7538results about "Processing detected response signal" patented technology

The invention discloses a polyethylene plastic hollow plate defect detection method and system, and relates to the technical field of nondestructive testing, the system is composed of a plurality of functional modules, and the system comprises: a multi-mode sensing module for collecting multi-source data including a hollow plate surface image, three-dimensional morphology data and an ultrasonic reflection signal; the historical database construction module is used for performing space-time alignment and feature level fusion on the multi-source data to generate a composite defect feature matrix; collecting ultrasonic signal waveforms of known defects, extracting characteristic parameters, and establishing a historical waveform library; calculating a characteristic parameter classification range of each type of defects based on a historical waveform library; judging whether the detection waveform parameter falls into a classification range corresponding to the processing noise or not, and if so, rejecting the signal; otherwise, keeping; and the defect area identification module is used for dividing the surface of the hollow plate into uniform grids based on the surface image of the hollow plate, and counting the density of the residual ultrasonic detection sources in each grid.

The invention provides a technical condition prediction method for a tunnel civil engineering structure, and relates to the technical field of traffic control monitoring systems.A biological acoustic composite sensing system comprising a distributed optical fiber acoustic sensing system and microorganism sample collection and analysis is constructed, and acoustic depth mode characteristics are mined by applying a deep learning model; the microbial dynamic biomarker is analyzed and identified by using bioinformatics; multi-modal information, a domain knowledge graph and a causal inference algorithm are fused, and a dynamic causal network model for revealing an internal driving relation in the degradation process is constructed; further, training and applying an adaptive neural network prediction model based on causal driving and fusing physical and biochemical mechanism constraints, and carrying out probabilistic prediction on future technical conditions of the tunnel; and executing anti-fact reasoning by using the causal network and the prediction model, and generating and optimizing an intervention strategy of cooperation of physical measures and biochemical measures. According to the method, the accuracy and advance of tunnel structure state prediction can be remarkably improved.

The invention relates to the technical field of defect detection, and discloses a pipeline repairing method and system based on defect detection. The method comprises the following steps: performing acoustic detection on a pipeline based on a multi-frequency sound wave signal to obtain pipeline sound field data; carrying out electromagnetic wave scattering detection on the pipeline to obtain electromagnetic scattering data; constructing a sound field distribution diagram in the pipeline and an electromagnetic property distribution diagram on the surface of the pipeline; performing feature extraction on the sound field distribution diagram to obtain a first detection feature set, and performing feature extraction on the electromagnetic characteristic distribution diagram to obtain a second detection feature set; inputting the first detection feature set and the second detection feature set into a space-time diagram convolutional network model for pipeline defect diagnosis to obtain a defect position and a defect type; and performing causal reasoning and repair scheme matching on the pipeline according to the defect position and the defect type to obtain a target pipeline repair scheme, thereby improving the accuracy of pipeline defect detection, thereby improving the accuracy of pipeline repair.

The invention relates to the technical field of defect detection, and discloses a flange forging defect detection method and system. The method comprises the following steps: performing three-dimensional scanning and material acoustic characteristic measurement on a to-be-detected wind power flange to obtain layer partition sound isolation path data; generating an array element excitation control file of the double-array phased array ultrasonic detection system; applying the array element excitation control file to a double-array phased array ultrasonic detection system, and performing micro defect feature enhancement on a received echo signal to obtain a feature-enhanced signal data set; performing ultrasonic emission and data acquisition on the wind power flange to obtain a global detection data set; and inputting the global detection data set into the two-stage defect detection model for defect feature extraction and classification evaluation, and generating a defect detection evaluation report. According to the method, the detection rate and the classification accuracy of the micro forging defects are improved.

The invention provides an efficient flaw detection method of an ultrasonic flaw detector for automatic detection of a welding seam, and belongs to the technical field of welding seam detection.The efficient flaw detection method comprises the steps that firstly, a welding seam three-dimensional digital model is established, an optimal scanning path is determined, reflection echo signals are processed in real time through an adaptive beam forming algorithm, and defect features are extracted and subjected to quantitative analysis; and establishing a neural network model based on the defect characteristic spectrum, and performing deep learning training by using the sample library. A multi-angle scanning strategy is adopted to implement full-coverage detection, and cross scanning is applied in a key area to improve the detection probability. And calculating the accurate position of the defect by using a time-difference method, and constructing a defect three-dimensional distribution diagram by combining a multi-angle data fusion technology. The optimal filtering parameter is determined through the acoustic feature optimization function, and the defect signal-to-noise ratio is improved. And finally, a quality evaluation report is automatically generated based on a detection result, so that the problems of defect identification accuracy and detection efficiency in weld flaw detection are effectively solved.

The present application provides a method for pipeline defect detection using the magnetostrictive characteristics of ultrasonic guided waves, which relates to the technical field of pipeline defect detection, including: obtaining the geometric and material parameters of multi-layer heterogeneous pipelines, and establishing a mathematical model of the pipeline; according to the mathematical model, calculating the dispersion curve and modal characteristics of ultrasonic guided waves in the pipeline; selecting a suitable excitation frequency and guided wave mode, and optimizing the detection parameters of ultrasonic guided waves; presetting defects of different types and sizes in the pipeline, and analyzing the influence of defects on guided wave propagation characteristics through numerical simulation; building a multi-layer heterogeneous pipeline ultrasonic guided wave detection experimental system, collecting guided wave signals under different defect conditions, and extracting time-frequency domain characteristic parameters. The present invention can realize the effective detection and evaluation of internal defects of multi-layer heterogeneous pipelines at different temperatures, and provides an important technical means for ensuring the safe operation of pipelines.

The invention discloses a railway track damage detection method, and belongs to the technical field of railway track detection. According to the method, an image acquisition module and an ultrasonic detection module are installed at the bottom of a track detection vehicle, the detection vehicle is controlled to run, and track top face and side face image sequences and ultrasonic reflection signals are acquired; preprocessing the image, and respectively inputting the image into a deep convolutional neural network model and a support vector machine classifier to obtain a crack identification result and a wear level; processing an ultrasonic reflection signal, and judging a layering defect; and finally fusing the data, marking a damage position and generating a structured detection report. According to the method, the problems of incomplete detection, low precision and the like in the existing railway track damage detection are solved, efficient detection of track surface cracks, side abrasion and internal layering defects is realized through collaborative acquisition of the multi-modal sensor, intelligent algorithm processing and data fusion, and the comprehensiveness and reliability of detection are improved.

The invention relates to the field of circuit board quality detection, in particular to an aluminum circuit board surface treatment quality detection system and method. The method comprises the following steps: acquiring a real-time thermal imaging view of the aluminum circuit board; performing multi-point sampling calculation on the real-time thermal imaging view of the aluminum circuit board, and performing temperature distribution analysis so as to generate temperature distribution data of the aluminum circuit board; performing temperature rise distribution trend reconstruction based on the temperature distribution data of the aluminum circuit board, and performing abnormal region positioning so as to obtain a coating abnormal region; carrying out multidirectional ultrasonic pulse detection on the coating abnormal area, and collecting a multi-array sound wave signal; carrying out low-pass filtering processing on the multi-array sound wave signals, then carrying out three-dimensional projection remodeling, and constructing a three-dimensional sound wave projection drawing; and acoustic wave penetrating circuit board attenuation inversion is carried out on the three-dimensional acoustic wave projection drawing, and then surface defect analysis is carried out, so that an aluminum circuit board surface defect area is obtained. The method for accurately and efficiently detecting the surface defects of the aluminum circuit board is realized.

The invention relates to the technical field of data processing, and discloses a steel structure welding seam quality ultrasonic detection method and system. The method comprises the following steps: scanning a welding seam to obtain digital parameters, constructing a virtual model to generate an ultrasonic response database, optimizing a detection parameter forming scheme, executing distributed acquisition to obtain a signal matrix, extracting defect features by using a neural network, and outputting a digital detection report through transfer learning. According to the method, automatic optimization of detection parameters and accurate extraction and intelligent analysis of defect features are realized, the accuracy, efficiency and reliability of detection are improved, and the dependence on artificial experience is reduced.

The invention provides a surface crack detection method and system based on array surface waves, and relates to the technical field of crack detection, and the method comprises the following steps: carrying out noise reduction and enhancement processing on an original signal collected by a workpiece, establishing an environment compensation mechanism, extracting a key sonic path distance characteristic and a crack characteristic from the processed signal, the features are input into a neural network to predict the crack size, theoretical verification is carried out in combination with a microdynamics model, and a final detection result is obtained through fusion of a prediction result and a theoretical result.

The invention relates to the technical field of structural health monitoring and intelligent diagnosis, in particular to a structural fatigue damage identification method based on acoustic emission and deep learning, and the method comprises the steps: collecting a structural response signal under a fatigue load through an acoustic emission sensor array, inputting the structural response signal to a CNN-BiLSTM-Attention mixed deep learning model, and carrying out the recognition of the structural fatigue damage through the CNN-BiLSTM-Attention mixed deep learning model; the model extracts local time domain features through a dynamic adaptive convolution kernel, captures long time sequence dependence by using a bidirectional long-short-term memory network, focuses key damage features through a bimodal space-time attention mechanism, divides damage stages based on a nonlinear dynamic threshold algorithm of fracture opening amount, constructs a training data set of physical-data fusion, and performs dynamic time domain feature extraction. The learning rate is optimized by adopting a gradient sensitive cosine annealing algorithm, and the robustness of the model is improved in combination with an anti-noise and anti-loss function. The method integrates physical characteristics and an intelligent algorithm, and has the advantages of adaptive noise suppression, strong cross-domain generalization ability, high real-time performance and the like.

The invention provides a new energy battery internal microcrack detection method, which comprises the following steps: acquiring a battery internal three-dimensional structure image by adopting a high-resolution X-ray tomography technology, extracting a pole piece area through an image segmentation algorithm, and carrying out bending deformation simulation on a pole piece to obtain a pole piece internal stress distribution cloud picture; and combining the retrained health state evaluation and residual service life prediction model with battery electrochemical performance characterization data, coupling microcrack evolution and battery performance degradation, predicting a future crack propagation trend and a corresponding performance degradation condition of the battery, and guiding quality control in a battery design and production process.

The invention provides a prefabricated building quality intelligent detection method and system, and relates to the technical field of data processing, and the method comprises the steps: employing a deep residual network to carry out the feature fusion analysis of ultrasonic echo signals, so as to obtain a feature vector corresponding to each signal; according to the feature vectors and a time-frequency domain combined threshold segmentation algorithm, welding defect types are recognized, a defect space coordinate set is marked, a defect coordinate set containing four-dimensional information is formed, and the welding defect types comprise air holes, incomplete fusion and cracks; calculating dynamic offset parameters of the defect coordinate set relative to the theoretical axis of the cantilever beam in the defect-reference fusion model, wherein the parameters comprise a defect depth gradient, a transverse offset vector and a stress wave attenuation coefficient; and inputting the dynamic offset parameters into a pre-trained welding quality evaluation model to predict the service life of the structure. According to the invention, the residual service life of the fabricated building structure can be evaluated more accurately.

The invention provides a high-temperature chemical pipeline defect detection system and method based on magnetostrictive ultrasonic guided waves, and relates to the technical field of high-temperature chemical pipeline defect detection.The method comprises the steps that magnetostrictive excitation signals are applied, the propagation process of the guided waves in a pipeline is calculated through numerical simulation, and the amplitude and phase information of guided wave signals is obtained; performing time-frequency domain analysis on the guided wave signal, extracting group velocities and phase velocities of guided waves in different modes, determining frequency dispersion characteristics of the guided waves, and evaluating the influence of a high-temperature environment on guided wave propagation; processing the received guided wave signal by adopting a modal decomposition and reconstruction algorithm to obtain a corrected guided wave signal; and carrying out feature extraction and parameter estimation on the corrected guided wave signal to obtain the position, size and type information of the pipeline defect. According to the invention, the problem of multilayer heterogeneous pipeline guided wave detection in a high-temperature environment is solved, the accuracy and reliability of defect detection are improved, and an effective method is provided for pipeline health monitoring and safety assessment.

A device may include an inspection robot comprising a payload configured to interrogate an inspection surface in response to the inspection robot moving on the inspection surface, the payload comprising an ultrasonic (UT) sensor. A device may include a data verification controller, comprising: a verification data circuit structured to interpret verification data from the payload, an inspection status circuit structured to determine an inspection data quality value in response to the verification data; and a data verification circuit structured to perform a data verification operation in response to the inspection data quality value.

The present invention provides an intelligent recognition method based on the fusion of a defect pulse signal and an image, comprising: step 1, collecting data, and selecting ultrasonic A scan data and ultrasonic S scan data capable of characterizing defect information on the basis of data obtained by machine scanning a defect, wherein the ultrasonic A scan data is ultrasonic sequence data, and the ultrasonic S scan data is sector image data; step 2, pre-processing the ultrasonic A scan data and the ultrasonic S scan data; step 3, after pre-processing the ultrasonic A scan data and the ultrasonic S scan data, extracting feature information from the ultrasonic A scan data and the ultrasonic S scan as an index for distinguishing defects, and using the feature information respectively extracted from the ultrasonic A scan data and the ultrasonic S scan data as an input variable for classification; step 4, performing feature value evaluation and screening; and step 5, performing a classification task. The present invention is a recognition method for the fusion of two types of defect data features, which may improve the recognition accuracy.

The invention discloses a power transmission line connection fitting fault detection method based on acoustics and electromagnetic induction, and belongs to the technical field of intelligent inspection and nondestructive detection of power transmission lines. A multi-mode sensor is carried by an unmanned aerial vehicle, ultrasonic wave, vibration, images and power frequency electromagnetic field data are synchronously collected, and a multi-dimensional original data set is constructed. Signal quality is improved by adopting wavelet noise reduction, beam forming and sound image fusion technologies, and flight vibration and electromagnetic interference are effectively suppressed by combining an adaptive filtering algorithm and a physical shielding structure. And acoustic, image and electromagnetic characteristics are extracted and normalized and fused, a dynamic threshold reference library is established, and intelligent grading discrimination of abnormity, defects and faults is realized through multi-stage early warning logic. A detection result automatically generates a report and is mapped to a three-dimensional line model, and operation and maintenance system linkage is supported. According to the method, synchronous identification of surface and internal defects is realized, the anti-interference capability is high, the detection accuracy is high, the inspection efficiency and safety are remarkably improved, and the method is suitable for intelligent operation and maintenance of the high-voltage transmission line.

The invention belongs to the technical field of engineering supervision, and discloses a concrete strength measurement system and method for civil construction engineering, and the method comprises the following steps: a test point planning module determines the number and position of detection points; the route planning module plans a running route of the detection mechanism; the detection mechanism sequentially moves to each detection point position according to the planned route; the positioning module analyzes the distance from the ultrasonic probe to the laser; the ultrasonic probe A is aligned with the ultrasonic probe B; the ultrasonic probe A and the ultrasonic probe B scan a concrete building; detecting the strength of the concrete building by using a rebound apparatus; the humidity acquisition module acquires humidity data of concrete; the temperature acquisition module acquires environment temperature data; and the ultrasonic analysis module comprehensively calculates the concrete strength by combining the rebound value, the concrete humidity, the environment temperature and the ultrasonic detection sound velocity. According to the invention, concrete strength on-site detection can be carried out on a high-rise building; by integrating various data, the strength of the concrete can be evaluated more accurately.

The invention provides a crane balance beam crack defect detection method and system, and relates to the technical field of defect detection.The crane balance beam crack defect detection method comprises the steps that a plurality of ultrasonic guided wave sensor arrays are arranged on the surface of a crane balance beam to be detected, ultrasonic guided wave signals in the balance beam are collected, and ultrasonic guided wave time-frequency domain feature vectors of the balance beam are constructed; constructing a three-dimensional finite element simulation model of the crane balance beam to obtain multiple groups of balance beam crack defect expansion strain field data, and constructing a crack defect deformation ultrasonic-strain field fusion feature vector; training a crack defect deformation detection intelligent discrimination model by using the crack defect deformation ultrasonic-strain field fusion feature vector, obtaining a time-frequency domain feature vector and a strain distribution feature vector of the crane balance beam to be detected, and inputting the time-frequency domain feature vector and the strain distribution feature vector into the trained crack defect deformation detection intelligent discrimination model for intelligent discrimination screening. And outputting a crack defect detection result of the balance beam to be detected, wherein the crack defect detection result comprises the type, size, form and specific position of the crack defect.

The invention relates to a pavement structure damage detection method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring image detection data, radar detection data and sound wave detection data of a to-be-detected road surface; performing structural damage dual-channel identification on the image detection data, the radar detection data and the sound wave detection data to obtain initial structural damage detection data of the to-be-detected road surface; according to the preliminary structural damage detection data, determining physical model use guide information and physical model setting parameters corresponding to the to-be-detected road surface; and according to the physical model use guide information and the physical model setting parameters, performing mechanical analysis and fluctuation propagation analysis on the to-be-detected pavement by using each physical analysis model to obtain pavement structure damage detection data of the to-be-detected pavement. By adopting the method, the accuracy and reliability in pavement structure damage detection can be effectively improved.

The invention belongs to the technical field of petroleum pipeline inner wall defect detection, and particularly relates to a petroleum pipeline inner wall defect detection system which comprises a pipe section division module, a wall thickness analysis module, a straight pipe section detection module and a bent pipe section detection module. Ultrasonic incident detection and eddy current scanning are synchronously carried out in areas with different wall thicknesses, eddy current and ultrasonic dual-mode detection are fused to generate a three-dimensional map containing defect boundary and depth information, a deposition risk area is divided on the basis of stress distribution of flow field simulation of a bent pipe section, an adaptive defect detection mode is adopted in different areas, and the defect detection accuracy of the bent pipe section is improved. Therefore, targeted detection of the defects of the inner wall of the petroleum pipeline is realized, missed identification is avoided to a great extent, and the completeness and reliability of defect detection can be greatly improved.

The invention discloses a welding detection method and application thereof in welding monitoring of an LNG (liquefied natural gas) storage tank. According to the scheme, 3D point cloud data, surface image data and cross-sectional image data acquired from a welding seam by combining a 3D laser scanner, an industrial camera and an ultrasonic phased array probe are subjected to multi-modal data fusion; the fusion data is input into a trained defect recognition model, so that the weld joint can have a more perfect data source in quality detection classification, and a reliable data basis is provided for weld joint detection; besides, according to the scheme, a welding seam model based on digital twinning is constructed on the basis of fusion data, in this way, when the welding seam has defects caused by abnormal welding, the welding seam model constructed by the digital twinning model can be used for positioning the defects and outputting local welding seam model data with the defects, and meanwhile, by combining a detection result, the defect detection accuracy is improved. And more visual and reliable detection feedback can be provided for background management personnel.

The invention belongs to the field of hollowing detection, and provides an outer wall hollowing detection method and system, and the method comprises the steps: carrying out the multi-mode detection of an outer wall, and obtaining an infrared thermal imaging image and an ultrasonic detection signal; performing image enhancement processing on the infrared thermal imaging image; respectively carrying out feature extraction on the infrared thermal imaging image and the ultrasonic echo image by using a convolutional neural network; extracting and fusing high-dimensional feature vectors, and aligning an infrared thermal imaging image with an ultrasonic echo image through an image registration algorithm and a spatial transformation model; and classifying the fused feature vectors through a multi-layer sensor according to the data aligned in the step, and outputting the specific position, size and severity of the hollowing area. According to the scheme, the outer wall hollowing detection can be efficiently carried out.

The invention relates to a control method of a digital ultrasonic flaw detector calibrating device. The control method comprises the following steps: acquiring shape data and material parameters of a to-be-detected material, calling a matched standard test block, calculating adaptive ultrasonic frequency and flaw detector parameters, creating and solving a multi-objective optimization model, and planning a detection path of the to-be-detected material based on an optimal solution; emitting ultrasonic waves with matched frequency to implant the quantum dot mark array to carry out fluorescence-ultrasonic dual-mode signal correlation calibration; scanning according to a planned detection path, establishing a time-varying channel model to predict a current time delay variation, extracting a fundamental frequency and a harmonic component of a probe excitation pulse by adopting an autocorrelation algorithm to serve as a reference, constructing a state vector of a filter based on the predicted current time delay variation, and performing phase correction on a received signal to obtain a phase correction signal; and analyzing the defect of the to-be-detected material based on the corrected receiving signal. By integrating material parameter dynamic modeling, multi-modal signal collaborative calibration and adaptive path optimization, the detection precision and efficiency under complex working conditions are improved.

The invention provides a shield tunnel longitudinal seam joint recessive damage diagnosis method based on multi-mode ultrasonic guided waves, and the method comprises the steps: applying orthogonal polarization in a specific region of a longitudinal seam joint, and forming a space-time coupled guided wave interference pattern in the whole region of the longitudinal seam joint; the acquired guided wave interference map is utilized to separate baseline interference characteristics and abnormal disturbance components caused by hidden damage through multi-physics field reverse decoupling, and the abnormal disturbance components are represented as energy abnormal attenuation and phase distortion in a specific polarization direction; constructing a three-dimensional damage sensitive vector field in the longitudinal seam joint; and carrying out dynamic manifold topology analysis by utilizing the generated three-dimensional damage sensitive vector field, identifying topological characteristics in the three-dimensional damage sensitive vector field, and tracking a migration path and an intensity evolution trajectory of the topological characteristics along with the change of the detection condition. According to the method, full-dimension diagnosis and evolution prediction of the hidden damage of the longitudinal joint connector of the shield tunnel are realized.

The invention relates to the field of image processing, in particular to a welding quality comprehensive evaluation method and system, and the method comprises the steps: obtaining an ultrasonic image of a welding joint, carrying out the contour detection, and extracting a defect region of the welding joint; gradient features and gray features of pixel points in the defect area are calculated, feature vectors of target pixel points in the defect area are constructed, and the feature vectors serve as the expression degree of the defect area; edge information of the defect area is obtained so as to determine defect types, the defect types comprise cracks and bubbles, the shape and size of a window are set according to the defect types, bilateral filtering processing is carried out, defect information is extracted, and comprehensive evaluation is carried out on the welding quality. According to the method, the appropriate window shape and size are selected according to the defect type, the detail information of the defect is effectively reserved, the definition of the ultrasonic image is remarkably enhanced through the self-adaptive filtering strategy, and therefore the welding quality is better evaluated.

The invention, which relates to the industrial circuit board monitoring technology field, discloses an industrial circuit board operation abnormity monitoring system comprising a multi-sensor welding spot data acquisition module, a signal processing module, an element welding spot mechanical fatigue monitoring module, a welding spot fatigue fault grading early warning module, and an early warning and response module. Data fusion is carried out on the acoustic emission sensor, the strain gauge sensor and the ultrasonic sensor, the state of the welding spot is monitored synchronously from three dimensions of stress acoustic emission, mechanical deformation and internal defects, the strain gauge sensor captures tiny deformation around the welding spot, and the ultrasonic sensor detects internal potential defects. The acoustic emission sensor senses stress release signals, the three kinds of sensor data are subjected to deep fusion analysis through the D-S evidence theory, the problem of misjudgment caused by one-sided information is effectively avoided, stress waves during crack propagation are captured through the acoustic emission signals, and faults can be diagnosed in time in combination with deformation data of the strain gauge sensor.

The invention relates to the technical field of nondestructive testing, and discloses an ultrasonic nondestructive testing method and system for a welding seam of a steel structure. The method comprises the following steps: adopting a self-adaptive coupling ultrasonic probe to carry out signal acquisition on the surface of a welding seam of a steel structure to obtain original ultrasonic signal data; performing multispectral adaptive wavelet transform processing on the original ultrasonic signal data to obtain an enhanced ultrasonic signal; performing blind source separation on the enhanced ultrasonic signal to obtain a separated weld defect signal; performing triangular constraint average interpolation and adaptive extended Kalman filtering fusion processing on the separated weld defect signals to obtain filtered weld defect feature data; and performing regularization Gaussian field weld contour reconstruction and defect positioning processing based on the filtered weld defect feature data to obtain a weld contour model and defect position data. According to the invention, the stability and reliability of weld defect detection are improved, and high-precision detection of weld internal defects is realized.

The invention discloses a coal and rock mass fracture instability multivariate signal fusion analysis method, and belongs to coal and rock mass analysis. The method comprises the following steps: carrying out time-frequency domain processing on an acoustic emission signal to obtain an accumulated ringing characteristic value # imgabs0 # and an accumulated b characteristic value # imgabs1 #; a main control frequency characteristic value # imgabs2 # and an amplitude characteristic value # imgabs3 # are accumulated; a spatial characteristic value # imgabs4 # of acoustic emission; fitting a fitting function # imgabs7 # from the characteristic value # imgabs5 # to the characteristic value # imgabs6 # respectively; according to the fitting function # imgabs8 #, calculating a correlation coefficient matrix R between the characteristic values # imgabs9 # to # imgabs10 #; according to the matrix R, determining a weight coefficient # imgabs11 # of each characteristic value; a first-order derivative function # imgabs13 # and a second-order derivative function # imgabs14 # of the fitting function # imgabs12 # are obtained, and according to the first-order derivative function # imgabs15 # and the second-order derivative function # imgabs16 #, a demarcation time point # imgabs17 # in the coal and rock mass damage process is determined; and the boundary time point # imgabs19 # is corrected by using the weight coefficient # imgabs18 #, and a corrected boundary time point # imgabs20 # is obtained. In order to solve the problems that an existing coal and rock mass fracture instability judgment index is single, and coal and rock mass fracture instability early warning is low in single index precision, the early warning precision is improved through multi-element signal fusion.

The invention discloses a composite board ultrasonic defect detection method which comprises the following steps: S1, fixing a carbon fiber circumferential winding composite pressure container on a bracket, and establishing a coordinate system with the axis of the container as a z axis; s2, a 40 MHz dry coupling phased array ultrasonic probe is attached to the scanning starting point, and the contact angle of the probe is recorded; s3, moving the probe along the spiral track of the outer surface of the container at the speed of 20mm / s, and collecting A-scanning echo signals of all array elements; s4, performing pulse compression and time domain deconvolution processing on the acquired signal to obtain a time domain echo sequence; s5, delay time is calculated according to the shell curvature, the array element signals are compensated, and focusing B-scanning data are generated; s6, splicing the B-scanning data at the step length of 0.5 mm * 0.5 mm, and reconstructing C-scanning data corresponding to the space coordinates; and S7, inputting the C-scanning data into the trained Transform network, and outputting defect types, sizes and three-dimensional coordinates. The method realizes high-frequency ultrasonic composite board defect accurate detection, remarkably improves the microcrack recognition rate, and is widely applied to safety evaluation scenes of high-pressure hydrogen storage tanks and pressure vessels.