1504 results about "Stress distribution" patented technology

The invention discloses a wind power bolt state detection method and system, and belongs to the technical field of mechanical structure testing and health monitoring, and the method comprises the steps: obtaining the real-time strain data of each monitoring point; acquiring operation condition data of the unit in real time; dynamically generating a finite element model of the tower drum structure according to the operation condition data; the finite element model and the real-time strain data are combined to generate a stress distribution map of the whole tower cylinder bolts; inputting the stress distribution atlas into a preset fatigue damage model, calculating a bolt life attenuation rate and positioning an external load abnormal source; and when it is detected that the life attenuation rate exceeds a preset threshold value, generating a grading early warning signal and outputting a maintenance strategy according to a positioning result. According to the method, a wireless strain sensor network, dynamic finite element model correction and multi-dimensional fatigue damage analysis technology are adopted, a whole tower barrel stress distribution map is generated in real time, external working condition data are associated, the bolt service life attenuation rate can be accurately evaluated, a load abnormal source can be positioned, and a graded maintenance strategy can be output.

The invention provides a BIM-based fabricated building component management method and system, and the method comprises the steps: firstly obtaining a building information model data set of a target fabricated building project, a component attribute parameter set covering a prefabricated component unit, and a component geometric feature set, and then carrying out the multi-level component feature analysis of the building information model data set, obtaining a standardized geometric feature sequence and a dynamic construction state feature set containing a transportation positioning offset, a hoisting stress distribution gradient and an installation collision risk coefficient, and then performing cross-stage collaborative analysis on the features based on a preset construction stage matching strategy to generate a component matching strategy set; and the component dynamic optimization module is used for indicating component installation sequence adjustment and connection node precision compensation, finally generating a component dynamic optimization instruction set according to the component matching strategy set, and synchronizing the component dynamic optimization instruction set to the building information model data set to update component attributes, so that efficient management of the fabricated building components is realized.

Disclosed in the present invention is a foundation deformation calculation method based on numerical model stress extraction. First, a numerical model is established to calculate the vertical additional stress distribution and horizontal additional stress distribution of foundation soil, wherein the elastic modulus of the foundation soil is selected within a range of two to five times the compression modulus on the basis of engineering experience; a layer-wise summation method is used, in which layered settlement is directly calculated for a natural foundation, settlement in a reinforced zone and settlement in an underlying layer are both calculated for a composite foundation, and layered settlement amounts are superimposed and multiplied by a settlement empirical correction coefficient to obtain a final settlement amount; the projection of a horizontal displacement calculation point on the ground and a vertical soil column below the horizontal displacement calculation point are taken as a vertically placed finite elastic long beam, the cross section of the soil column is square, the side length of the soil column is preferably 0.1 m or below, the modulus of the beam is the same as that of the soil, one end is located in a ground surface plane, and the other end is located in a plane where a calculation depth is located; and a distributed load is dispersed into several concentrated loads, a foundation bed coefficient is determined on the basis of a specification, and the horizontal displacement of the calculation point is obtained by means of a beam-on-elastic-foundation method.

The invention relates to the technical field of welding deformation control, and discloses a multi-stage adjusting method and system for steel structure welding deformation control. The method comprises the steps that steel member parameters are measured, a welding deformation prediction model is established, and pre-deformation data are obtained; performing accurate pre-deformation processing according to the data; executing segmented welding and recording a temperature field and a deformation amount; performing local heat treatment on the high-stress area to obtain stress distribution; correcting a deformation area by using multi-point force; and performing precision detection and quality evaluation according to the corrected data to form a control file. According to the method, high-precision control over welding deformation of the complex steel structure is achieved through data transmission and feedback adjustment of all stages.

The invention discloses an appearance visual detection method and device for an electric control part of a new energy automobile. Structural stress distribution data of the electric control component are obtained and subjected to partition processing, and the surface of the electric control component is divided into a stress sensitive area and a conventional area; carrying out multi-wavelength spectrum scanning and polarization state detection on the stress sensitive area by adopting self-adaptive path planning, synchronously acquiring a polarization characteristic matrix, dual-wavelength interference data and depth spectrum data of the determined key attention area, and carrying out time domain synchronization and space registration processing to generate multi-dimensional detection characteristic data; extracting an optical anisotropy coefficient, a microcosmic deformation tensor and an interface reflection feature vector, and determining layering defect feature parameters in combination with multiple threshold judgment; and calculating local curvature distribution, an interface shear stress field and thermal stress distribution, and performing quantitative matching with the material characteristic parameters to generate quantitative evaluation indexes of the layering defects. According to the invention, accurate identification and rapid positioning of microscopic layering defects in the multi-layer composite insulation structure of the electric control component are realized.

The invention relates to the technical field of bridge digital twinborn platform construction, and particularly discloses a Unity 3D bridge digital twinborn platform construction method integrated with a Midaas-Civil calculation function, and the method carries out the real-time monitoring and evaluation of a bridge through a digital twinborn technology, thereby improving the structural safety and maintenance efficiency of the bridge. The method comprises the following steps: acquiring stress distribution data of a bridge in real time through bridge monitoring equipment, and analyzing a stress concentration area in a digital twin platform; a crack propagation rate and a stress gradient change value are extracted, a comprehensive feature vector is constructed, the current state of the bridge is evaluated by using a machine learning model, and a high-risk area is identified according to an evaluation result; the maintenance strategy of the bridge is dynamically optimized by predicting the future crack expansion trend and stress distribution change of the low-risk area; and on the basis of a risk assessment result, a resource optimization allocation model is constructed, optimal allocation and dynamic adjustment of bridge maintenance resources are realized, and accurate implementation of a maintenance strategy is ensured.

The invention relates to the technical field of pipeline stress fatigue detection, and discloses a natural gas station elbow tee joint stress fatigue digital intelligent analysis method and system and a product. The method comprises the following steps: establishing a multi-physics field coupling digital twinborn model of the elbow tee joint, integrating a geometric structure, material attributes and a fluid-solid coupling mechanism, and simulating flow-induced vibration stress and corrosion fatigue interaction; collecting real-time multi-source data of the elbow tee joint; real-time multi-source data is utilized to update boundary conditions and parameters of the digital twin model, and material constants are dynamically corrected through machine learning, so that self-calibration of the model is realized; performing stress distribution analysis based on the updated model to obtain stress field data, and performing fatigue crack propagation prediction in combination with real-time multi-source data to generate a prediction result; and based on the prediction result, evaluating fatigue failure risks, including crack growth rate, residual life evaluation and failure probability, and outputting alarm information or optimization decision information.

The invention relates to the technical field of construction risk identification, and discloses a bridge construction hazard source identification and risk analysis method and system. The method comprises the following steps: collecting geological, adjacent-channel and adjacent-river and support load parameters to establish a danger information base; performing three-dimensional mapping to generate an associated risk matrix; extracting key node data to form a stress distribution diagram; fusing the process progress to construct a safety margin curve; calculating a critical point to generate an early warning threshold table; and quantitatively verifying the prevention and control effect to form a closed-loop optimization strategy chain. According to the method, the bracket-load-environment three-dimensional association risk matrix and the dynamic safety margin index curve are established, and the risk dynamic identification, accurate early warning and closed-loop optimization of the whole construction process are realized, so that the safety management level of bridge bracket cast-in-place construction is improved, and the probability of construction safety accidents is reduced.

The invention discloses a powder metallurgy gear hardness detection method and system, and belongs to the technical field of metallurgy gears, and the method comprises the steps: obtaining a to-be-detected gear three-dimensional model, and dividing a hardness evaluation region; a thermal response image is collected, a thermal response time gradient parameter TRTG is extracted, and a thermal diffusion characteristic matrix T is formed; establishing a hardness initial model H1 based on the TRTG and compactness; constructing a finite element stress field model, extracting a stress distribution modulation coefficient SDMC, and forming a stress characteristic matrix S; performing weighted correction on H1 based on T and S, and establishing a comprehensive hardness prediction model H2; outputting a hardness prediction result of the whole gear and the key area, and identifying a potential weak area; according to the method, thermal response and stress modulation parameters are fused, non-destructive and high-resolution hardness distribution prediction is achieved, and the method is suitable for quality evaluation and failure early warning of the powder metallurgy gear of a complex structure.

The invention relates to the technical field of building management, in particular to a BIM-based building construction management method and system and a medium. The method comprises the following steps: acquiring building structure data; a tower crane full-life-cycle BIM model is constructed based on the building structure data, wherein the tower crane full-life-cycle BIM model comprises three-dimensional parameterized information of a base embedded part, a tower body standard knot, a slewing mechanism and a suspension arm assembly; acquiring building construction environment data; carrying out tower crane installation optimization on the tower crane full-life-cycle BIM model by utilizing building construction environment data to obtain a tower crane installation scheme; and base stress distribution simulation is conducted on the tower crane full-life-cycle BIM model according to the tower crane installation scheme, and tower crane base stress distribution data are generated. According to the invention, by integrating the building structure and the construction environment data and combining real-time sensing monitoring and intelligent collaborative optimization, the accuracy and dispatchability of tower crane management in building construction are improved.

The invention relates to the technical field of bridge engineering, in particular to a system and method for predicting the service life of a steel structure bridge after fatigue crack repair. The method comprises the steps that load data and stress data in the operation process of a bridge structure after fatigue crack repairing are collected; dynamic response characteristic parameters are extracted in combination with stress distribution characteristics of the fatigue crack repair area; performing multi-dimensional data fusion and preprocessing on the dynamic response characteristic parameters, and constructing a data set for training a deep learning algorithm; combining a deep learning algorithm and a fracture mechanics theory to construct a crack propagation rate prediction model and a residual life prediction model; according to the operation state of the bridge and the performance degradation law of the repairing area, the remaining life of the repaired structure is predicted, and multi-stage early warning is conducted. According to the method provided by the invention, bridge structure performance evaluation, life prediction and potential risk early warning can be realized, and the safety and reliability of bridge operation are ensured.

The invention provides an aviation fastener service life prediction method and system based on stress analysis, and the method comprises the steps: collecting a dynamic stress data set of a target fastener in a service environment, carrying out the three-dimensional stress field reconstruction of the dynamic stress data, and obtaining a three-dimensional stress field model; the method comprises the following steps: generating stress distribution characteristics including an axial stress gradient, a radial stress cumulant and a tangential stress fluctuation coefficient, and then calling a pre-trained life prediction model to carry out nonlinear degradation analysis on the stress distribution characteristics to obtain a residual life prediction value and a key failure area identifier of a target fastener. Further, on the basis of the incidence relation between the temperature fluctuation sequence and the material thermal expansion coefficient, service environment compensation correction is conducted on the residual life prediction value, and finally, a fastener maintenance strategy set containing a stress release path adjustment scheme and a surface strengthening treatment scheme is generated according to the key failure area identifier; and precise prediction and effective maintenance of the service life of the aviation fastener are realized.

The invention provides a big data intelligent decision analysis method and system. The method comprises the following steps: acquiring vibration data and mechanical node stress distribution data in power equipment transportation, and extracting high-frequency harmonic distortion characteristics aligned with a vibration data timestamp; performing cross-domain correlation analysis on stress data fluctuation and harmonic characteristic dynamic change, and identifying a symbiotic mode of mechanical stress abnormality and electrical harmonic distortion by using a machine learning model; based on logistics path planning and equipment constraints, a cooperative control instruction for adjusting the transportation path and the power supply loop parameters is generated through a multi-target cooperative optimization algorithm; and constructing a joint risk assessment matrix, and dynamically adjusting the cooperative relationship between the transportation path and the harmonic suppression parameter according to the quantitative analysis result and the cooperative control instruction to realize the closed-loop decision of the power equipment. According to the technical scheme provided by the invention, the power equipment transportation data processing efficiency is remarkably improved.

The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent defect detecting and repairing method and system for a vacuum isothermal forging based on multi-mode sensing data. The method comprises the following steps: respectively acquiring image data, heat distribution data and stress monitoring data of the vacuum isothermal forging through a visual sensor, a thermal sensor and a stress sensor, and preprocessing the data to generate a time-space aligned multi-modal data set; performing fusion processing on the multi-modal data set, extracting multi-dimensional features of defects through a deep learning network, and generating a defect feature sequence; optimizing a repair path planning model based on the defect feature sequence in combination with the stress distribution features, and generating a repair task sequence; and inputting the repairing task sequence into the intelligent repairing equipment to execute repairing operation. According to the method, the multi-mode sensing technology and the deep learning algorithm are combined, high-precision detection, classification and repair of the defects of the vacuum isothermal forging are achieved, the production efficiency and the product quality are remarkably improved, and the defective rate and the repair cost are reduced.

The invention provides an on-line monitoring method and system for crack propagation of silicon-based new material equipment in a high-temperature environment, and relates to the technical field of crack detection, and the method comprises the steps: collecting stress distribution data through arranging a stress sensor, and generating a stress field distribution diagram by using an attention mechanism deep neural network model; identifying a stress concentration region based on a region growing algorithm, when a stress value exceeds a preset threshold value, acquiring temperature distribution data, extracting temperature distribution characteristics through a deep mixed probability model, performing multi-modal data fusion in combination with a stress field distribution diagram, and establishing a crack propagation prediction model by using a graph structure neural network. The crack propagation rate and direction are solved through a swarm intelligence optimization algorithm, when the crack propagation rate exceeds a preset threshold value, a control system adjusts equipment operation parameters, the sampling frequency of a sensor is adjusted according to the crack propagation direction, and model parameters are updated through an incremental learning method to achieve real-time monitoring.

The invention provides a cold-rolled sheet shape multivariable cooperative control optimization method and system. The method comprises the steps that rolling tension set value parameters are determined on the basis of a dynamic coupling model so as to be matched with dynamic tension fluctuation in the high-speed strip steel rolling process of a cold-rolled sheet, the magnetic conductivity change in the strip steel rolling process is detected through the magnetoelastic effect, and a magnetic conductivity gradient map is generated; residual stress distribution data are inverted based on the magnetic conductivity gradient map, and target axial roll shifting control parameters influencing the roll gap shape are generated; and multivariable cooperative matching is conducted on the rolling tension set value parameter and the target axial roll shifting control parameter, a matching result is obtained, the rolling tension and the roll gap shape are adjusted, and strip steel deviation and fluctuation caused by dynamic coupling of the composite material tension and the cold-rolled sheet shape in the cold-rolled sheet high-speed strip steel rolling process are restrained. According to the technical scheme, through cooperative control of the dynamic coupling model and the magnetic conductivity gradient map, deviation and fluctuation of the strip steel are effectively restrained, and the shape stability and rolling precision of the cold-rolled plate are improved.

The invention discloses a stress analysis method for bridge incremental launching construction, and particularly relates to the field of stress analysis. According to the method, a mechanical state matrix is generated by combining wavelet noise reduction and Kalman filtering through distributed optical fibers, a piezoelectric force sensor, a vibrating wire stress meter and other equipment and real-time data; a pushing system-temporary support-beam body coupling dynamic mechanical model is established, and a generalized alpha method is adopted for solving and Bayesian optimization calibration parameters are adopted; key parameters are identified on line through a three-level progressive framework, boundary conditions are dynamically updated in combination with a total station and a laser tracker, and supporting rigidity self-adaptive adjustment is achieved through fuzzy PID control. Model confidence evaluation adopts a residual norm-modal confidence criterion-Bayesian update three-level mechanism, finally, stress distribution, support reaction and displacement deviation are calculated based on a correction model, and a safety index, threshold alarm and risk analysis report is generated through a fuzzy comprehensive evaluation method and fed back to a construction control center in real time.

The invention discloses a road protection multi-scene coupling simulation method and system based on digital twinning, and relates to the field of electric digital data processing, and the method comprises the steps: collecting dynamic response data (strain, acceleration and displacement) of a traffic protection facility through multiple sensors, and building a scene feature library containing vehicle collision, wind load and seismic waves; and projecting response data to a unified time axis, identifying a multi-scene overlapping interval, and determining an action sequence and duration. Primary and secondary scenes are judged according to scene action parameters, and mechanical correction values and standard response parameters are extracted. And establishing a digital twinborn model for coupling simulation to obtain a stress distribution condition, evaluating a structural damage level and generating a safety performance report. By implementing the method, the deviation between the mechanical property simulation result of the protection facility and the actual working condition can be reduced.

The invention relates to the technical field of semiconductor device testing, in particular to a SiC MOSFET power cycle testing method which comprises the following steps: S1, building a composite environment testing platform, configuring dynamic testing parameters, automatically calculating a physical boundary and collecting sensor data in real time; s2, establishing a finite element simulation model based on the physical boundary and sensor data, and outputting optimized dynamic test parameters and a simulated stress distribution diagram; s3, synchronously applying composite stress according to the dynamic test parameters and the stress distribution diagram, and collecting multi-dimensional test data in real time; when the system is used, through dynamic boundary calculation and real-time data acquisition, the intelligent degree and reliability of the test are improved, the test period is shortened, the failure prediction accuracy is improved, the system is suitable for reliability evaluation of SiC MOSFET devices in the fields of new energy, aerospace and the like, a large number of physical tests are avoided through virtual simulation, and the reliability of the SiC MOSFET devices is improved. And reduction of device loss and resource waste is facilitated.

The invention relates to the technical field of data processing, and discloses a stress monitoring and early warning method and system in a net rack dismantling process. The method comprises the following steps: scanning a net rack to obtain geometric data, and constructing a three-dimensional model; establishing a finite element model based on the three-dimensional model to analyze stress distribution; key point installation sensors are determined; converting the collected data into a stress value, a displacement value and a support reaction value; constructing a third-level early warning threshold model; and when the monitoring value exceeds the threshold value, early warning is triggered and emergency disposal is executed. Accurate monitoring, real-time evaluation and graded early warning of the whole dismantling process are achieved, and the safety and controllability of dismantling operation are remarkably improved.

The invention provides a macroscopic finite element simulation analysis method and device for a sealing element considering plastic deformation, and belongs to the field of sealing element design. The method comprises the following steps: acquiring an engineering stress-strain curve of a plastic material in a compression state; converting the engineering stress-strain curve into a real stress-strain curve; determining a yield limit, and taking a strain point corresponding to the yield limit as a demarcation point of elasticity and plastic deformation; an elastic section of a real stress-strain curve is extracted, the elastic modulus is calculated, and the Poisson's ratio is obtained through axial and transverse strain measurement in a uniaxial compression test; a true stress-strain curve plastic section is extracted, and a multi-linear isotropic hardening model is established; constructing a complete elastic-plastic constitutive model covering the elastic deformation area and the plastic deformation area; the stress distribution, the strain response and the contact performance of the plastic sealing element in a compression deformation state are simulated. According to the method and the device provided by the invention, the problem that the result does not conform to the reality due to the fact that only elastic modulus and Poisson's ratio are used in existing sealing element simulation can be solved.

The invention relates to a stamping die service life dynamic evaluation system and method based on multi-mode sensing. According to the system, a feature extraction module obtains stress distribution data and crack position information and inputs the data into a trained three-dimensional stress field model to obtain dynamic feature parameters of a stress concentration area under high-frequency cyclic loads; the crack prediction module judges the dynamic characteristic parameters based on a preset threshold value, and once the dynamic characteristic parameters exceed the preset threshold value, analysis is carried out according to the mapping relation between the stress level and the crack rate and the microstructure evolution rule at the same time, and a crack propagation prediction value is generated. And the service life evaluation module inputs the crack propagation prediction value into the trained health state evaluation model, and finally the service life evaluation value of the stamping die is obtained. The service life evaluation value of the stamping die under the existing working condition and the operation condition is accurately estimated, the production efficiency is effectively improved, the production cost is reduced, the potential crack risk of the die is perceived in advance, and accurate and dynamic die service life evaluation is achieved.

The invention relates to the technical field of production line control, and discloses an intelligent control method and system for production line mold machining. According to the method, a pressure distribution state is determined by obtaining initial cutting parameters of a cutter and surface pressure data of a mold, mold node stress distribution is calculated by utilizing a three-dimensional finite element model, and a stress concentration position is identified. And predicting a mold deformation trend through a linear regression model according to the thermal expansion coefficient and the environment temperature data. Meanwhile, the initial cutting parameters are optimized by combining a cutting parameter optimization objective function, and the objective function comprehensively considers factors of tool wear, surface quality and residual stress. And on the basis of a die deformation prediction result, a dynamic compensation algorithm is adopted to adjust supporting structure parameters and optimize cutting parameters, and finally optimal cutting parameters and a target tool path are generated. According to the method, precise coordination control over pressure distribution and mold deformation under the complex working condition is achieved, the residual deformation risk is reduced, and the mold machining precision is improved.

The embodiment of the invention relates to the technical field of data modeling analysis, and discloses a tunnel rock stratum large deformation analysis method and system based on three-dimensional modeling, and the method comprises the steps: collecting initial three-dimensional point cloud data through three-dimensional laser scanning; constructing an initial three-dimensional geologic model containing spatial morphological characteristics and a rock stratum interface geometric topological relation through space coordinate matching and curved surface reconstruction; finite element unstructured mesh generation is carried out on the model, and a rock stratum mechanical analysis model is established in combination with physical and mechanical parameters; inputting construction parameters and process information, activating unit nodes in sequence, and solving a force balance equation to simulate stress distribution; identifying a potential deformation area through iterative calculation, and adjusting boundary conditions to predict a deformation trend; the three-dimensional space distribution cloud atlas, the time evolution curve and the position marks are fused to generate multi-view deformation early warning information, the accuracy and the visualization degree of tunnel rock stratum deformation analysis are improved, and support is provided for construction safety.

The invention discloses a concrete structure crack image detection method and system based on deep learning. The method comprises the following steps: acquiring a multi-view image; performing spatial alignment according to the multi-view image to obtain a matched image sequence; performing image fusion according to the matched image sequence to obtain a fused image; performing semantic segmentation based on transfer learning according to the fused image and a preset neural network model to obtain a crack mask; according to the crack mask and the fusion image, skeleton extraction is carried out, and a preliminary crack network topology is obtained; optimizing the preliminary fracture network topology to obtain a fracture network topology; carrying out finite element analysis according to preset material attributes, a stress state and a concrete structure to obtain a stress distribution cloud picture; and according to the stress distribution cloud picture and the fracture network topology, carrying out space matching to obtain a stress fracture correlation model. The method can analyze the dynamic change of the concrete crack.

The invention relates to an intelligent building damage detection method based on machine vision. The method comprises the following steps: multi-modal data acquisition; preprocessing and feature enhancement: processing the visual data and the acoustic data; damage detection: designing a material constitutive driven GAN architecture, calculating and generating stress distribution of a crack by a generator through a differentiable finite element analysis layer, performing L2 loss constraint on the stress distribution and an ABAQUS simulation result, evaluating image authenticity and physical consistency by a discriminator by adopting a double-branch structure, and outputting damage parameters in cooperation with a lightweight YOLOv8-Nano detection head; acoustics-vision depth fusion detection: aligning a potential space through a double-flow cross-modal encoder in combination with cross-modal contrast learning, and preferentially processing hidden damage by using a gating fusion unit for dynamically calculating a fusion weight through material acoustic impedance; when the visual detection confidence coefficient is smaller than a set threshold value, triggering frequency domain mutation detection and spatial positioning of the acoustic mode; and edge-cloud cooperative processing.

The invention relates to the field of electric power engineering, and discloses a cement electric tower foundation differential settlement monitoring method and system, and the method comprises the steps: obtaining cement electric tower foundation bearing parameters and environmental geological data, constructing a sensing displacement array, deploying basic monitoring points, calibrating an initial reference value, and then collecting displacement data in a fixed time period, the method comprises the following steps: calculating a uniform settlement amount, generating a deformation early warning signal in combination with inclination angle information, analyzing a support weak area according to the early warning signal, determining a reinforcement priority, detecting stress distribution and a crack propagation trend, generating a settlement regulation and control instruction, analyzing instruction feedback data, identifying an abnormal settlement mode, calculating an instability risk coefficient, and compensating and correcting offset data. And analyzing a settlement evolution rule, and finally making a grading maintenance strategy. The maintenance safety and stability of the cement electric tower structure can be improved.

The invention discloses a large-model-based steel bar engineering quality detection method, system and equipment and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring vibration response data and three-dimensional point cloud data of a to-be-detected reinforcement project; inputting the vibration response data into a pre-trained vibration characteristic analysis model to obtain a stress distribution map and a stress transmission path, and performing segmentation processing on the three-dimensional point cloud data along the stress transmission direction to obtain segmented point cloud data; based on the segmented point cloud data, extracting morphological characteristics of each section of reinforcing steel bar in the to-be-detected reinforcing steel bar project, and establishing a stress-geometry coupled digital twinborn model according to the stress distribution map and the morphological characteristics; performing dynamic mapping on the digital twinborn model and a pre-calibrated BIM design model, and determining steel bar deformation parameters of the stress abnormal region; and based on the steel bar deformation parameters, generating a quality detection report of the to-be-detected steel bar project. By implementing the technical scheme provided by the invention, the accuracy of reinforcing steel bar engineering quality detection can be improved.

The invention discloses an electric actuating mechanism service life evaluation method and system based on dynamic load monitoring, and particularly relates to the technical field of service life evaluation. For a fretting wear accumulation effect caused by resonance of a load frequency spectrum and a mechanical gap and lubricating oil film fracture reconstruction lag caused by the fact that the load change rate exceeds a critical value, an output shaft torque signal is collected for time-frequency domain conversion, and a load change frequency spectrum and a load change rate are obtained; and in combination with oil film state monitoring, the lubrication state deterioration risk and the wear accumulation effect are evaluated, and the failure critical risk is determined. When the failure critical risk is lower than a safety threshold value, analyzing contact surface stress distribution through a finite element to evaluate the wear degree, predicting the residual life by combining the failure threshold value of the similar electric actuating mechanism in a historical database, and judging whether the electric actuating mechanism needs to be maintained or replaced in advance, so that the actual wear and failure trend of the electric actuating mechanism under multiple working conditions can be accurately reflected; and the scientificity of maintenance decision and the operation safety are improved.

The invention discloses a heterogeneous road structure cavity disease inspection method, device and equipment and a storage medium, and the method comprises the steps: constructing a highly-simulated heterogeneous road structure model, and adaptively generating an irregular cavity disease meeting the horizon geometric constraint; the problems that electromagnetic wave reflection simulation is distorted due to the fact that a traditional homogeneous simplified model cannot reflect dielectric mutation between aggregate and a cementing material, and a regularized cavity form does not conform to a real stress distribution expansion mode are solved. According to the method, radar forward modeling simulation data containing complex heterogeneous backgrounds and randomly-shaped holes can be automatically generated in batches, a rich, real and diversified training data set is provided for intelligent identification of road internal diseases based on deep learning, and the bottleneck problem of lack of real disease radar image data is relieved.