477 results about "Strain distribution" patented technology

The invention provides a time division multiplexing multi-parameter synchronous sensing method and system based on a multi-core optical fiber. A multi-core optical fiber containing three heterogeneous fiber cores is embedded into a measured object, and a distributed reflection grating array forming an inclined angle with the axial direction is inscribed on the surface of each fiber core; energy exchange data between modes of all fiber cores and reflection grating wavelength shift signals are synchronously collected by adopting a time division multiplexing technology, phase difference signals are extracted through a cross correlation algorithm to serve as curvature reference values, dynamic compensation operation is carried out on the wavelength shift data and the phase signals, and accurate strain distribution is obtained after temperature interference is effectively eliminated; a coordinate transformation model is established by combining the three-dimensional geometric parameters of the spiral optical fiber, vector synthesis is performed on the curvature reference and the strain parameters, and accurate reconstruction of the three-dimensional deformation field of the measured object is realized. According to the technical scheme provided by the invention, through multi-core fiber multi-parameter synchronous sensing and three-dimensional coordinate mapping, temperature self-compensation high-precision three-dimensional deformation field real-time reconstruction is realized.

The invention discloses a bridge detection method and system based on a digital twin technology, and relates to the field of bridge structure health monitoring. The method comprises the following steps: acquiring a strain distribution value, a vibration spectrum value and an environmental load spectrum value in real time through a sensor network deployed in a physical bridge, generating a structural response data set, and synchronizing the structural response data set to a digital twinborn body; calculating a damage index value and an accumulated damage quantity value based on the structural response data set; inputting the damage index value and the accumulated damage quantity value into a preset safety criterion, and calculating a safety margin coefficient value and a failure risk grade value; calculating a residual life prediction value based on the safety margin coefficient value and the environmental load spectrum value, and synchronously correcting a degradation rate value of the digital twin; and generating a priority maintenance instruction according to the failure risk grade value, the residual life prediction value and the safety margin coefficient value, and feeding back maintenance effect data to the digital twinborn body to complete updating after execution. The bridge operation and maintenance efficiency and safety are remarkably improved.

The invention discloses an intelligent cable fault accurate positioning and early warning method and system, and the method comprises the steps: collecting the temperature gradient, strain distribution and partial discharge signals of the whole length of a cable in real time through a distributed optical fiber sensing network, and generating a multi-dimensional feature matrix of the operation state of the cable; based on the multi-dimensional feature matrix, outputting a preliminary fault positioning coordinate; generating corrected fault coordinates according to the topological structure data of the cable laying environment and the electromagnetic interference distribution diagram; historical fault data, real-time operation parameters and the corrected fault coordinates are fused, and a fault risk thermodynamic diagram in a future preset duration is output; and based on the fault risk thermodynamic diagram and real-time monitoring data, generating fault first-aid repair information by using a dynamic priority algorithm, synchronously triggering an early warning signal, and visually displaying a fault positioning result and a risk area in a three-dimensional geographic information system. According to the embodiment of the invention, rapid positioning, accurate early warning and intelligent disposal of the cable fault can be realized.

The invention relates to the technical field of structure health monitoring, and discloses an underground pipe gallery structure corrosion monitoring and evaluation method based on optical fiber sensing, which comprises the following steps: constructing a composite optical fiber sensing structure which comprises a Fe-C alloy coated optical fiber and a long-period grating, synchronously monitoring the chemical component change and the structural mechanical state of the corrosion product through the photoluminescence spectrum characteristic peak displacement and the strain distribution cloud picture; measuring deviation caused by coating layer loss is dynamically corrected in combination with the change rate of the environment refractive index; a reference grating is embedded to eliminate temperature drift interference, and iron ion in-situ deposition is triggered to realize sensing interface self-repairing. According to the invention, through a multi-physical-quantity cooperative sensing and self-adaptive compensation mechanism, the problems of signal interruption and the like caused by difficult decoupling of chemical-mechanical parameters and film coating failure in traditional monitoring are avoided, the long-term stable monitoring capability of a corrosion process in a complex environment is remarkably improved, and high-precision data support is provided for safety assessment of an underground pipe gallery structure.

The invention relates to the technical field of track structure health monitoring, in particular to a track crack recognition and fatigue life prediction method and system based on deep learning and finite elements, and the method comprises the steps: collecting and preprocessing a track surface crack image, and obtaining crack image data; a deep neural network model is trained, and a corresponding track crack recognition algorithm based on deep learning is determined; identifying and extracting positions and geometric dimensions of the cracks; based on the geometrical characteristics and the material attributes of the track, establishing a corresponding track finite element model, and inputting the position and the geometric size of the crack into the track finite element model to generate and obtain a track model containing crack damage; based on wheel-rail rolling contact fatigue finite element simulation, simulating the mechanical response of a track structure, determining the fatigue life of the track in combination with the stress-strain distribution of the track, and predicting the development trends of cracks under different operation conditions; automatic and efficient identification of track cracks is realized, and the accuracy of fatigue life evaluation is improved.

The invention relates to the technical field of pipeline safety monitoring, and discloses an online lossless real-time monitoring system for micro-strain of an in-service natural gas pipeline. A micro-strain data acquisition unit of the system acquires a micro-strain data set on the surface of the in-service natural gas pipeline in real time. And the three-dimensional strain field reconstruction unit receives the data set and executes three-dimensional strain field reconstruction processing to generate strain distribution characteristics of the pipeline. And the life prediction model analysis unit calls a pre-trained life prediction model to carry out nonlinear analysis processing on the strain distribution characteristics, and outputs a residual life prediction value and a key risk area identifier of the pipeline. The environmental factor compensation unit performs environmental factor compensation correction processing on the residual life prediction value to generate a corrected residual life prediction value. And the maintenance strategy generation unit generates a pipeline maintenance strategy set according to the key risk area identifier. According to the invention, real-time and accurate evaluation and intelligent maintenance decision support of the health condition of the pipeline are realized.

The invention relates to the technical field of silicon waveguide and optical fiber coupling, and particularly provides an AI-based silicon waveguide-optical fiber mode field self-alignment coupling method and system, and the method comprises the steps: monitoring the strain distribution of a chip in real time through an integrated distributed fiber bragg grating array, and obtaining the light field intensity distribution of a coupling region through cooperation with a near-field optical probe array; constructing a deep space-time convolutional network, and generating a deformation compensation vector and a waveguide reconstruction parameter by an output layer; a nanofluid channel is integrated on the side wall of the waveguide, and the refractive index of liquid is adjusted through an electrowetting effect to realize dynamic broadening of a mode field; and the electrostatic driving micro-beam embedded below the waveguide adjusts the spatial pose of the waveguide according to an instruction of an output layer of the depth space-time convolutional network. The system comprises a light field intensity acquisition module, a convolutional network construction module and a spatial pose adjustment module. According to the method, real-time analysis and prediction are performed, so that system parameters are actively adjusted, and alignment precision and stability are improved.

The invention discloses a roadway surrounding rock deformation monitoring method based on an optical fiber sensor, and the method comprises the steps: laying on the surface of roadway surrounding rock through employing a distributed optical fiber sensing technology, and obtaining original optical signal data; processing backscattered light data in the original optical signal data through a Brillouin optical time domain analysis algorithm, extracting frequency deviation of an optical signal, and obtaining strain distribution data; according to the strain distribution data, in combination with a pre-established surrounding rock mechanical model, performing initial inversion calculation by adopting a finite element method to obtain initial three-dimensional deformation distribution; on the basis of the initial three-dimensional deformation distribution, updating dynamic boundary conditions of the surrounding rock mechanical model by acquiring real-time geological parameter data to obtain an optimized mechanical model; carrying out inversion calculation on the strain distribution data again by adopting an optimized mechanical model to generate final three-dimensional deformation distribution; and based on the final three-dimensional deformation distribution, performing comparison judgment through a preset deformation threshold, determining the stability state of the surrounding rock, and generating an early warning signal.

The invention provides a cable bending fatigue life prediction method and system based on reinforcement learning, and relates to the technical field of cable life prediction, and the method comprises the steps: collecting cable test sample data, calculating a stress distribution field and a strain distribution field, constructing a stress intensity factor calculation module, and calculating a crack propagation track and rate. A damage evolution tensor is constructed as a state feature, a network is trained based on a deep Q learning algorithm, uncertainty is quantified by adopting a Markov chain Monte Carlo method, and a credible interval and an early warning threshold are established.

The present invention relates to a method and apparatus for calculating and evaluating the loss of light collection efficiency of a heliostat. 【Solution means】The present invention simulates the pulsating wind load distribution of a heliostat at different elevation-azimuth angles by CFD calculation, extracts the aerodynamic admittance function and shape parameters, constructs a pulsating wind load spectrum prediction model, calculates the pulsating wind load spectrum by combining the attitude parameters of the heliostat, applies the pulsating wind load spectrum to the structural model of the heliostat mirror by finite element analysis, obtains the instantaneous dynamic response, displacement and strain distribution characteristics of the heliostat, simulates the influence of mirror deformation on the light reflection path, quantifies the instantaneous influence on the light collection efficiency of the deformed mirror, and generates a dynamic curve of the change of light collection efficiency over time.

The invention relates to the technical field of hydraulic test of nickel alloy pipes, and discloses a hydraulic test system for a thin-wall nickel alloy pipe. According to the system, in the axial loading state of the thin-wall nickel alloy pipe, a closed water pressure cavity is established through an annular sealing clamp, and then strain distribution data on the surface of the pipe body is collected through a distributed optical fiber sensor array. And constructing a three-dimensional stress field reconstruction model based on the acquired strain distribution data, calculating an equivalent plastic deformation threshold value of the pipe body under different water pressure gradients, adjusting a water pressure loading curve slope parameter according to the equivalent plastic deformation threshold value, and generating a dynamic water pressure loading instruction. And the high-frequency pressure pulse generator executes the instruction, synchronously triggers the X-ray diffractometer to collect lattice distortion data of the tube body, inputs the data into the material microscopic damage evolution model, and outputs a microscopic defect spatial distribution map. And finally, material constitutive parameters of the three-dimensional stress field reconstruction model are corrected based on the atlas, closed-loop feedback control is formed, and multi-dimensional data acquisition and accurate management and control of the thin-wall nickel alloy pipe hydrostatic test are achieved.

The invention discloses a secant pile nondestructive testing system and method based on multi-source sensing data fusion, and belongs to the technical field of civil engineering detection, the full pile long strain is axially monitored through an optical fiber, three-dimensional imaging and stress wave global scanning of a secant interface are realized through a sound wave transmitting-receiving probe pair, line-plane-body multi-level detection is formed, and the secant pile nondestructive testing system and the secant pile nondestructive testing method based on multi-source sensing data fusion are obtained. Through cooperative complementation of strain / temperature data, stress wave data and wave velocity three-mode data, defect type probabilistic recognition is achieved, the weight of each sensor is dynamically adjusted according to the real-time environment, and the defect comprehensive probability is calculated and output. And dynamic weight distribution is realized. Through multi-source sensing data fusion analysis, pile body integrity, occlusion interface compactness and temperature strain distribution characteristics can be accurately identified, and the method has the characteristics of multiple detection dimensions, high precision and strong interference resistance.

To provide a method for predicting a strain distribution map and determining damage based on deep learning.SOLUTION: The present invention comprises: a step S1 of establishing an image dataset of finite element analysis results for strain distribution map prediction; a step S2 of building a deep learning model for strain distribution map prediction based on a DeepLabv3+ network and performing learning and validation; a step S3 of establishing a dataset for damage determination of a structural analysis model and performing preliminary processing and data enhancement operations; a step S4 of building a binary classification deep learning model for damage determination based on a convolutional neural network and performing learning and validation for transition learning; and a step S5 of performing an interpretability analysis on the learned binary classification structural analysis model and outputting a region in an image that more contributes to classification.SELECTED DRAWING: Figure 1

The invention relates to the technical field of bridge engineering detection, in particular to a distributed optical fiber strain sensing detection system for a bridge engineering dynamic and static load test, and the system comprises a distributed optical fiber strain sensing module which is used for obtaining the strain information of different positions of a bridge; the dynamic and static load signal acquisition and conversion module is used for converting the strain signal into a strain electric signal suitable for transmission and processing; the temperature compensation module is used for carrying out temperature compensation on the strain signal according to the change of the collected environment temperature; the data processing and analyzing module is used for processing and analyzing the strain electric signal subjected to temperature compensation so as to obtain the strain distribution condition and related parameters of the bridge structure under the action of dynamic and static loads; and the monitoring and early warning module is used for monitoring the state of the bridge in real time according to the result obtained by the data processing and analyzing module and sending out an early warning signal when the strain of the bridge is abnormal. The problem that in the prior art, comprehensive, high-precision and real-time dynamic and static load strain monitoring cannot be carried out on bridge engineering is solved.

The invention belongs to the technical field of fatigue testing, and discloses a testing machine phase difference optimization method and system and a rubber product fatigue testing machine. The method comprises the steps of presetting a loading path signal, collecting sensor data, and performing feature extraction on the sensor data to obtain phase difference features; taking the parameters of the test piece and the phase difference characteristics as input of a dual-channel lightweight model, and obtaining a phase difference predicted value of each channel; based on a multi-target dynamic parameter optimization algorithm, control parameters of the test machine are optimized and adjusted in combination with the phase difference predicted value and the inter-channel coupling characteristics; the strain distribution of the test piece is monitored in real time, and when the strain unevenness is larger than a preset strain threshold value, channel load redistribution is triggered, and the phase difference is optimized again; according to the invention, test piece characteristics and loading path changes can be sensed in real time, the phase difference prediction error is reduced, the test efficiency is improved, the energy consumption is reduced, and the authenticity of multi-axis load simulation, the phase control precision and the comprehensive performance of the system are remarkably improved.

The invention discloses a welding deformation prediction system based on simulation analysis, and relates to the technical field of welding deformation prediction.The welding deformation prediction system comprises the steps that a micro-area unit grid is constructed by obtaining a geometric model of a to-be-welded structure, material heterogeneous distribution and welding path information; based on the grid and material characteristics, establishing a local nonlinear thermal-mechanical coupling simulation model; inputting heat source parameters, calculating an instantaneous temperature field, and constructing a stress evolution function in combination with path change; further deducing initial strain distribution, and generating a local deformation estimation map; error inversion is executed by comparing with historical experimental data, heat source and material parameters are corrected, and a simulation model is updated; and finally, outputting a welding deformation prediction map, and identifying a deformation sensitive region in the microstructure. The method has the high-precision prediction capability and the self-adaptive correction characteristic, and is suitable for deformation control and process optimization of the micro-scale welding structure.

The invention belongs to the technical field of road engineering detection, and discloses a geological radar and distributed optical fiber roadbed compaction detection method and device, and the method comprises the steps: obtaining a dielectric constant profile detected by a radar and the initial Brillouin scattering light frequency offset of optical fiber strain; establishing an initial reference state including optical fiber strain distribution and a radar dielectric constant profile; comparing the Brillouin scattering light frequency offset and the radar dielectric constant profile image which are acquired in real time with an initial reference state, determining a strain gradient abrupt change region, and correcting a preset optical fiber temperature drift compensation model by using a radar scanning result; and performing temperature drift component removal on the Brillouin scattering light frequency offset obtained in real time to obtain pure strain frequency shift data, calculating according to the frequency shift data to obtain accumulative settlement, and generating a roadbed compaction effect detection result. Continuous longitudinal strain monitoring is provided through the geological radar and the sensing optical fiber, and an abnormal section is accurately positioned.

The invention relates to the technical field of strength detection, in particular to a tunnel freezing pipe structure integrity detection method and system.The method comprises the following steps that stress values and strain values under the differential temperature gradient and pressure distribution condition are analyzed based on the diameter, wall thickness, length and elastic data of a tunnel freezing pipe, and mechanical parameters of key nodes and connecting parts are identified; and analyzing a local stress concentration area and crack propagation, and generating stress and strain distribution prediction values. According to the method, the detection precision is improved through multi-dimensional parameter analysis and refined prediction, mechanical parameters and crack propagation areas of key parts are accurately identified, local stress concentration and potential damage are defined, efficient path planning and equipment frequency distribution are achieved by combining damage path sorting and real-time data dynamic adjustment, continuous monitoring of the key areas is ensured, and the accuracy of the detection is improved. By optimizing the damage index and evaluating the structure risk in real time, the overall state and local risk of the freezing pipe are comprehensively controlled, and the detection scientificity and reliability are improved.

The invention discloses a distributed OFDR sensing signal demodulation method and system based on local ridge line tracking, relates to the technical field of optical fiber sensing, and is used for solving the problem that a traditional cross-correlation algorithm is difficult to consider robustness and real-time performance at the same time. The method comprises the following steps: respectively processing a reference beat frequency signal of a sensing optical fiber to be measured in a strain-free state and a measurement beat frequency signal of the sensing optical fiber to be measured in a strain state to obtain a reference complex Rayleigh scattering signal and a measurement complex Rayleigh scattering signal containing a plurality of sensing positions along the optical fiber; calculating a cross-correlation function based on the two signals; stacking the cross-correlation functions corresponding to all sensing positions along a distance domain, and constructing a two-dimensional cross-correlation thermodynamic diagram matrix; performing filtering processing and weighting processing on the two-dimensional cross-correlation thermodynamic diagram matrix to obtain an optimal frequency drift distance; and demodulating based on the optimal frequency drift amount to obtain strain distributed along the to-be-measured sensing optical fiber. According to the invention, high-robustness and rapid demodulation of the signal in an application scene with extremely high real-time requirement can be realized.

The invention discloses a multispectral image and LiDAR fusion method suitable for dam body deformation monitoring, and belongs to the field of hydraulic engineering safety monitoring. According to the method, a dam body surface multispectral image and three-dimensional point cloud data are obtained through an unmanned aerial vehicle, and crack spectral features and structural joint geometric features are extracted after processing; constructing a CNN and finite element coupling model, inputting the surface displacement field after feature fusion into the finite element model, and simulating internal stress-strain distribution in combination with material parameters; and analyzing the space-time correlation between the surface displacement and the internal stress, optimizing parameters by combining measured data, and outputting a deformation correlation result. According to the invention, all-directional correlation monitoring of the surface and internal deformation of the dam body is realized, the limitation of a single data source is overcome, the monitoring precision and reliability are improved, and support is provided for safety assessment of the dam body.

The invention belongs to the technical field of tire engineering design, and discloses an engineering tire belted layer end structure finite element design analysis method and system. Comprising the steps of obtaining and preprocessing tire carcass contour data and initial belted layer parameters, conducting belted layer end parametric modeling on the basis of a standardized tire geometric model, constructing a durability finite element analysis model to obtain a stress-strain distribution field, conducting multi-parameter sensitivity analysis to determine a key influence factor weight matrix, and obtaining a stress-strain distribution field. And optimally designing a belted layer end structure based on the matrix, and finally performing durability verification and evaluation. According to the method, through accurate geometric expression, flexible parametric modeling, accurate performance analysis and multi-objective optimization design, belted layer end stress distribution uniformization and structural rigidity optimization are achieved.

The invention discloses an accurate monitoring system for linear deformation of a loess tunnel portal side upward slope, and relates to the technical field of geotechnical engineering and surveying and mapping. The system comprises a sensing network layer, a data acquisition and transmission layer, a cloud platform and data analysis layer and an application display layer, displacement data of discrete monitoring points are reconstructed into continuous full-field displacement and strain distribution through Kriging interpolation, then a displacement vector field is regarded as a whole to be analyzed, the maximum shear strain field of the displacement vector field is calculated, an abnormal area with the deformation gradient abrupt change is accurately positioned, and the deformation gradient abrupt change accuracy is improved. The abnormal point sets are merged into independent clusters according to spatial proximity and deformation consistency, and the geometric trajectory of a potential slip crack surface or crack is accurately outlined through curve fitting, so that the technical path fundamentally changes the limitation of point-mode monitoring, and can automatically identify a hidden linear failure trend which is difficult to find by naked eyes in a non-contact manner, so as to improve the detection accuracy of the hidden linear failure trend. And refined data support is provided for slope stability evaluation.

The invention relates to the technical field of deep and far sea structure monitoring, and discloses an intelligent monitoring system and method for a deep and far sea floating structure based on an optical fiber sensing technology, and the system comprises a data collection and processing module which employs an optical fiber sensor to collect and align a strain signal, a vibration signal and a temperature signal of the deep and far sea floating structure, obtaining a strain distribution curve, vibration spectrum characteristics and temperature field mapping; the intelligent monitoring module is used for generating an abnormal positioning result, a damage trend model and noise compensation data; the health diagnosis module outputs a diagnosis report; and the remote communication module is used for starting real-time total transmission of the low-orbit satellite communication terminal when the risk level reaches the target level, otherwise, maintaining incremental compression transmission of the ad hoc network buoy relay unit. The problems that a traditional monitoring mode is low in efficiency and poor in real-time performance in a deep and far sea environment, conventional electronic monitoring equipment is prone to faults, and data transmission is unstable can be solved, the intelligent level and reliability of monitoring are improved, and the safety monitoring requirement is met.

The invention relates to the technical field of aluminum alloy drill rods, in particular to an aluminum alloy drill rod body thread high-temperature fatigue life prediction method, which comprises the following steps: establishing a drill string dynamic model to obtain load spectrum initial data; establishing an aluminum alloy drill rod body thread thermal-mechanical coupling finite element model to obtain initial stress-strain distribution; performing a high-temperature fatigue test to obtain fatigue life data of the aluminum alloy material at different temperatures; establishing a high-temperature fatigue damage model; and constructing a high-temperature fatigue life prediction model based on the load spectrum initial data, the initial stress-strain distribution, the fatigue life data and the high-temperature fatigue damage model. Therefore, the problems that the fatigue failure mechanism of the aluminum alloy drill rod body thread in the high-temperature environment is not explored, the coupling effect of creep damage and fatigue damage is not fully considered, the influence rule of vibration load characteristics on the fatigue life and the failure mode is not clear, and engineering practice cannot be accurately guided are solved.

The invention discloses a BIM-based cable construction method. The method comprises the steps of obtaining BIM model data including a cable design path, a design bending radius and design stress; in the cable laying process, the optical fiber sensors are tightly attached and fixed to the surface of an outer sheath of the cable to be laid, and the optical fiber sensors are continuously distributed in the length direction of the cable; acquiring actual strain distribution data and actual bending radius distribution data of the cable measured by the optical fiber sensor in real time, and performing spatial position correlation comparison with design stress data and design bending radius data in the BIM model; if the actual strain value of any position point exceeds the strain threshold range corresponding to the design stress or the actual bending radius value is smaller than the design bending radius value, it is judged that construction deviation exists in the position point; and generating an adjustment instruction for subsequent cable laying operation based on the position point information with the construction deviation. The cable laying precision and the construction safety can be effectively improved.

The invention provides an anti-freezing optical cable freezing expansion pressure detection analysis method and system, and relates to the field of optical cable freezing prevention. The method comprises the steps of analyzing strain and deformation of a protection steel pipe and an optical cable in the protection steel pipe according to an optical cable structure and a protection steel pipe structure, and constructing a strain and deformation calculation model; through basic parameters of the optical cable and the protection steel pipe, calculating a cross sectional area variation by using a strain and deformation calculation model; constructing an optical cable pressure calculation model according to the relationship between the cross sectional area variations; analyzing the pressure caused by the icing layer in the protection steel pipe and a frost heaving strain theory, and constructing a frost heaving strain distribution model under the continuous icing layer condition; and according to the constructed optical cable pressure calculation model and the frost heaving strain distribution model under the continuous icing layer condition, frost heaving pressure calculation and related factor analysis of the internal material of the protection steel pipe are carried out. According to the method, influence factors of optical cable freezing damage are deeply analyzed, and guidance is provided for improving the anti-freezing design of the directly-buried optical cable.

The invention relates to the technical field of aluminum alloy forging, in particular to a forging process capable of reducing reverse forging coarse grains of 6061 aluminum alloy, which comprises the following steps: acquiring a 6061 aluminum alloy ingot, homogenizing and annealing the 6061 aluminum alloy ingot at 480-520 DEG C for 12-16 hours, cooling to room temperature by water, performing ultrasonic flaw detection, performing turning surface treatment, and alternately forging and pressing in three directions, thereby obtaining the 6061 aluminum alloy. The method comprises the following steps: placing a fine-grain 6061 aluminum alloy forge piece in a graded heating furnace, completing sub-dynamic recrystallization, applying directional pressure stress of 50-80MPa to the 6061 aluminum alloy forge piece, analyzing grain boundary characteristic distribution by adopting an electron back scattering diffraction technology, and obtaining the grain boundary optimized 6061 aluminum alloy forge piece with a discontinuous coarse-grain structure through laser shock strengthening and stress relief annealing. According to the method, the problems that reverse forging coarse grains are caused by uneven strain distribution during forging and pressing, and fine grains and high-density nanoprecipitation cannot be effectively obtained and a grain boundary structure cannot be optimized due to the fact that follow-up forging, heat treatment and stress treatment parameters are difficult to accurately control can be solved.

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 discloses a structure simulation optimization method for handbag accessories. The method comprises the following steps: determining an optimization target and setting a load condition and a boundary condition; creating a three-dimensional geometric model of the handbag accessory; performing mesh generation on the geometric model to generate a finite element mesh model; defining the material attribute of each part of the handbag accessory; boundary conditions and loads are applied, and physical constraint and stress conditions of the handbag accessories in the actual use process are simulated; carrying out simulation calculation by using finite element analysis software to obtain deformation, stress and strain distribution of the handbag accessory under the action of a load; performing structure optimization on the handbag accessories according to a simulation calculation result; according to the structure simulation optimization method for the handbag accessories, the problems of precision, efficiency and cost in the design of the handbag accessories are solved, the intelligent level of the design process is improved, and the quality and performance of products are ensured.

The invention discloses a physically and data-driven composite material multi-parameter inversion identification method, which comprises the following steps of: performing non-contact full-field deformation measurement on a composite material test piece to obtain displacement distribution data; performing numerical differentiation on the displacement distribution data through a spatial difference method to obtain strain distribution data of the composite material test piece, and performing gridding processing on the strain distribution data of the surface of the composite material test piece; constructing a neural network model; constructing a multi-objective loss function; composite material parameters are preset, the composite material parameters are embedded into the neural network model to participate in gradient operation, and the gradient of a loss function is obtained; and carrying out training optimization on the gradient of the loss function, adjusting the weights of the control item loss function, the boundary item loss function, the constitutive relation item loss function and the data item loss function, optimizing the hyper-parameters of the neural network model until a preset convergence condition is met, and obtaining the target parameters of the neural network model. The defect that the constitutive parameters of the thick-section composite material are obtained through an experimental method is effectively overcome.