time stability and registration residual errors, and pixel-level reliability weights are adaptively synthesized through logarithmic variance proportions among modes; analyzing and solving local linear mapping in a neighborhood by using a weight-weighted observation matrix, and calculating a weighted residual error according to the local linear mapping; a binary and probability anomaly graph is generated with a robust threshold.

The invention provides a fire extinguisher tank bursting pressure prediction method and system. According to the method, a micro-deformation sequential sequence and a pressure gradient value of the surface of the fire extinguisher tank body under high pressure are collected, and a deformation displacement field matrix is generated through optical flow analysis so as to quantify the deformation rate; a laser holographic interferometer is deployed on a pressurization test bench, laser phase parameters are dynamically adjusted based on the deformation rate, matched holographic interference fringes are output, and a deformation evolution topological graph is constructed through fringe phase changes; identifying a deformation rate abnormal fluctuation mode in the topological graph, and associating the pressure gradient value to generate a deformation pressure association tensor; acquiring a deformation propagation path and a stress concentration trend based on the tensor, and simulating a critical fracture pressure value and a potential fracture position; and integrating results to form bursting pressure risk output. According to the invention, millisecond-level accurate prediction of the submillimeter-level fracture position is realized, the burst pressure risk assessment precision is improved, and the test cost is reduced.