341 results about "Strain distribution" patented technology

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 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 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.

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 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 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 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 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.

The invention relates to the technical field of bridge load tests, and provides a bridge load test crack increment change observation method and system, and the method comprises the steps: collecting prestressing tendon circumferential strain distribution data and dual-wavelength strain response data; calculating an interface bonding stiffness change parameter; pre-stress modulation effect compensation and polar coordinate system conversion are carried out on the strain response data and the rigidity change parameters, and micro-crack characteristic signals are obtained; load data and beam group arrangement parameters are collected, and a micro-crack propagation prediction model is established in combination with the interface bonding stiffness change parameters; inputting the micro-crack characteristic signal into a micro-crack propagation prediction model, and outputting a micro-crack development trend prediction result; and adjusting sampling parameters of a dual-wavelength time division multiplexing algorithm and characteristic extraction parameters of prestress modulation effect compensation according to a micro-crack development trend prediction result. According to the invention, the precision of observing the increment change of the micro-crack in the bridge load test is improved, and the automatic monitoring and prediction of the micro-crack of the prestressed concrete bridge are realized.

The invention discloses a non-destructive and high-precision testing and calibrating device and method for internal geometric parameters of a multi-core optical fiber. The device comprises a two-dimensional arc groove testing mold, a first rotating clamp, a second rotating clamp, a fan-in and fan-out device, an optical switch, an optical frequency domain reflectometer and data collecting and processing equipment. By embedding the to-be-measured multi-core optical fiber into the groove with the known curvature and combining the distributed strain demodulation technology, the distributed strain distribution in the bending state is directly measured, and the distributed strain distribution in the bending state is directly measured through a mathematical model of the bending-torsion composite action and a cosine function fitting algorithm without cutting or damaging the optical fiber structure. And synchronously calculating the torsion period, the core spacing and the core included angle. The method overcomes the defects that a traditional microscope method depends on end face preparation and destructive detection, realizes on-line measurement of all-fiber length, can adapt to multi-core fibers with different core numbers and cladding structures, and can be expanded to high-precision industrial scenes such as fiber-optic gyroscope calibration, flexible conduit morphology monitoring, submarine optical cable process optimization and the like.

The invention discloses an underwater intelligent cable spatial form reconstruction system based on fusion of fiber bragg grating sensing and underwater acoustic positioning, and belongs to the technical field of underwater measurement and sensing. A plurality of fiber bragg grating sensors are inscribed on a three-core fiber in the intelligent cable of the system along the length direction to measure multi-point strain data along the length direction. According to the strain distribution, the bending curvature and the torsion of each measuring point are calculated, a continuous curvature and torsion function is constructed, the tangent vector, the normal vector and the sub-normal vector of each point of the cable are solved in combination with a Freen-Serret algorithm, and the tangent vector is integrated to obtain the space curve form of the cable. And determining the absolute position of the cable terminal in the three-dimensional space according to the acoustic ranging result. And realizing spatial form and attitude recognition of the whole underwater cable through constraint fusion of a curve reconstruction result and an end point position. According to the system, an inertial measurement unit is arranged on a cable to obtain local attitude information, so that the accuracy and the stability of curve reconstruction are improved. The method can be widely applied to space state monitoring of underwater long cable operation.

The invention relates to the field of deformation monitoring, in particular to an underground pipeline deformation real-time monitoring method and device, equipment and a medium. The method comprises the steps that through real-time connection with equipment such as an optical fiber sensor and a three-dimensional laser scanner, spectral signals can be received in real time, three-dimensional point cloud data can be collected, and data collection delay is avoided. In a data processing link, whether a strain distribution map is generated based on a spectral signal or a deformation displacement vector is determined from three-dimensional point cloud data, an efficient algorithm is adopted to quickly complete calculation and analysis, and the data processing time is greatly shortened. Moreover, the electronic equipment continuously monitors the strain distribution map and the deformation displacement vector in real time, and once it is monitored that the strain gradient and the displacement change rate of the monitoring node in the pipeline deformation field model exceed threshold values, an early warning mechanism is triggered immediately, deformation early warning information is generated, and the real-time performance of underground pipeline deformation monitoring is improved.

The invention relates to the technical field of composite armored cables, and discloses an intelligent sensing type photoelectric composite armored cable for deep-sea mining equipment, which comprises a cable core formed by twisting a plurality of power cable core conductors, an optical fiber communication unit, a medium conveying pipeline and an intelligent sensing unit, and an outer coating layer, tin-plated annealed copper wires and high-strength copper foil wires are arranged in a conductor of the power cable core, an insulation shielding layer is wrapped outside the conductor, the intelligent sensing unit integrates a plurality of optical fibers and optical fiber grating sensors, a loose structure is arranged outside the intelligent sensing unit, one intelligent sensing unit is used for monitoring strain distribution, the other intelligent sensing unit is used for monitoring temperature distribution, and other redundant optical fibers are used as redundant backups. A certain length of grid region interval is preset, distributed measurement along the length of the cable is realized, a sensing optical fiber in the intelligent sensing unit is connected to an optical fiber demodulator of a deck control room, a software system displays strain, temperature, vibration and form data of each point of the cable in real time, an alarm threshold value is set, and the structure is highly integrated and can adapt to a deepwater environment.

The invention belongs to the technical field of optical fiber sensing, and particularly relates to an optical fiber composite sensing array and edge demodulation device for long-distance infrastructure multi-parameter monitoring, and the optical fiber composite sensing array comprises a distributed temperature sensing channel, a distributed vibration sensing channel, an optical fiber grating sensing channel, a main sensing optical fiber, and a wavelength division multiplexing assembly. A wavelength separation device and a digital system; the edge demodulation device comprises a time synchronization mechanism, a data processing stream and a multi-parameter fusion model, a single main sensing optical fiber is utilized to integrate a distributed temperature sensing channel, a distributed vibration sensing channel and an optical fiber grating sensing channel, the edge demodulation device is arranged, and a multi-source data fusion model is constructed; and full-field synchronous reconstruction of a temperature field, a vibration event and strain distribution is realized, so that high-precision synchronous monitoring and real-time decision making of multiple physical quantities are realized.

The invention relates to a blade root fatigue load prediction and inversion system for an offshore wind turbine, and the system comprises a data collection assembly which obtains a circumferential strain gradient, and synchronously collects wave spectrum energy distribution, leading edge erosion degree and three-dimensional wind field data to establish a joint input spectrum; the hybrid calculation component is used for generating an adversarial network, reconstructing strain distribution of a bolt hole in a non-degraded state, and comparing a circumferential strain gradient to obtain an abnormal blade root dangerous point; and the control output assembly is used for superposing the damage accumulation cloud picture of the blade root dangerous point, displaying the fatigue life consumption rate and calling an equivalent load spectrum duration curve, and generating an independent variable pitch angle instruction according to the combination of asymmetric load distribution and the combined input spectrum. According to the method, quantitative identification and positioning of early-stage micro-damage can be realized, the hysteresis limitation of traditional threshold alarm is broken through, complex fatigue analysis is converted into engineering interpretable indexes, and a fault backtracking technical support is provided; load active compensation control is achieved through an independent variable pitch instruction, and fatigue crack initiation and expansion are effectively restrained.

The invention provides a highway bridge bearing capacity calculation method and system, and relates to the technical field of bearing capacity calculation, and the method comprises the steps: obtaining basic parameters of a target highway bridge; performing model construction according to the basic parameters to obtain a bridge three-dimensional mechanical model; carrying out load mode analysis according to the bridge three-dimensional mechanical model, and carrying out space distribution on a dead load standard value and a live load dynamic effect based on the number of lanes and a load action area to obtain a total load value; stress field solving is conducted according to the total load value and the bridge three-dimensional mechanical model, and stress-strain distribution data are obtained by calculating stress-strain distribution of the main beam midspan section and the pier beam connection area section; calculating the ultimate bearing capacity according to the stress-strain distribution data, and evaluating the bearing capacity based on the relationship between the allowable stress and the maximum stress of the material to obtain the ultimate bearing capacity value of the bridge; and correcting the ultimate bearing capacity value of the bridge based on the service life and the current damage degree to obtain a highway bridge bearing capacity evaluation result.

The invention discloses a robot global attitude estimation method combining physical modeling and visual perception, which combines a Cosseerat Rod theory, utilizes sparse internal sensor input to model internal strain distribution of a flexible body, and utilizes dual quaternion representation to carry out efficient global attitude coding. In order to realize comprehensive spatial registration, the method is further combined with attitude clues of monocular vision depth estimation, so that joint registration between the reconstructed internal shape and external visual feedback is realized. The fusion framework dynamically corrects a difference between a shape based on an internal physical model and an external visual perception attitude, thereby realizing accurate and stable control in a complex or limited environment. The method is particularly effective in applications such as surgical robots and soft continuum actuators, as these applications require minimal perception and strong global-local state perception.

The invention relates to the technical field of intelligent sensing of bridges, in particular to an energy collection device arranged in a bridge expansion joint, a design method and a bridge expansion joint monitoring mechanism, and the device comprises a conversion unit which is arranged in a bridge expansion joint structure, captures the strain of the structure after the structure is stressed, and generates an alternating flexural electric signal; the substrate layer is positioned between the conversion unit and the bridge expansion joint and is used for adjusting the strain distribution of the conversion unit; the rectification unit is integrated with an impedance matching circuit, is connected with the conversion unit, converts the alternating deflection electric signal into direct current, and transmits the direct current to the energy storage unit; compared with the prior art, the monitoring mechanism for the bridge expansion joint has the advantages that a self-energy-supply structure based on the flexoelectric effect is arranged in the device, an external power source is not needed, the wiring and maintenance cost is remarkably reduced, meanwhile, millimeter-level displacement distinguishing and real-time structure health assessment are achieved through the monitoring unit, and the monitoring accuracy is improved. And the structural safety of the bridge expansion joint is ensured.

The invention provides a concrete normal section reinforcement design method and system, electronic equipment and a storage medium. The method comprises the following steps: establishing a section stress model containing multi-layer reinforcement strain distribution based on plane section assumption; the stress state of the whole process of the section from axle center compression to axle center tension is simulated by setting strain proportion parameters of the strain of the outermost reinforcing steel bar on the tension side and the limit pressure strain of the concrete; aiming at a plurality of set strain state control points, calculating stress of each steel bar layer position, contribution of the stress to axial force and bending moment and contribution of the stress to a concrete compression area; and based on the section static balance condition, the optimal reinforcement area meeting the design load requirement is determined through iterative calculation, and the configuration constraint of multiple layers of steel bars is considered in the iterative calculation. The method overcomes the defects that a traditional method overestimates the effect of the pressed steel bars and ignores the strain difference of multiple layers of steel bars, and the accuracy and safety of reinforcement design of high-performance concrete structures such as nuclear islands are remarkably improved.

The invention belongs to the technical field of tire design, and discloses a mining wide-body vehicle tire crown carcass contour design method and system, and the method comprises the steps: collecting data to construct a working condition pedigree, carrying out load analysis to obtain an inflation equilibrium state target contour, and forming a crown stress-strain ideal distribution domain; surveying and mapping a material rigidity change mapping table, constructing a deformation matrix, and reversely solving to obtain an initial contour map of the mold; dividing crown carcass curvature partitions, carrying out crown stress analysis, and carrying out matching evaluation to form a deviation correction parameter set; a contour design scheme is accurately corrected, inflation deformation is simulated, and a final crown carcass contour map is obtained through adaptability evaluation selection. According to the method, through combination of mechanical analysis and material characteristics, accurate contour control of the tire from design to production is achieved, and the durability and working condition adaptability of the tire of the mining wide-body vehicle are improved.

The invention discloses a decoupling method of an integrated six-dimensional force sensor for a combine harvester, and belongs to the technical field of sensor application. The sensor adopts a Y-shaped three-beam radial symmetric structure, and highly integrates a belt wheel function and a six-dimensional force detection function: establishing an elastomer structure simplified mechanical model, establishing a semi-analytical method model, and analyzing to obtain a strain distribution rule under a uniaxial load. Acquiring a sensor geometric structure and a strain gauge measurement position which meet design requirements according to a strain distribution rule; a multi-output least square support vector regression machine is adopted, hyper-parameters are optimized through grid search, a high-precision mapping model from an electric signal to a six-dimensional load is constructed, the decoupling precision is greatly improved, the whole process innovation from the structure to stress analysis to decoupling is realized, and the method is suitable for large-scale popularization and application. The device is especially suitable for agricultural machinery scenes with limited space and complex load, such as combine harvesters.

The present invention relates to the field of automated identification technology, and specifically discloses an adaptive deformation correction and identification system for flexible curved barcodes. By real-time monitoring of the three-dimensional shape changes and strain distribution of the barcode area on the flexible surface, the system can accurately capture complex deformation situations, and utilizes a K-means clustering algorithm to calculate curvature eigenvalues, and Haar wavelet transform to analyze strain data to determine deformation eigenvalues, thereby comprehensively evaluating the degree of deformation of the barcode and its impact on the identification process. Based on these eigenvalues, a random forest model is constructed to predict the degree of deformation and output a deformation score to ensure high-precision identification. When severe, uncorrectable deformation is detected, the system automatically triggers an alarm mechanism, including an audible alarm, a visual signal, and a network notification, to promptly inform management personnel and ensure the stability and long-term reliability of the system.