12019 results about "Statistical physics" patented technology

The local intrinsic dimensionality (LID) for a diffusion model with respect to a particular data sample is determined by using the diffusion model's diffusion process to apply noise to a data sample and evaluate how the estimated log probability of the data sample changes at different levels of noise. Particularly, the differential of change in noise to change in log probability can be used to determine the local intrinsic dimensionality. This may be determined by evaluating the log probability at several noise levels and determining a slope of the difference. In additional examples, the differential is evaluated directly at a selected noise level. The selected noise level can be optimized by calculating the estimated LID for various data samples at a variety of noise levels and selecting the LID that corresponds to a “knee” where the estimated LID sharply changes.

The invention discloses an automatic control method and system for secondary granulation of a high-voltage zinc oxide resistor disc, relates to the technical field of intelligent manufacturing of power equipment, and solves the problems of out-of-control particle morphology caused by dynamic coupling parameter identification lag and control instability caused by multi-physical field parameter coupling in an existing method. According to the invention, a dynamic physical property parameter matrix is generated in real time based on multi-band dielectric relaxation spectrum analysis and terahertz wave tomography; predicting a fluidized phase change threshold value and an energy gathering area through multi-physics field coupling modeling; a time sequence attention deep reinforcement learning algorithm is adopted to generate a multi-field cooperative adjustment instruction; positioning a parameter conflict source and triggering decoupling compensation by combining a high-frequency vibration and acoustic emission combined monitoring module; performing closed-loop correction on the control network weight based on the laser spectrum data and a partial least squares regression model; the real-time performance of fluidization parameter identification, the stability of multi-field coupling control and the recovery efficiency of abnormal working conditions are remarkably improved, and meanwhile the batch consistency of the electrical performance of the resistor discs is guaranteed.

The invention relates to the technical field of crustal stress field data processing, in particular to a crustal stress field three-dimensional dynamic inversion method based on a multi-scale adaptive algorithm. The method comprises the following steps: acquiring a geological data set of a target area; constructing a crustal stress field three-dimensional initial model based on the geological data set, and performing geologic body space division and mesh generation to obtain crustal stress field three-dimensional mesh model data; performing multi-scale region division on the crustal stress field three-dimensional grid model data, and establishing a multi-scale weighting function to obtain multi-scale partition mapping information; and constructing a cross-scale boundary adaptive transmission mechanism, and establishing a stress tensor continuity constraint model at a multi-scale partition boundary to obtain cross-scale stress boundary coupling data. Through a multi-scale adaptive algorithm and dynamic closed-loop optimization, high-precision, dynamic and continuous inversion of a crustal stress field in a complex geologic structure is realized.

The invention relates to the technical field of rock mechanics, and discloses a rock three-dimensional reconstruction method and system based on multi-source information fusion, and the method comprises the steps: obtaining and preprocessing data, carrying out the spatial feature learning of a fusion feature vector through a 3D-CNN network, and constructing a three-dimensional voxel model of rock microscopic damage; converting the fused image data into a point cloud model of the underground cavern surrounding rock structure by adopting a three-dimensional reconstruction algorithm based on point cloud, and constructing a digital twin framework of the underground cavern surrounding rock structure based on an implicit surface reconstruction algorithm; feature parameters output by the three-dimensional voxel model and the digital twinning framework are used as input, and the optimal supporting opportunity and supporting parameters are output through an LSTM-CNN fusion model; in the underground engineering construction process, surrounding rock deformation data are collected in real time, and a supporting scheme is adjusted in real time through a depth deterministic strategy gradient algorithm; according to the method, the scientificity and timeliness of support design under complex geological conditions can be improved.

The invention belongs to the technical field of automatic detection, and discloses an online laser detection and grading regulation and control system for particle size distribution of crushed raw ore. The invention aims to solve the problems of halo effect interference caused by adhesion of fine dust in a complex industrial environment and insufficient detection precision of a traditional method. The system obtains dynamic scattering light field data through the laser scanning module, identifies a halo effect region and generates a compensation coefficient by using the light field preprocessing module, and realizes accurate reconstruction of particle boundaries in combination with the edge reconstruction module. The particle size distribution analysis module extracts multi-dimensional geometric features based on the accurate boundary data, and generates a real-time particle size distribution curve. The grading strategy generation module formulates a regulation and control strategy according to deviation characteristics of the curve and a preset standard, and the dynamic regulation and control module adjusts parameters of crushing equipment in real time and optimizes particle size distribution. The method effectively overcomes the halo effect interference, improves the detection precision and the system stability, breaks through the single limitation of a traditional method, and provides comprehensive data support for a downstream process.

The invention discloses a sliding bearing friction wear prediction method based on fluid mechanics, and relates to the technical field of mechanical state monitoring, and the method comprises the steps: collecting single-point temperature, local pressure, vibration time domain signals and a bearing pedestal inclination angle through a sensor, and generating sensor data; performing field reconstruction based on a fluid mechanics conservation equation on the sensor data to obtain multi-field data; performing spatial alignment on the multi-field data, inputting the multi-field data into a long short-term memory (LSTM) network, and generating wear state characteristics; training a sparse correlation vector machine regression model RVM by using the historical wear data, establishing a nonlinear mapping relationship between the wear state characteristics and the wear depth, taking the wear state characteristics as the input of the vector machine regression model RVM, and outputting the predicted wear depth; sensor data and the predicted wear depth are fused in real time through Kalman filtering, and when prediction deviation exceeds a covariance threshold value, vector machine regression model RVM parameters are updated.

The invention belongs to the technical field of turbulent flow field prediction and deep learning, and discloses an incompressible turbulent flow field prediction method based on a potential diffusion model. The method comprises the following steps: acquiring original turbulence data; processing the turbulence data; constructing a turbulence prediction model; model training; and evaluating the model and the like. The model of the technical scheme of the invention specifically comprises the following steps: designing a multi-scale Fourier auto-encoder for extracting multi-scale space and frequency domain features in a turbulence field and obtaining a global structure and a local scale structure of turbulence; a novel accelerated sampling method is proposed and introduced in the diffusion process, namely a diffusion probability model solver greatly shortens the reasoning time in a potential space and keeps high fidelity in long-time-sequence prediction; a physical constraint loss item based on a partial differential equation is introduced, and a Navier-Stokes equation (N-S) is explicitly introduced into a training process, so that the physical consistency of results is effectively improved, and errors are remarkably reduced.

The invention relates to the technical field of laser spectrum detection, in particular to an LIBS (Laser-induced Breakdown Spectroscopy) spectrum noise reduction method, system and equipment based on adaptive threshold wavelet transform and a storage medium. Acquiring an original spectral signal of the laser-induced breakdown spectroscopy; performing five-layer multi-layer wavelet decomposition on the original spectral signal by adopting a db4 wavelet basis function to obtain a high-frequency coefficient and a low-frequency coefficient of each layer; calculating a noise intensity standard deviation based on the detail coefficient of the highest decomposition layer; dynamically determining the optimal value of the regulation factor through a double-layer optimization strategy combining a grid search method and a golden section iterative optimization method; constructing an adaptive threshold value based on the noise intensity standard deviation and the adjustment factor; carrying out threshold value processing on the high-frequency coefficient by adopting a self-adaptive threshold value; and performing wavelet reconstruction on the processed high-frequency coefficient and low-frequency coefficient, and outputting a denoised spectral signal. While the LIBS spectral signal-to-noise ratio is remarkably improved, the spectral feature form is completely reserved, and reliable technical support is provided for laser-induced breakdown spectroscopy detection in a complex industrial environment.

The invention discloses an uninsulated superconducting coil electromagnetic characteristic prediction method, system and device and a medium, and relates to the field of electrical digital data processing. Constructing a loss function based on the data fitting loss and physical constraint loss including a Kirchhoff's current law loss item, a T-A equation loss item and an E-J power law loss item; training, testing and verifying the network model by adopting a sample data set, balancing each loss item in a loss function by adopting an adaptive weight distribution strategy in the training process, and minimizing the loss function through an Adam optimization algorithm to obtain an electromagnetic characteristic prediction model; and inputting the obtained current working condition parameters into the electromagnetic characteristic prediction model to obtain an electromagnetic characteristic prediction result. According to the method, the problems of low calculation efficiency, poor physical consistency and generalization ability and the like in the existing method are solved, and a feasible tool is provided for rapid design, optimization and operation of the uninsulated superconducting coil.

The invention provides a third-generation semiconductor device multi-physical field coupling effect dynamic monitoring method and system, and relates to the technical field of semiconductors, and the method comprises the steps: collecting the data of a temperature field, an electric field, a magnetic field and a stress field through a complementary sensor array, constructing a feature tensor, inputting the feature tensor into a pre-trained graph neural network, and extracting a coupling effect dynamic evolution rule; on-line analysis and prediction are carried out by using an adaptive time window and a recurrent neural network, a multi-field coupling correlation degree evaluation model is established, an early warning level is determined according to the deviation between the coupling correlation degree and an early warning threshold value, and device risk evaluation and maintenance suggestions are generated, so that early warning of the failure risk of the semiconductor device is realized.

The invention relates to the technical field of chip temperature control, and discloses a chip temperature regulation and control system and method, and the method comprises the steps: building a thermal field dynamic model, integrating real-time power consumption data and environment heat dissipation parameters, and generating a thermal resistance adjustment coefficient and a power consumption correction coefficient; establishing a temperature prediction network to perform multi-source temperature field prediction; a dynamic regulation and control strategy is set according to a prediction result, and heat dissipation control parameters are optimized to achieve thermal field balance control; and executing feedback calibration and model iteration. The system comprises a thermal field dynamic modeling module, a multi-source temperature field prediction module, a dynamic regulation and control strategy execution module, a feedback calibration module and an iteration module. The method can accurately predict the heat distribution of the chip, realizes real-time and intelligent temperature regulation and control, improves the performance and reliability of the chip, reduces the energy consumption, and has good adaptability and universality.

The invention discloses an energy storage system state evolution trend prediction method based on multi-source data fusion. The method comprises the steps of terminal voltage, current and temperature time sequence data acquisition, time sequence segmentation normalization, multi-physics field coupling feature construction, trend prediction model construction and training and energy storage system state evolution trend prediction. According to the method, the distinguishing capacity of the model for charging and discharging physical characteristics is improved, meanwhile, the voltage change rate, the multi-dimensional feature vector of the differential internal resistance and the thermal-electric coupling effect and the explicit encoding electric-thermal-resistance coupling relation are constructed, the transient response and the temperature hysteresis effect can be effectively captured, and then the model can be used for analyzing the charging and discharging physical characteristics. A degradation-aware cross-cycle feature extraction and gating mechanism is adopted, short-term fluctuation and long-term trend are adaptively balanced in multi-scale prediction, the prediction conflict problem is relieved, finally, physical constraints based on the electrochemical law and the internal resistance temperature characteristic are embedded in a loss function, it is ensured that the prediction result is accurate in numerical value and conforms to the physical law, and the prediction accuracy is improved. And generation of physically impossible solutions is avoided.

The invention relates to the technical field of three-dimensional reconstruction and simulation, and discloses a foam material porous structure three-dimensional reconstruction and simulation system, which comprises a scanning imaging module used for carrying out scanning imaging on a foam material sample to obtain pore image data; the pore boundary recognition module is used for performing pore boundary recognition based on the pore image data to obtain a pore boundary recognition result; the three-dimensional reconstruction module is used for executing three-dimensional reconstruction of bubble evolution physical constraints according to the pore boundary recognition result to obtain a three-dimensional reconstruction model; the coupling simulation module is used for performing grid division and coupling simulation on the three-dimensional reconstruction model to obtain mechanical simulation data; and the parameter solving module is used for carrying out density field optimization and parameter solving based on the mechanical simulation data to obtain optimal gap design parameters, so that seamless connection from simulation analysis to optimization design is realized, the structural design period of the foaming material is shortened, and the design precision and consistency are improved.

The invention provides a double-pulse high-frequency switching power supply control method and system for precise electroplating, and relates to the field of electroplating, and the method comprises the steps: collecting area surface data through a three-dimensional scanner, and obtaining a three-dimensional model; extracting a high-curvature area grid according to the three-dimensional model, and calculating to obtain a surface concave-convex degree index; performing current field simulation, and calculating current density vector distribution; when the deviation of the current density vector distribution exceeds a preset deviation threshold value, pulse parameters are optimized through a genetic algorithm; selecting a pulse parameter which is most matched with the surface concave-convex degree index, and carrying out iterative calculation to obtain updated current density vector distribution; based on the difference between the updated current density vector distribution and the initial distribution, predicting a coating thickness value, and determining a preliminary evaluation index; and optimizing pulse parameters to obtain a final control scheme of uniform current distribution. According to the method, pulse parameters can be dynamically optimized according to the geometrical shape of the workpiece, and uniform current distribution and stable plating quality are realized.

The invention discloses a hybrid Lp regularization magnetotelluric two-dimensional inversion method based on adaptive weight, and the method comprises the steps: reading magnetotelluric observation data, carrying out the quadrilateral finite element grid discretization of a whole inversion region, building an inversion grid, and building an initial model and a prior model of the inversion conductivity of the inversion region; based on the initial model and the prior model of the inversion conductivity and the magnetotelluric observation data, constructing a mixed Lp regularization inversion objective function comprising an L1 regularization item, an L2 regularization item and a data fitting item; in an inversion iteration process, adaptively adjusting weight factors of the L2 regularization item and the L1 regularization item; and based on the inversion grid, performing magnetotelluric two-dimensional inversion according to the inversion objective function mixed with Lp regularization, the initial model and magnetotelluric observation data, and outputting optimal model parameters of Gaussian Newton inversion to obtain underground electrical structure information. According to the invention, the resolution and accuracy of the inversion result are improved.

The invention relates to an infrared spectrum baseline drift dynamic compensation method and system based on multi-parameter fusion, and the method comprises the steps: continuously obtaining spectrum data, current environment parameters and light source state data of a target oil full wave band, carrying out the feature extraction, and obtaining a baseline drift sensitive feature set and a coupling feature set; constructing a prediction model taking the baseline drift amount as output, and taking the baseline drift sensitive feature set and the coupling feature set as input; the reverse baseline drift amount based on the baseline offset of each wave band is superposed in the spectral data as compensation; according to the invention, through multi-source data acquisition and analysis, monitoring of spectrum baseline drift influence factors is realized; accurate correction of different spectral characteristics is realized through a dynamic compensation strategy special for a wave band; and through a continuous self-adaptive updating mechanism, stable tracking of the long-term drift trend is realized, and the effect of prolonging the equipment maintenance period is achieved.

The invention discloses a tight reservoir three-dimensional crustal stress field modeling method based on an improved neural network. The tight reservoir three-dimensional crustal stress field modeling method comprises the steps that S1, a unified-format multi-source geological physical data tensor set is constructed; s2, constructing a frequency domain hierarchical enhancement-SIREN implicit neural network structure based on the unified format multi-source geological physical data tensor set; s3, inputting the candidate hyper-parameter configuration into the frequency domain hierarchical enhancement-SIREN implicit neural network to complete one-time model training; s4, aiming at each candidate hyper-parameter configuration, initializing an inner-layer population of the black widow optimization algorithm, completing second model training, and obtaining an optimal model parameter of the frequency domain hierarchical enhancement-SIREN implicit neural network; s5, tight reservoir fracturing parameter optimization and real-time safety window adjustment are achieved. According to the method, the continuous stress field can be quickly generated at the resolution of 1 m, real-time well section updating and fracturing scheme optimization are supported, and the fracturing transformation effect, the fracturing safety margin and the reliability of economic productivity prediction are remarkably improved in practical application.

The invention relates to the technical field of electrical cabinet monitoring, in particular to an electrical cabinet active anti-condensation method and system based on a condensation mechanism mathematical model. According to the invention, a mathematical model based on a condensation formation mechanism is constructed, and environmental meteorological parameters, station room environmental parameters, electrical cabinet internal microenvironment parameters and cable trench state parameters are monitored through a four-stage monitoring system; and fusing the multi-source heterogeneous data, inputting a condensation mechanism mathematical model to calculate a condensation risk index, and adopting a hierarchical response mechanism to cooperatively control an execution mechanism according to the obtained condensation risk index value to realize active defense of condensation. According to the method, the condensation phenomenon with the space-time dynamic characteristic is quantitatively described through a mass transfer and heat transfer coupling equation, a grading prevention and control strategy and a cooperative control algorithm are developed by calculating the condensation risk index, setting the threshold condition and judging the condensation formation risk level, the prevention and control intensity is dynamically adjusted according to the risk level, and the control accuracy is improved. And the risk response speed and the resource utilization efficiency under the complex working condition are obviously improved.

The invention discloses a spectral gas concentration inversion method based on a CNN-Transform fusion model, and belongs to the technical field of trace gas detection and spectral signal processing. The method comprises the following steps: acquiring second harmonic spectrum data of target gas through a TDLAS (tunable diode laser absorption spectroscopy) system; carrying out abnormal value elimination and smooth filtering processing on the acquired signal, and constructing a standard input vector; a depth model fusing a one-dimensional convolutional neural network (CNN) and a Transform architecture is designed, the CNN is used for extracting local spectrogram features, and the Transform is used for modeling a global dependency relationship; and inputting the training set and the verification set into the model for training optimization, and finally realizing high-precision inversion of the sample gas concentration of the test set. Experimental results show that the method is superior to an existing model in the aspects of fitting precision, robustness and error control, and has good physical consistency and engineering popularization value.

The invention relates to the technical field of attitude measurement, and discloses a gradienter attitude real-time calibration method based on multi-modal data fusion, which comprises the following steps: acquiring multi-modal original data and completing unified preprocessing to obtain multi-modal data; reconstructing a liquid surface form in a physical domain neural operator layer, and outputting a physical domain attitude and a residual error; noise and drift are deduced in a sensing domain neural operator layer, and a sensing domain attitude estimation value, an offset parameter, a scale parameter and a residual error are output; establishing a deviation memory bank, updating by using residual errors and historical results, generating a long-term drift compensation amount, and superposing a sensing domain result; inputting a physical domain and a compensated sensing domain result into a dynamic constraint reversible transformation model, and outputting a fusion attitude and uncertainty; and executing slow variable refining compensation on the updated parameters, and outputting final real-time calibration attitude and quality information. According to the invention, by introducing multi-modal data fusion and double-layer reversible neural operator modeling, real-time, high-precision and long-term stable calibration of the attitude of the gradienter is realized.

The invention relates to the technical field of precision manufacturing, and discloses a multi-mode laser galvanometer calibration method and device. The method comprises the steps of obtaining a smooth angle sequence, comparing the smooth angle sequence with a calibration model, extracting an abnormal component if a comparison deviation is abnormal, and performing smooth processing to obtain a deviation vector; fusing the statistical characteristics of the light beam position and the deviation vector, predicting the light beam position, and generating a calibration parameter set if the light beam position exceeds a deviation range; extracting a key compensation item and optimizing a driving sequence, fusing frequency domain interference to generate a simulation track, and determining a stability index; and comparing the environment feature set, adjusting parameters to obtain an enhanced path model, and determining a final calibration scheme in combination with real-time feedback. According to the method, accurate calibration of the galvanometer in a multi-mode dynamic scene can be realized.

The invention relates to the technical field of battery safety management, in particular to a battery thermal runaway early warning method and system based on multi-dimensional feature fusion. The method comprises the following steps: collecting thermal characteristic data, gas characteristic data and electrochemical characteristic data of a battery; calculating a temperature gradient vector and an isothermal consistency index by using the thermal characteristic data, and capturing abnormal changes of a battery hot spot region by using a local dynamic sampling increasing method; calculating an internal and external field temperature difference phase difference by utilizing the thermal characteristic data and the gas characteristic data, and judging a heat source attribute by combining a heat contribution ratio model; and calculating a cross-modal early response phase difference, constructing a phase difference feature matrix, combining the temperature gradient vector, the heat source attribute and the phase difference feature matrix to form a fusion feature matrix, and inputting the fusion feature matrix into a dynamic weight fusion discrimination model to calculate a thermal runaway risk probability. According to the invention, based on a multi-dimensional feature fusion method, the thermal runaway risk probability is calculated in real time, and the early-stage accurate early warning of the thermal runaway of the battery is realized.

The invention provides a digital modeling method for enhancing connectivity of a low-porosity rock core, a medium and equipment, and relates to the field of digital modeling of rock cores, and the method comprises the following steps: reconstructing a Gaussian random field based on SAXS data to obtain an initial model of a three-dimensional digital rock core pore structure; extracting pore center points of the initial model, and constructing an initial network connecting all the pore center points; based on a minimum spanning tree algorithm, identifying mutually isolated pore clusters in the initial network, and selecting a most efficient seepage path skeleton connected with the isolated clusters; based on the seepage path skeleton, constructing a throat with fractal characteristics; and according to the total volume of the throat, performing morphological corrosion operation on an original pore area in the initial model, embedding the constructed throat into the corroded model, and then performing controllable morphological expansion operation until the volume variation of the final model is smaller than a preset error threshold. According to the method, the reconstruction precision of the digital rock core in microstructure and macroscopic connectivity is effectively improved.

The invention discloses an efficient optical flow estimation method and device based on Mama, and the method comprises the steps: carrying out the normalization and size alignment of two adjacent frames of images, and extracting the dense features of a fixed down-sampling rate through a shared weight convolution encoder; the two-frame features are sent to a multi-level feature enhancement module, an intra-frame modeling unit and a cross-frame interaction unit are cascaded and matched with channel reforming and residual error correction, and enhanced features are obtained; constructing a four-dimensional cost body on a low resolution, performing probability normalization along a target coordinate dimension, weighting a target coordinate grid according to a probability to obtain a corresponding coordinate, and subtracting the corresponding coordinate from a source coordinate to obtain an initial optical flow; and carrying out attention-guided space fusion on the initial optical flow and context and local correlation, sending the fused optical flow to a differential Mama-based autoregressive refinement module, carrying out iterative updating according to a small number of fixed steps, recovering to a target resolution through convex combination up-sampling, and outputting a final optical flow. According to the method, the optical flow field can be accurately estimated under the conditions of low complexity and low time delay.

The invention relates to a wellbore multiphase flow model numerical solution and gas-liquid distribution state inversion method and system, and belongs to the technical field of petroleum engineering, and the method comprises the steps: 1, constructing and training a physical information neural network for drilling wellbore multiphase flow dynamic simulation and overflow gas distribution state inversion; determining input and output of the physical information neural network; determining a loss function of the physical information neural network; training a physical information neural network; 2, designing an adaptive optimization algorithm, optimizing the final solution precision and convergence speed of the physical information neural network, and obtaining an adaptive physical information neural network; designing an adaptive activation function; designing a self-adaptive sampling mechanism based on residual errors; 3, based on the self-adaptive physical information neural network, numerical solution and gas-liquid distribution state inversion of the shaft multiphase flow model are achieved. According to the method, the problem that a traditional numerical method usually needs high-precision grid division and a large number of computing resources is effectively solved.

The invention discloses a complex terrain three-dimensional modeling and earthwork volume calculation method based on a multi-source fusion point cloud, belongs to the technical field of surveying and mapping and engineering surveying, and mainly solves the problems of low terrain modeling precision and insufficient earthwork calculation efficiency in a complex scene. The method comprises the following steps: constructing a ground-air integrated multi-source sensing network to synchronously acquire laser point cloud, multi-view images and positioning data; adopting PointNet + +-based initial registration and multi-scale ICP fine registration fusion to generate a unified point cloud; combining semantic segmentation and penetration probability filtering to accurately extract a digital elevation model; constructing a similar triangular prism voxel model by using a constrained Delaunay triangulation network; and finally, the earth volume is rapidly calculated by adopting a GPU parallel voxel cutting and filling algorithm, so that high-precision terrain modeling and rapid engineering quantity calculation are realized, and the method can be efficiently and reliably applied to large-scale projects such as roads and mines.

The invention belongs to the technical field of deep learning, and particularly discloses an ultra-precision full-field displacement measurement method and system based on a convolutional variational auto-encoder. Comprising the following steps: acquiring a video when a to-be-detected structure is subjected to vibration deformation, and selecting a picture of a deformation position to construct a data set; training a deep learning model of the convolutional variational auto-encoder based on the data set; reconstructing the gray value of the original image by using the trained deep learning model to obtain a gray value containing infinitesimal displacement information; and carrying out displacement calculation on the reconstructed image by utilizing an optical flow method, and realizing ultra-precision displacement calculation on the to-be-measured structure according to gray value conversion. According to the method, the problem that the infinitesimal displacement smaller than the sensitivity limit is difficult to measure is solved, the problem that the to-be-measured structure is blocked and cannot be measured is solved by utilizing the characteristics of the generative deep learning model, and the basic data precision of displacement measurement is remarkably improved.

The invention relates to the technical field of pipeline detection, and discloses a pipeline weld defect analysis method and device based on magnetic flux leakage detection.The method comprises the steps that a composite excitation signal is generated and optimized through a multi-frequency excitation source, a magnetic flux leakage attenuation coefficient and scattering characteristics are calculated in combination with pipeline parameters, and if the attenuation coefficient exceeds a preset attenuation threshold value, the pipeline weld defect is detected; if yes, adjusting the excitation signal for optimization, extracting high-frequency transient and low-frequency stable features of the optimized magnetic flux leakage signal, and generating a preliminary feature vector of the weld defect; mapping the preliminary feature vector to a three-dimensional defect space position, generating a space distribution diagram containing the defect position, optimizing the preliminary feature vector to make a fuzzy region clear, and obtaining the depth and direction of the defect; and according to the depth and direction of the defect, analyzing the spatial distribution characteristics of the preliminary feature vectors, determining the type of the defect, dynamically adjusting excitation signal parameters for optimization, recalculating the attenuation coefficient of the signal, extracting the optimized scattering features, and obtaining a high-precision weld defect positioning result.

The invention provides a data correction method and system combined with lattice structure additive manufacturing process characteristics, and the method comprises the steps: firstly employing a Newton iteration method, taking the radius of a pillar as a variable, optimizing the relative density of an iteration target and generating a lattice structure geometric model under the condition that a process constraint condition is satisfied, and extracting actual geometric parameters after forming through CT scanning, the elastic modulus is corrected through the pillar diameter deviation and the defect volume fraction, a compression failure mechanism is combined, a density-related failure criterion is introduced, a nonlinear relation with the relative density is constructed through specific energy absorption data, material plasticity parameters are inversely optimized, and a defect coupling evaluation model is constructed according to the surface powder sticking rate and the pillar diameter deviation. And establishing a Gaussian mixture model based on a stress-strain curve to screen out abnormal data, and finally fusing the parameters to construct a data correction model. The dot matrix structure design precision can be improved, and then a high-quality data basis is provided for performance prediction-structure design two-way feedback.

The invention provides a composite board detection method and system, and relates to the technical field of board detection, and the method comprises the steps: carrying out the ultrasonic scanning of a composite board, dividing the composite board into a plurality of acoustic characteristic layers, building a sound wave propagation model based on a recursive transfer matrix method, and calculating the reflection coefficient and transmission coefficient of each layer interface. A deep learning algorithm is combined with physical constraints to dynamically correct acoustic parameters, the corrected parameters are substituted into a model reconstruction signal, and defects are accurately positioned. And performing grid division on the defect position to analyze stress field distribution, and determining a stress concentration position. And fine scanning is carried out on a stress concentration area by adjusting probe parameters, and high-resolution data is obtained to update defect distribution information. And finally, outputting detection data including defect positions, stress concentration distribution and hazard levels, and realizing accurate detection and evaluation of the defects of the composite board.