2122 results about "Nondestructive testing" patented technology

The invention provides a multi-mode ultrasonic fusion pressure vessel weld defect nondestructive testing method and system, and relates to the technical field of nondestructive testing. According to the method, geometric parameters of a welding seam are obtained through three-dimensional laser scanning, and an optimal scanning parameter set is generated; driving ultrasonic phased array equipment to scan for one time and synchronously acquire shear wave full-matrix capture and longitudinal wave linear scanning data; performing energy flow angular spectrum analysis and envelope analysis on the bimodal data, extracting defect feature parameters and constructing a three-dimensional feature tensor; carrying out multi-dimensional feature fusion by adopting Tucker decomposition, and enhancing a core tensor through physical modeling; generating three types of defect indication diagrams including a defect existence possibility diagram, a defect relative scale diagram and a defect space orientation diagram from the enhanced feature tensor; and the three types of indication diagrams are visually presented for comprehensive interpretation of detection personnel. Through multi-modal data fusion and physical modeling enhancement, the defect identification accuracy and detection efficiency are remarkably improved, the false alarm rate is reduced, and reliable technical support is provided for pressure vessel welding seam safety detection.

The invention relates to the technical field of precision manufacturing nondestructive testing and machine vision image processing, in particular to a titanium alloy ring piece surface microcrack defect detection system based on machine vision, which comprises an image acquisition module for acquiring a to-be-processed image data set; the manifold calibration module is used for acquiring a main direction field of background textures and converting the to-be-processed image data set into a standard space image with aligned texture flow; the sparse decomposition module is used for acquiring a shear wave coefficient and decomposing the shear wave coefficient into a low-rank component matrix and a sparse component matrix; the reconstruction judgment module is used for generating a microcrack defect distribution diagram, obtaining a residual image and generating a final defect distribution diagram; the self-adaptive feedback module is used for adjusting the sparsity constraint weight in the robust principal component analysis algorithm; according to the method, the problem of signal aliasing caused by frequency overlapping of cracks and background textures is effectively solved, and the detection sensitivity under the strong texture background is remarkably improved.

The invention discloses a multi-mode fruit sugar degree nondestructive testing method, device and system and a medium, and the method comprises the steps: preprocessing an input fruit near infrared spectrum image and a fruit visible light image to obtain a one-dimensional near infrared spectrum input image tensor and a two-dimensional visible light input image tensor; a pre-trained multi-modal deep learning fusion model is utilized to obtain the predicted fruit sugar degree, and the multi-modal deep learning fusion model is pre-trained to establish an input near infrared spectrum input image tensor and a visible light input image tensor. The method comprises a spectral feature extraction network, an image feature extraction network, a multi-modal feature fusion network and a regression prediction network. The objective of the invention is to solve the problem of limited prediction precision caused by the fact that single spectral information is susceptible to noise, illumination conditions, peel thickness, water content and other factors, and improve the accuracy of fruit sugar degree nondestructive testing.

The invention relates to the technical field of nondestructive testing, and discloses a bimetal composite pipe three-dimensional reconstruction method and system based on multi-source data fusion. The method comprises the following steps: acquiring magnetic flux leakage signals and thickness data of the bimetal composite pipe in a high-pressure environment, and preprocessing the magnetic flux leakage signals and the thickness data to obtain a clean multi-source signal data set; performing time domain and frequency domain feature alignment on the data set to generate a fusion data matrix; extracting a preliminary defect feature set through multi-layer convolution processing; performing classification training on the defect features to obtain a defect type classification result containing confidence scores; if the crack exists, depth fitting is carried out to quantify the crack depth; if the preset risk threshold value is exceeded, generating a three-dimensional defect distribution model and evaluating connectivity; and finally, outputting a quantitative evaluation report of the pipeline risk level. According to the method, efficient fusion of multi-source data, intelligent identification of defect types and three-dimensional visual reconstruction are realized, and the accuracy and evaluation efficiency of pipeline defect detection are remarkably improved.

The invention relates to the technical field of intelligent detection, and discloses a building quality evaluation method and system based on concrete nondestructive testing and a storage medium. The method comprises the steps that a piezoelectric ceramic sensor array is arranged to collect micro-vibration response signals, and an original vibration data set is obtained; extracting an energy distribution coefficient of each frequency band by using a wavelet packet decomposition algorithm, and constructing a damage feature vector matrix; establishing a physical constraint neural network model, and outputting a damage variable time sequence; fusing the damage variable with ultrasonic and rebound data, and calculating comprehensive strength and damage degree indexes; and calculating the remaining service life by using a time sequence prediction algorithm, and generating an evaluation report. According to the method, the technical problem that the existing concrete nondestructive testing technology cannot realize microstructure damage evolution dynamic monitoring and residual life prediction is solved, and the accuracy of building quality evaluation and the scientificity of predictive maintenance decision are improved.

The invention relates to the field of optical nondestructive testing, and discloses a visualization method for detecting mango quality based on hyperspectrum, which comprises the following steps of: firstly, selecting mango samples, and simulating different mango ripening stages by dividing the mango samples into a plurality of groups of samples; acquiring a high-dimensional spectral image by using a hyperspectral imaging system, and measuring the hardness, the soluble solid content and the titratable acid content; correcting and extracting the high-dimensional spectral image to obtain and optimize the average reflection spectrum of the sample, and establishing a mango maturity classification model and a quality regression prediction model; performing characteristic wavelength contribution degree analysis and screening, and optimizing a quality regression prediction model by adopting a key wave band; the method is applied to each pixel point of a high-dimensional spectral image, a two-dimensional distribution diagram is generated, and mango quality visualization is achieved. The method solves the problems that in the prior art, rapid and lossless judgment of the mango ripening stage cannot be achieved, accurate prediction and visual distribution of key internal quality indexes cannot be achieved, and intelligent grading and quality control of picked fruits are not ideal.

The invention provides an energy efficiency optimization and predictive regulation and control method for a building electromechanical system based on digital twinning. The method comprises five layers of architectures including a physical entity layer, a data acquisition and interaction layer, a digital twinning model layer, an energy efficiency analysis and optimization layer and a predictive regulation and control layer. The dynamic digital twinborn body interacting and synchronizing with the physical electromechanical system in real time is constructed, fundamental transformation from passive lag control to prospective predictive control is achieved, a high-fidelity virtual model is used as a nondestructive testing platform, countless operation strategies can be safely and rapidly simulated and evaluated in a virtual space, and therefore accurate optimization is achieved, and the real-time dynamic digital twinborn body is obtained. The overall energy efficiency of the system is remarkably improved, the dilemma that the traditional method makes consideration and loses others is eliminated, and the leap-type improvement of the comprehensive operation quality is achieved.

The invention relates to the technical field of electrical equipment detection, and discloses a terahertz time-domain spectroscopy-based GIS insulating material nondestructive testing method, which comprises the following steps: acquiring GIS insulating material time-domain signals by using a reflection-type terahertz time-domain spectroscopy (THz-TDS), and separating and reconstructing defect echoes based on electromagnetic simulation modeling in combination with wavelet transform, STFT and a transmission matrix method; multi-dimensional features are extracted, SVM and RBFNN are trained after dimensionality reduction, and a classification and quantitative regression model fusing physical constraints is constructed; and inputting a signal of a sample to be detected into the model to realize rapid, non-contact and accurate detection on the type, size and depth of the defect. The method overcomes the defects that traditional detection is low in efficiency, has damage or is difficult to quantify, the signal-to-noise ratio, the sensitivity and the automation level are remarkably improved, the method can be widely applied to GIS production quality control, installation acceptance, operation and maintenance monitoring, health assessment and life prediction, and powerful support is provided for safe and reliable operation of an electric power system.

The invention discloses an ultrasonic weak scattering enhancement detection system and method for micro leakage of a liquid pipeline, and relates to the technical field of industrial pipeline state monitoring and nondestructive testing. By performing enhancement processing on a bubble weak scattering signal generated by micro-leakage in the pipeline and inverting bubble parameters, online monitoring, graded early warning and positioning analysis of micro-leakage of the liquid pipeline are realized. The system comprises a clamping type transducer assembly and a control processing unit which are electrically connected, the transducer assembly is mounted on the outer wall of the pipeline; the control processing unit is configured to collect a baseline reference signal and construct a background model; transmitting a coding excitation signal, and driving an ultrasonic transducer assembly to transmit ultrasonic waves; synchronously acquiring ultrasonic echoes or transmission signals; carrying out preprocessing and weak scattering enhancement processing on the original sampling signal; inverting a bubble parameter and a gas content parameter; carrying out leakage judgment, and outputting micro leakage monitoring quantity and graded alarm information; the invention further discloses a method based on the system.

The invention discloses a welding slag ladle for containing copper slag and a design method. The welding slag ladle is suitable for containing, cooling and inverted placing procedures of high-temperature copper slag. The cinder ladle comprises a cylinder body, a ladle bottom, rib plates, trunnions and supporting legs, the cylinder body and the ladle bottom are formed by welding high-temperature-resistant and high-strength rolled plates, the thickness of the cylinder body is 70-100 mm, and the thickness of the ladle bottom is 80-120 mm; setting an inclination angle beta of 11.5-21 degrees relative to the center line of the bus of the cylinder body, and controlling the roughness of the inner wall to be less than or equal to 12.5 microns so as to reduce the separation resistance after the copper slag is cooled; thickness combination, stress distribution, deformation and rib plate arrangement are optimized through structural mechanical simulation, and it is ensured that the structural strength meets the requirements under high temperature and thermal cycle loads; full penetration weld joints are adopted, nondestructive testing is carried out, and the welding reliability is guaranteed; compared with a traditional casting cinder ladle, the mass of the cinder ladle is reduced by 10%-15%, the deslagging efficiency is improved, the service life is prolonged to more than two times of the original service life, and the cinder ladle has the advantages of being light in structure, high in bearing capacity, high in deformation resistance and good in maintainability.

The invention discloses a lithium battery laser welding detection method, and belongs to the technical field of nondestructive testing. The method comprises the following steps: acquiring a three-dimensional path point sequence of a to-be-detected welding seam; controlling the transient thermal excitation unit to apply transient thermal excitation along the path; a high-speed multispectral imaging unit is used for synchronously collecting the thermal radiation attenuation process of the weld joint under at least two different spectral bands, and a space-time spectrum data cube is formed; and finally, inputting the data cube into an artificial intelligence model for defect identification. The system correspondingly comprises a three-dimensional visual sensor, a transient thermal excitation unit, a high-speed multispectral imaging unit and a synchronous control and data processing unit. According to the method, different types of welding defects such as air holes and incomplete fusion can be effectively distinguished by analyzing the heat diffusion characteristic difference under different spectral bands, the interference of uneven surface emissivity is inhibited, and the online detection accuracy and reliability of the three-dimensional complex welding seam are improved.

The invention discloses a steel wire rope nondestructive testing method and system based on sensing and physical feature fusion, and belongs to the technical field of steel wire rope nondestructive testing. The method comprises the following steps: firstly, carrying out saturation magnetization on a steel wire rope through an axial Gaussian difference excitation probe so as to improve the signal-to-noise ratio of a defect magnetic flux leakage signal; and meanwhile, the instantaneous acceleration is synchronously acquired through the inertial measurement unit. And establishing a speed-signal model based on the law of electromagnetic induction, and performing dynamic speed compensation on the magnetic flux leakage original voltage signal. And then, quantitatively extracting characteristic parameters with clear physical significance, such as peak voltage, maximum gradient, full width at half maximum and the like, from the standardized magnetic flux leakage signal, fusing the characteristic parameters with deep characteristics extracted by the deep learning model, and inputting a fused characteristic vector into a full-connection neural network and a classifier to realize defect type identification and degree evaluation. According to the invention, high-precision and interpretable intelligent detection and residual life prediction of steel wire rope defects can be realized, the detection reliability is obviously improved, and predictive maintenance is supported.

The invention relates to the technical field of nondestructive testing and performance analysis of engineering materials, in particular to a nondestructive testing and performance degradation analysis method for bridge structure materials. The method comprises the following steps: acquiring material nondestructive testing monitoring data, and executing unified alignment, noise portraying and quality labeling; carrying out combined noise reduction and component evaluation, and generating a noise reduction parameter snapshot; extracting a marginal spectrum and characteristic energy ratio, and establishing a material characterization characteristic index dictionary; calculating a variation index, judging material performance degradation, and constructing a supervision signal; and training the prediction network, generating a trend and a residual error, and completing early warning fusion by adopting a time neighborhood consistency strategy to obtain a material performance degradation early warning level sequence. According to the method, continuous nondestructive monitoring and quantitative characterization of material levels are realized, characteristics and parameters are traceable, cross-channel and cross-time evaluation is consistent, prediction and early warning are sensitive and stable, and the method is suitable for long-term service safety evaluation.

The invention discloses an intelligent monitoring and evaluating system for the metal health state of a generator set, and belongs to the technical field of state monitoring of power generation equipment. The system comprises a data acquisition and preprocessing module, a data fusion and feature extraction module, a metal health state evaluation module, a residual life prediction and fault tracing module and a visual intelligent decision support module. The method comprises the following steps of: extracting cross-scale damage characteristics by fusing macroscopic operation data and microscopic nondestructive testing signals; calculating a comprehensive health index by adopting a physical mechanism model and data-driven model adaptive weighted fusion mode; dynamic residual life prediction and failure reason intelligent traceability are realized; and finally, graded early warning and maintenance decision suggestions are provided through a three-dimensional visual interface. According to the method, the problems of single monitoring dimension, model isolation and static prediction in the prior art are solved, and comprehensive, high-precision and interpretable evaluation and predictive maintenance support of the health state of the metal part are realized.

The invention belongs to the technical field of road engineering roadbed nondestructive testing, discloses a roadbed humidity nondestructive testing method based on wireless radio frequency signal multi-parameter inversion, and aims to solve the problems that a traditional roadbed humidity testing method is destructive, low in efficiency and discontinuous in monitoring or detection parameters are easily interfered. The method specifically comprises the following steps of: firstly, embedding an RFID tag and acquiring an on-site received signal strength indication value; then establishing a relation model of the received signal strength indication value and the roadbed moisture content, and extracting characteristic parameters only influenced by the moisture content; then, constructing a neural network model, taking the extracted characteristic parameters as input, and utilizing field data to train the model to invert the roadbed moisture content; and finally, the spatial distribution of the moisture content of the roadbed is obtained by combining the distribution of the test points and the roadbed structure information, so that the efficient, non-contact and continuous monitoring of the humidity state of the roadbed is realized.

The invention discloses an electromagnet electromagnetic ultrasonic thickness measurement method and system based on an anti-collision unmanned aerial vehicle, and relates to the field of nondestructive testing, and the method comprises the steps: obtaining the coordinates of a preset detection point, controlling the flight path of the unmanned aerial vehicle, and determining the safety position information of the unmanned aerial vehicle located near the preset detection point; the motion of the multi-degree-of-freedom mechanical arm is controlled through a servo motor and an encoder, contact impact is reduced through a buffer mechanism, and attitude information of the electromagnetic ultrasonic sensor unit aligned with the surface of the to-be-detected object is determined; the magnetic attraction force is adjusted in real time through a closed-loop feedback system, and a linear frequency modulation pulse is adopted as an excitation signal to obtain an echo signal containing acoustic-electromagnetic coupling interference; acoustic-electromagnetic coupling interference is corrected and processed in an auxiliary mode through a multi-mode signal separation algorithm and machine learning, and final thickness data is obtained; and transmitting the final thickness data to a cloud platform, and providing a data visualization interface and an alarm function on the cloud platform. According to the invention, accurate thickness detection and remote monitoring of a complex structure material can be realized.

The invention relates to a glass capillary microscopic flaw detection method and system, and relates to the field of material nondestructive testing, and the method comprises the following steps: collecting a capillary detection signal; when the capillary detection signal is consistent with the detection starting signal, collecting a capillary serial number; determining a sound detection number according to the capillary number; obtaining a standard reaction characteristic based on the capillary serial number and the sound detection serial number; generating and transmitting a corresponding sound wave signal to the capillary tube based on the sound detection number, and collecting sound reaction characteristics; when the sound reaction characteristics are inconsistent with the standard reaction characteristics, controlling vibration auxiliary equipment to apply auxiliary vibration to the capillary tube, and collecting post-vibration sound reaction characteristics; if the post-vibration sound reaction characteristics are inconsistent with the sound reaction characteristics, a capillary tube cleaning prompt is reported; if the after-vibration sound reaction characteristics are consistent with the sound reaction characteristics, capillary tube defect prompts are reported. The method has the effect of improving the detection accuracy of the glass capillary.

The invention provides a steel wire rope nondestructive detection device and method, the detection device comprises a shell, a signal processing module and a plurality of probes, the shell is provided with a detection cavity, the detection cavity penetrates through the shell along the axial direction of the shell, and the detection cavity is used for placing a steel wire rope; the plurality of probes are arranged on the inner wall of the detection cavity, and each probe is connected with the signal processing module through a wire; the probe comprises an excitation coil and two detection coils, the two detection coils are located on the two sides of the excitation coil, and the excitation coil and the detection coils are coaxially arranged, so that the detection coils can be most effectively coupled with eddy current magnetic field signals generated by the excitation coil and modulated by defects, and the signal acquisition efficiency is improved.

The invention relates to the technical field of nondestructive testing, in particular to a multi-modal-based steel box girder complex weld defect detection system which comprises a multi-modal data acquisition module, a data processing and intelligent identification module and a control and integration platform. The multi-modal data acquisition module comprises an ultrasonic phased array probe, an X-ray imaging sensor, a visual imaging subunit and a positioning unit, and is used for synchronously acquiring ultrasonic, ray, visual images and spatial position information of a welding seam; the data processing and intelligent identification module is used for carrying out space-time registration and feature level fusion on the multi-modal data and realizing defect identification, positioning and quantification by utilizing a deep learning model; and the control and integration platform drives the movement mechanism to realize automatic scanning and provides result visualization and digital twin interfaces. According to the invention, the detection precision, efficiency and reliability of the internal and surface defects of the complex structure of the steel box girder can be improved.

The invention relates to the technical field of edible mushroom detection, and discloses an edible mushroom quality nondestructive detection system based on deep learning. The system comprises an image acquisition module, a feature extraction module, a deep learning analysis module and an abnormity early warning module. The image acquisition module is used for acquiring appearance image data of the edible mushroom sample; the feature extraction module receives the detection instruction, sets analysis windows based on the appearance image data, and extracts and analyzes quality feature parameters in each window; the deep learning analysis module classifies and identifies the quality of the edible mushrooms according to the quality characteristic parameters, and performs comprehensive evaluation after detection is finished; and the abnormity early warning module monitors the color uniformity, the form integrity and the surface defect degree of the edible mushrooms and carries out abnormity early warning. According to the system, automation and intelligentization of edible mushroom quality detection are realized, subjective interference of manual detection can be effectively avoided, subtle feature differences are captured, different detection requirements are met, quality abnormity is early warned in time, and the detection efficiency and the result stability are improved.

The invention belongs to the technical field of nondestructive testing, and relates to a method for imaging residual oil in a powder metallurgy component based on pulse thermal excitation, which comprises the following steps: applying preheating disturbance pulse to a target area to generate a pre-excitation thermal field; applying a main detection pulse to the same target area at an interval of a preset time lag, and generating a transient composite hot spot; obtaining the whole process from formation to cooling dissipation of the transient composite hot spot, and generating a dynamic thermal field image sequence; images in a specific time window are extracted, and a key stage hot spot evolution image set is generated; the physical state of residual oil in the target area is judged, and a residual oil state label is generated; performing mapping association on the residual oil state label generated at each detection position and the two-dimensional coordinate of the residual oil state label to generate a residual oil state visual image; according to the invention, the problem of missing detection or misjudgment caused by difficulty in effectively amplifying weak thermal physical property difference between oil liquids in different states due to single excitation mode in the existing thermal imaging technology is solved.

The invention relates to the technical field of power transmission line detection devices, and particularly discloses a lightweight power transmission line X-ray imaging detection device which comprises a rack, two groups of walking mechanisms and a detection mechanism. The rack is provided with a telescopic extension rod, and the detection mechanism is arranged at the tail end of the extension rod. The walking mechanism comprises a walking arm and a walking wheel which can synchronously move in the Y direction, and the detection mechanism comprises an imaging plate and a ray source which can be adjusted in multiple directions. According to the device, by simplifying the structure of the walking arm, adopting the translation driving device, optimizing the layout of the detection mechanism and the like, the light weight of the whole structure is realized. According to the design, the weight and the size of the device are reduced, transportation and high-altitude deployment are facilitated, meanwhile, the walking stability is guaranteed through reasonable gravity center arrangement, and nondestructive testing on the power transmission line can be efficiently completed.

The invention relates to the technical field of nondestructive testing and intelligent manufacturing, and provides a weld defect intelligent identification system based on multi-source data fusion. The system synchronously obtains a visual image, a three-dimensional point cloud and ultrasonic array data of a welding seam through a multi-source data acquisition module; a hidden danger density field construction module generates surface and internal hidden danger density fields; the cross-space association and defect topology extraction module associates and binds high-density areas meeting spatial continuity and gradient consistency conditions in the two density fields into a cross-space association cluster through density gradient consistency analysis and a preset topology mode template, and generates a defect sign vector; and finally, the defect decision module outputs the defect type, position, size and confidence coefficient based on the sign vector. According to the method, multi-source heterogeneous data is innovatively unified to hidden danger density field characterization, collaborative, accurate and explainable recognition of internal and external defects of the weld joint is achieved through topology analysis of a cross-space correlation cluster, and the limitation of a traditional single detection means is overcome.

The invention discloses a cable trench fireproof cover plate performance detection device and method based on multi-band electromagnetic waves. The device comprises a microwave detection module, a terahertz imaging module, a high-precision two-dimensional mechanical scanning platform, a synchronous control and data fusion unit and a comprehensive data analysis platform. The microwave module is used for transmitting and receiving 1-10GHz electromagnetic waves to realize deep structure detection; the terahertz module works at a frequency band of 0.1-3 THz and is used for acquiring near-surface time-domain spectral information. And the scanning platform drives the modules to perform two-dimensional rasterization scanning on the surface of the cover plate. And the synchronous control and data fusion unit coordinates the time sequence action of each module to ensure the time-space consistency of data acquisition. And the comprehensive data analysis platform performs fusion processing on the multi-source data through an inversion and imaging algorithm to generate a comprehensive evaluation report covering internal macroscopic defects, near-surface microdefects and coating thickness. According to the invention, through collaborative integration of microwave and terahertz technologies, multi-scale and integrated nondestructive testing and performance evaluation of the fireproof cover plate from a deep layer to a surface layer are realized.

The invention relates to the technical field of pressure vessel health monitoring and nondestructive testing, in particular to an electromagnetic acoustic surface wave-based pressure vessel intergranular corrosion non-contact detection system, which comprises the following modules: a multi-field coupling electromagnetic acoustic surface wave detection module, which comprises a plurality of detection unit arrays, each detection unit comprises a magnetic biasing module and an impedance matching module and is used for generating a local magnetic field on the surface of the pressure vessel and optimizing signal excitation and receiving; and the signal acquisition and preprocessing module is used for acquiring the surface wave signal of the detection module and carrying out denoising, filtering, curved surface coupling difference correction and lift-off correction on the signal to obtain high-quality signal data. According to the method, non-contact quantitative detection of the intergranular corrosion of the pressure vessel is realized through multi-field coupling electromagnetic acoustic surface wave detection and microscopic and macroscopic mapping and inversion, so that the problem that early corrosion is difficult to quantify due to the fact that contact ultrasound is mostly adopted in traditional pressure vessel detection and grain boundary micro-damage cannot be effectively identified is solved.

The invention discloses a water turbine blade coating binding force evaluation method and device, and relates to the technical field of hydroelectric generation. The method comprises the following steps: synchronously acquiring multi-modal nondestructive testing data of a coating area of a water turbine blade to be evaluated; inputting the multi-modal nondestructive testing data into the coating binding force prediction model for binding force prediction and integration to obtain a coating binding force spatial distribution diagram; the coating binding force spatial distribution diagram is input into a high-fidelity digital twin body for coupling simulation and life analysis, and a coating remaining life distribution diagram is obtained; inputting the coating binding force spatial distribution diagram and the coating residual life distribution diagram into an intelligent maintenance decision model for decision generation to obtain a blade maintenance decision; and carrying out visual display on the coating binding force spatial distribution diagram and the coating residual life distribution diagram, and generating a blade coating binding force evaluation report. The problems of low evaluation precision, single information dimension and disjunction between evaluation and decision in the prior art are solved.

The invention provides a nondestructive testing method and system for the quality of a filling body based on ultrasonic pulse speed, and belongs to the technical field of green mining of deep mineral products and quality detection of filling bodies. The system integrates an ultrasonic pulse wave velocity test module, a data processing module, a result output module and an adaptive calibration module which work cooperatively. According to the method, a UCS-UPV power function quantitative model is constructed through a three-factor multi-level test, in-situ-indoor deviation is eliminated in combination with an environment + stress two-dimensional adaptive correction coefficient, and filling body quality grading and evaluation are achieved through on-site ultrasonic detection and strength inversion. According to the method, nondestructive detection is adopted, various stope scenes from shallow parts to deep parts are adapted, a quality control closed loop and a data tracing system can be formed, stope stability and mining safety are effectively guaranteed, and the technical problems that a traditional detection method is high in destructiveness, high in cost and poor in timeliness, and existing ultrasonic detection is insufficient in adaptability and limited in precision are effectively solved.

The invention relates to the technical field of intelligent nondestructive testing, in particular to a multi-modal radar pipe network leakage defect detection method and system, and the method comprises the steps of multi-modal data collection, multi-modal data preprocessing, image feature extraction, multi-modal feature fusion, leakage detection and result output. The radar pipe network B-Scan image and the corresponding text data are collected, fusion of multi-modal data is achieved, effective information in different types of data is fully utilized, the precision and reliability of leakage detection are improved, the improved lightweight YOLOv8 model is adopted for image feature extraction, and the detection accuracy is improved. By adding a depth separable convolutional layer and introducing a space attention mechanism, the sensitivity to the leakage region features is enhanced, and the key features in the image can be extracted more accurately.

The invention relates to the field of industrial nondestructive testing and intelligent image analysis, in particular to a nondestructive testing digital image defect automatic positioning method and device. The system comprises an image preprocessing module, a multi-scale feature extraction module, an attention fusion positioning module and a defect mask output module. The method comprises the following steps: firstly, carrying out normalization and enhancement processing on an original digital ray image, then extracting multi-level image features through a convolution structure with a multi-scale receptive field, introducing a channel attention mechanism to carry out weighted enhancement on a defect area, and carrying out layer-by-layer up-sampling and fusion on a feature map by adopting a decoder to obtain a multi-level image; and finally outputting a pixel-level defect mask pattern. The system supports end-to-end training, has the advantages of high positioning precision, low calculation complexity, high adaptability and the like, and is suitable for a high-precision defect detection scene.

The invention relates to the technical field of metal pipe nondestructive testing, and discloses a method for detecting inner and outer surface defects of a titanium alloy pipe. The method comprises the following steps: preprocessing and fusing signals of a plurality of detection probes to generate a multi-modal data set; and performing dynamic space registration based on the real-time motion state of the pipe, and mapping data to the surface of a unified three-dimensional solid model. Mutually overlapped local analysis areas are divided, feature vectors are extracted, and circumferential features of the same axial position are aggregated to construct an annular feature sequence; and performing cross-domain association matching on the sequence and a corresponding manufacturing process parameter sequence, and identifying an abnormal mode related to the specific process event. According to the method, signal dislocation caused by pipe movement can be overcome, accurate space positioning and morphology reduction of defects are achieved, and defect source tracing can be achieved through association of characteristics and the technology.