549 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 provides a twin power station collision visual simulation method and system based on physical simulation, and relates to the technical field of data processing, and the method comprises the steps: obtaining a three-dimensional geometric model of a twin power station, carrying out space division, extracting geometric parameters, obtaining material data, constructing a material correlation model and a mechanical model, and carrying out fusion to obtain a physical simulation model; acquiring initial collision condition parameters as boundary conditions, performing local grid encryption, constructing a nonlinear kinetic equation of a collision process, performing time domain solution, determining equivalent stress distribution and equivalent plastic strain distribution, and generating a collision result; judging whether plastic deformation or fracture failure occurs, generating full collision process physical field data, generating a collision animation frame sequence, generating a stress distribution cloud picture and a plastic strain cloud picture, constructing a fracture special effect, and comprehensively generating a collision visual scene.

The invention relates to the technical field of detection, and discloses a polyurethane material performance detection method, which comprises the following steps: clamping a material to be detected on a loading platform, cooperatively controlling and simulating the stress effect of a target application scene, and capturing a time sequence image of the microstructure of the surface of the material by adopting a visual unit; synchronously acquiring internal strain distribution data through a distributed strain sensing array, acquiring a dynamic stress response spectrum in combination with a piezoelectric mechanical sensor, and generating a multi-modal deformation characteristic data set; performing crack density and texture orientation analysis on the time sequence image, extracting a crack growth rate and a direction consistency coefficient, and marking a strain concentration area; calculating an apparent elastic modulus attenuation rate based on the dynamic stress response spectrum, establishing a material performance degradation model, generating a dynamic flexible performance index through a weighted fusion algorithm, and performing graded evaluation on the material; material detection is upgraded from a single mechanical test to a multi-physics coupling intelligent evaluation system, and accurate and reliable detection support is provided for multiple application scenes.

The invention provides an intelligent networked automobile chassis drive-by-wire simulation method and system based on multi-domain modeling, and relates to the technical field of adaptive control, and the method comprises the steps: obtaining structural parameters and kinetic parameters of an intelligent networked automobile chassis, constructing a finite element grid, building a multi-flexible-body kinetic model corresponding to the intelligent networked automobile chassis, and constructing a multi-flexible-body dynamic model corresponding to the intelligent networked automobile chassis; introducing a coupling coordination equation, establishing a multi-flexible-body kinetic equation, and solving to obtain vibration response and stress-strain distribution under different working conditions; generating a plurality of finite element models, performing multi-physical field fusion and multi-error source fusion models, performing online error identification and active compensation, performing all-condition drive-by-wire chassis closed-loop control, and generating a drive-by-wire chassis closed-loop integrated simulation model; constructing a road three-dimensional parameterized model, generating a vehicle-road coupling model, generating a simulation test case, identifying a performance short board, constructing a knowledge graph and an optimization rule base, and generating an optimal control strategy.

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 provides a geotechnical engineering earthquake sand liquefaction discrimination method and system, and relates to the technical field of geotechnical engineering, and the method comprises the steps: collecting site basic data (a site parameter table, earthquake acceleration and pore pressure time history data); establishing a preliminary numerical model and applying boundary conditions to correct the preliminary numerical model into a complete numerical model; the pore pressure ratio and the shear strain distribution are obtained through nonlinear dynamic analysis, and the liquefaction risk is comprehensively evaluated; a key soil layer observation scheme is made according to the prediction result, field verification is carried out, and a final judgment result is obtained by combining actual measurement and numerical simulation. According to the method, numerical analysis and actual measurement data are combined, the sand liquefaction risk is rapidly and accurately evaluated, and field testing and observation are guided.

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 belongs to the field of highway disasters, and provides a traffic early warning and emergency method and system based on highway bridge disease detection, and the method comprises the steps: building a finite element model based on the structural characteristics of a highway bridge, and determining the stress-strain distribution of a key part of the bridge according to the finite element model; determining a detection point in the finite element model, and arranging a strain sensor at the corresponding detection point of the real bridge; comparing real-time data acquired by the strain sensor with the stress-strain distribution to determine sensor drift and / or sensitivity difference; correcting strain data output by the sensor according to sensor drift and / or sensitivity difference, generating corrected strain data, feeding back the corrected strain data to the finite element model, and dynamically updating the stress state of the bridge; based on the finite element model and the stress state, the health state of the bridge structure is judged, and if abnormity is found, a traffic early warning or emergency processing instruction is sent out.

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 discloses a biological material mechanical testing method and system based on finite element analysis, and is used for the technical field of data processing method.The biological material mechanical testing method comprises the following steps that a three-dimensional geometric model of a biological material is constructed, the three-dimensional geometric model of the biological material is imported into finite element analysis software, and the finite element analysis software is used for analyzing the three-dimensional geometric model of the biological material; grid division is executed, and material attributes and boundary conditions are set; finite element simulation is operated according to the set material attributes and boundary conditions, stress and strain distribution is analyzed, and grid division and the material attributes are adjusted based on the analysis result; and the adjusted grid division and material attributes are simulated again, a simulation result is verified through experimental data, and meanwhile material composition is adjusted based on the simulation result and a verification result. According to the method, each step can be completed according to preset time and quality standards, and deep understanding of the simulation process and prevention of potential problems are enhanced.

The invention provides a metal material surface microcrack positioning method. The method comprises the following steps: applying a torsional load to a numerical model, calculating stress-strain distribution in a material, describing a crystal deformation behavior by adopting a crystal plasticity constitutive equation, and obtaining dislocation density distribution and a slip system activation condition in each crystal grain; according to the dislocation density distribution and the slip system activation condition, the position where microcracks are easy to form in the material is judged, and when the dislocation density exceeds a preset critical value and multiple activation slip systems exist, the position is determined to be the position where the microcracks are easy to form; according to the fracture mechanical parameters and the fracture toughness of the material, whether the microcracks are expanded or not is judged, when the stress intensity factor exceeds the fracture toughness, it is determined that the microcracks are expanded, and the crack expansion path is determined by calculating the direction of the maximum release rate.

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 multi-scale strain measurement method for a metal material at an ultralow temperature, and the method combines a DIC technology and HR-EBSD analysis, and achieves the precise strain measurement from a macro scale to a micro scale. Macroscopic strain distribution on the surface of a sample is accurately captured through a DIC technology, and HR-EBSD analysis is utilized to further go deep into a microscopic level, so that a microscopic deformation mechanism of a material in an ultralow-temperature environment is disclosed. The overall strain of the material is researched from the macroscopic perspective, the specific region of interest in the macroscopic strain state is further deepened, the microscopic strain distribution of the region of interest is explored, and the deformation behavior and the mechanical property of the material at the ultralow temperature can be more comprehensively understood. Besides, a non-direct contact type heat exchange mode is adopted, so that accurate temperature control is realized, and meanwhile, the influence of water mist or condensation and the like generated by evaporation and condensation of a cooling medium on a test result is avoided.

The invention relates to the technical field of powder machines, in particular to an intelligent control method and system for a powder forming machine. The method comprises the steps that powder material parameters and mold parameters are obtained, the powder material parameters comprise particle size distribution, yield strength and a friction coefficient, and the mold parameters comprise mold geometric parameters; based on a discrete element method, particle dynamic analysis is conducted on the powder material parameters, particle mechanical motion characteristic data are generated, and the particle mechanical motion characteristic data comprise particle position data, speed data, contact force data and force chain distribution data constructed by the contact force data; based on a finite element method, structural mechanics analysis is conducted on the mold parameters, mold macromechanics response data are generated, and the mold macromechanics response data comprise stress distribution data, strain distribution data and displacement distribution data; and according to the particle mechanical motion characteristic data and the mold macromechanical response data, generating a powder pressing path control instruction of the mold.

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 belongs to the technical field of pavement load positioning, and provides a pavement load spatial distribution positioning method and system based on multi-source heterogeneous data fusion in order to solve the problem that the strain distribution of a pavement full-scale space cannot be comprehensively monitored at present. The method comprises the following steps: acquiring road surface laser radar data and road surface mechanical data of a set road section at each moment; fitting the ground from the road surface laser radar data and removing the ground, and based on the road surface laser radar data from which the fitting ground is removed, performing clustering identification on a vehicle type and a corresponding load so as to track a track of the vehicle; according to the method, the vehicle position and the pavement stress are decoupled from the pavement mechanical data by utilizing a finite element mechanical analysis method, and the pavement stress and load distribution diagram in the three-dimensional space is generated by combining the tracked vehicle track, so that the precise real-time identification of the pavement vehicle load space distribution is ensured.

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 field of material mechanical property testing and strain measurement, and particularly relates to a local strain measurement method and system based on a four-point bending and digital image correlation (DIC) technology, and the method specifically comprises the following steps: cutting to prepare a sample, carrying out surface electrolytic polishing, preparing speckles on the surface of the sample to be tested, and carrying out a four-point bending experiment; speckle images of the lower surface of the sample are collected, displacement correction is conducted in combination with bending deflection calculation, the bending load and the strain of the upper surface of the sample are obtained in real time, the stress-strain relation and local strain distribution of the lower surface of the sample are obtained through calculation, and finally deformation and failure mechanism analysis is conducted in combination with surface deformation and crack morphology. The method provided by the invention combines four-point bending and DIC plane strain measurement technologies, can realize stretching, compression and dynamic fatigue loading on the surface of the sample, is suitable for performance test and failure analysis of tension, compression and fatigue deformation of brittle metal materials such as intermetallic compounds and amorphous materials, and has the characteristics of convenience, rapidness and low cost.

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 spinning forming dislocation evolution prediction and process optimization method based on multi-scale modeling, which combines three calculation methods with different scales and levels, namely macroscopic finite element simulation, microcosmic crystal plasticity simulation and microcosmic dislocation dynamics simulation. A complete perspective from macroscopic mechanical behaviors to microscale mechanical response and dislocation density evolution and then to microscale single dislocation motion and interaction mechanism can be provided, so that the plastic forming process of the metal material can be understood more comprehensively. Not only are the prediction precision and reliability improved, but also the understanding depth of the material deformation behavior and the influence factors thereof is enhanced. By integrating information of different scales, key characteristics such as macroscopic stress, strain distribution, grain deformation response, dislocation density evolution and movement and interaction of a single dislocation of the material in the plastic forming process can be predicted more accurately. Meanwhile, in combination with multi-scale simulation, process parameters can be optimized to obtain better forming quality and performance.