1642 results about "Ultrasonic detection" patented technology

The invention relates to the technical field of nondestructive testing, and discloses a steel structure weld defect ultrasonic detection and identification system. A data acquisition module of the system acquires an original ultrasonic signal of a steel structure welding seam through ultrasonic detection equipment and acquires geometric attribute data of the welding seam; the model construction module constructs a welding seam three-dimensional digital model based on the data; a feature extraction module performs feature mining on the three-dimensional digital model and extracts a weld defect feature index set; the difference analysis module carries out deviation calculation on the characteristic index set and a reference index set in a standard welding seam characteristic database, and an abnormal area is identified; the risk assessment module calculates a defect sensitivity index according to the abnormal region in combination with real-time environmental parameters, and assesses a defect risk level; and the report generation module formulates a detection scheme according to the defect risk level, generates a detection instruction, executes ultrasonic scanning, collects performance data and generates a defect detection report. The system has the advantages of high detection precision, high reliability, automatic and standardized process and the like.

The invention relates to the technical field of artificial intelligence, and discloses an AI-based composite insulator internal defect ultrasonic detection method, which comprises a multi-mode ultrasonic probe array module, a signal preprocessing module, an AI defect analysis module, a dynamic parameter optimization module, an edge calculation module and a visual report module, the method comprises the following steps: acquiring a full-dimensional signal through a multi-modal ultrasonic probe array, and inputting the full-dimensional signal into a deep space-time convolutional neural network for defect recognition after adaptive noise reduction and feature fusion; the detection precision is improved by combining dynamic waveform matching and multi-physics coupling analysis; model lightweight and real-time processing are realized by adopting transfer learning and edge calculation. The system integrates the functions of parameter adaptive optimization, three-dimensional visualization and Internet of Things cooperation, solves the problems of low efficiency and high false detection rate of a traditional detection method, and improves the intelligent level and engineering applicability of composite insulator defect detection.

The invention discloses a bearing defect detection method and system based on machine vision and ultrasonic detection, and particularly relates to the technical field of industrial automatic detection, and the method comprises the steps: S1, a synchronous collection module: carrying out pulse triggering synchronous collection, and generating a time-space reference table; s2, a feature extraction module: performing image noise reduction segmentation and ultrasonic frequency domain decomposition, and outputting a defect feature vector; s3, a fusion identification module: performing cross-modal feature alignment fusion to generate a defect classification conclusion; s4, a size measurement module: performing contour fitting to calculate inner and outer diameters, and outputting a size deviation value; and S5, a comprehensive judgment module: carrying out threshold comparison logic judgment, and generating a multi-modal detection report. According to the method, a space-time reference is established through an encoder, images are segmented in a self-adaptive mode, features are extracted through wavelet decomposition ultrasound, feature weights are re-calibrated through a parallel network and an attention mechanism, composite defects are recognized through cross-modal fusion, comprehensive judgment is conducted in combination with dimensional deviation, and a multi-dimensional quality evaluation system is achieved.

The invention discloses a panel vertical seam water stop structure repair effect evaluation method, which comprises the following steps of: firstly, synchronously obtaining surface crack and internal defect data through cooperative detection of infrared thermal imaging and ultrasonic flaw detection; and carrying out space alignment on point cloud data generated by three-dimensional laser scanning and an ultrasonic detection result, and establishing a BIM model containing a crack three-dimensional form. A practical engineering environment is simulated in a laboratory to carry out material accelerated aging and test key indexes such as an elastic recovery rate, a base surface state is evaluated through a temperature and humidity sensor array in a construction stage, a six-axis grouting robot is adopted to execute accurate filling, a curing process is monitored by a synchronous pre-embedded optical fiber sensor, and after aging is simulated by combining a hydraulic test with climate, a construction period is shortened. A distributed optical fiber network is used for continuously collecting deformation data, a comprehensive evaluation model is constructed based on mechanics, durability and sealing indexes, and finally material selection and process parameters are optimized through machine learning.

The invention discloses a method and a device for detecting internal defects of a special pipeline with a rifling. The device for detecting the internal defects of the special pipeline with the rifling comprises a detection equipment walking mechanism, the CCD industrial camera is used for collecting core images, and the CCD industrial camera is arranged at the foremost end of the special pipeline internal defect detection device with the rifling; the CCD industrial camera is connected to the holder, and the holder can rotate in all directions; the light supplementing light source can adjust the intensity of the light source and is used for adjusting the intensity of the light source and improving the imaging effect; the ultrasonic detection module comprises an ultrasonic probe and is used for identifying internal defects of the pipeline; the internal defect detection device for the special pipeline with the rifling further comprises a positioning device, a data processing terminal and a data transmission line. According to the invention, the problems of difficult carrier advancing stability, insufficient detection coverage and data fusion, and limited defect quantification and positioning precision in the prior art can be solved.

The invention discloses a duct grouting fullness detection method and device, and relates to the technical field of duct grouting fullness detection.The duct grouting fullness detection method comprises the following steps that an ultrasonic probe is arranged at an inlet of a duct and transmits an ultrasonic signal, and a receiver is arranged at an outlet of the duct and used for receiving reflection and transmission signals and collecting ultrasonic propagation data in the duct; and performing frequency decomposition on the acquired signals, and analyzing energy attenuation conditions of the signals in different frequency bands by using penetration characteristics of ultrasonic waves with different frequencies. According to the method, multi-band ultrasonic detection, self-adaptive signal compensation and machine learning technologies are combined, so that the accuracy and robustness of duct grouting fullness detection are remarkably improved. Multi-band detection covers different depths, and an adaptive compensation algorithm maintains signal stability and reduces misjudgment. The machine learning optimizes data fusion, ensures that the system adapts to different construction conditions and outputs stable results, and the generated grouting map provides clear visualization to help engineers to quickly identify and maintain defect areas.

The invention discloses an on-line qualitative detection method for partial discharge of power equipment, which comprises a detection system and detection steps, and also comprises the following steps: the detection system comprises a high-frequency partial discharge detector for detecting a high-frequency current transformer; the ultrahigh frequency detection system is used for detecting electromagnetic wave radiation which is generated by partial discharge and has extremely short duration time; the ultrasonic detection system is used for detecting sound waves and ultrasonic signals generated along with energy release when partial discharge occurs; a transient ground voltage method detection system is used for live-line detection of equipment such as a medium-voltage switch cabinet. According to the online qualitative detection method for the partial discharge of the power equipment, different detected equipment and different detection methods are adopted to perform online detection on the partial discharge of the equipment, so that the early partial discharge phenomenon of the power equipment can be found in time, the long-term discharge monitoring data of the power equipment can be stored, and the overall detection effect is improved.

The invention provides an ultrasonic detection method and system for inverting residual stress by using sound wave phase velocity, and belongs to the field of air coupling ultrasonic guided wave detection. The method is based on an air coupling ultrasonic guided wave sound field propagation finite element model, and guides optimization design of air coupling ultrasonic transducer parameters. The air coupling ultrasonic guided wave detection technology is adopted for conducting non-contact excitation and receiving of guided wave signals on the composite material component with the preset stress through the stretcher. And calculating the phase velocity of the air-coupled ultrasonic guided wave in the composite material component based on a geometric positioning and time difference cooperative gradual difference method. And calibrating the phase velocity in the target composite material and preset stress to obtain a corresponding relational expression, performing phase velocity detection on a composite material component with unknown stress based on a calibration result of the target composite material, and performing inversion to obtain composite material construction residual stress. By adopting the method and the system, the residual stress detection of the composite material component is realized.

The invention relates to the technical field of semiconductors, in particular to an ultrasonic transmitting circuit, a chip and an ultrasonic module. The ultrasonic transmitting circuit comprises a first control circuit and a signal generating circuit. The first control circuit is used for outputting a first control signal; the signal generation circuit is used for outputting a driving signal according to the first control signal; the driving signal is used for driving a first booster circuit to output an excitation signal, and the voltage of the excitation signal is higher than that of the driving signal; the excitation signal is used for exciting the ultrasonic transducer to emit ultrasonic waves. According to the embodiment of the invention, the energy of ultrasonic waves emitted by the ultrasonic transducer can be improved, so that the ultrasonic receiving circuit can receive echoes with higher energy, and the sensitivity and the signal-to-noise ratio of ultrasonic detection are improved.

The invention discloses a non-convex contiguous group sparse variational filtering hyper-parameter adaptive calculation method, which comprises the following steps of: selecting a reference signal according to ultrasonic signal characteristics, and determining the size of a group sparse group through a specific formula in combination with a sampling frequency and a probe frequency; after interference removal is carried out on the ultrasonic signal, a scattering noise standard deviation is calculated based on the reference signal, and a regularization parameter is obtained in combination with a formula; calculating a penalty factor; when the detection sound path distance is less than 1000mm, adopting global filtering; when the sound path distance is detected to be larger than 1000 mm, local filtering is adopted, a punishment intensity reference value is estimated based on the noise level, the reference value is corrected in combination with the signal-to-noise ratio, and then a formula is substituted to calculate a punishment factor coefficient and a final punishment factor; in the iteration process, the relative variable quantity of the cost function is used as a convergence criterion, iteration is stopped when the variable quantity is smaller than a preset threshold value, and efficient and stable filtering is ensured; the method completely meets the engineering application requirements of industrial ultrasonic detection.

The invention relates to a GIS (Geographic Information System) basin-type insulator defect detection method, electronic equipment and a storage medium, belongs to the technical field of intelligent image identification and processing of power equipment, and solves the problem of how to improve the accuracy of GIS basin-type insulator defect detection. The method effectively improves the resolution and information integrity of defect images, constructs a MobileNetV2 and YOLOv11n lightweight network structure for the technical bottlenecks of large FMC data volume and complex imaging calculation, achieves the intelligent closed-loop process from defect identification, positioning and classification to segmentation evaluation, achieves the automatic identification and geometric quantity extraction of defect areas, and improves the accuracy of the detection result. A data support is provided for subsequent early warning judgment and decision making; according to the method, the ultrasonic detection identification precision and the imaging quality are effectively improved.

The application provides a hydrogen pipeline defect detection method and system, equipment and a storage medium, and belongs to the technical field of defect detection. The method comprises the following steps: acquiring phased array ultrasonic detection data, digital radiographic imaging data and hydrogen concentration data of a hydrogen pipeline; extracting defect boundary features based on the phased array ultrasonic detection data, extracting defect microstructure features based on the digital radiographic imaging data, and extracting hydrogen concentration spatiotemporal distribution features based on the hydrogen concentration data; constructing a modal correlation graph by taking the defect boundary features, the defect microstructure features and the hydrogen concentration spatiotemporal distribution features as nodes, and determining the edge weight between every two nodes in the modal correlation graph according to the mutual information of the two nodes; generating a multi-modal correlation feature vector based on the modal correlation graph; and obtaining a defect detection result of the hydrogen pipeline based on the multi-modal correlation feature vector. The application can improve the accuracy of hydrogen pipeline defect detection.

A system may comprise a neural network trained to predict mechanical damage to field-deployed industrial equipment with a main or supplementary function to transfer heat change phase, or drive / limit a reaction. The neural network may be trained using an idealized geometry. A processor may receive process input data from the equipment and may provide the data to the trained neural network. The processor may generate a prediction of mechanical damage using the trained neural network and the process input data. The process input data may comprise temperature data collected during operation. A scanning device may generate a digital twin of at least a portion of the equipment through scanning and ultrasonic testing. A monitoring module may monitor processes through a control system to collect the process input data. The neural network may comprise a convolutional neural network configured to calculate damage using a surrogate model.

The invention provides a comprehensive noise reduction performance evaluation method for a nonlinear ultrasonic detection signal noise reduction algorithm, and the method comprises the steps: firstly constructing a multi-working-condition noise reduction performance pre-screening mechanism based on error band analysis based on early-stage experimental data, and then introducing a radar map as an evaluation tool for the comprehensive noise reduction effect of the signal noise reduction algorithm. The noise reduction effects of a moving average method (MA), a spectral subtraction method (SS), a short-time Fourier transform method (STFT), a wavelet transform method (WT) and an orthogonal matching pursuit algorithm (OMP) are compared, and finally a signal noise reduction algorithm with the optimal comprehensive efficiency is screened out. On the basis of algorithm optimization, quantitative mapping rules between microcrack three-dimensional geometric parameters and relative nonlinear coefficients are analyzed through regression modeling, and the significant level of the correlation degree of the relative nonlinear coefficients and microcrack size parameters is effectively improved; and a high-confidence theoretical support is provided for quantitative nondestructive detection of the microcracks in engineering practice.

The embodiment of the invention provides an ultrasonic transducer, a preparation method of the ultrasonic transducer, an ultrasonic fingerprint module and electronic equipment. The ultrasonic transducer comprises a substrate, a bottom electrode, a piezoelectric layer, a top electrode and an excitation electrode, the bottom electrode is located on the upper surface of the substrate, the piezoelectric layer is located above the substrate and covers the bottom electrode, and at least part of the top electrode is located above the piezoelectric layer; the excitation electrode is located on the upper surface of the substrate, a distance exists between the excitation electrode and the bottom electrode, the piezoelectric layer is provided with an opening part, and the top electrode is electrically connected with the excitation electrode through the opening part; the excitation electrode is used for receiving an excitation signal and applying the excitation signal to the top electrode, so that an electric field is formed between the top electrode and the bottom electrode to excite the piezoelectric layer to emit an ultrasonic signal, and the bottom electrode is used for receiving an ultrasonic detection signal generated between the top electrode and the bottom electrode when the returned ultrasonic signal acts on the piezoelectric layer. The performance of the ultrasonic transducer in the scheme is good.

The invention discloses a marine pipeline leakage point and oil leakage accurate detection system and method, a focused ultrasonic detection module of the system emits ultrasonic focused sound beams to a pipeline wall, a signal acquisition and analysis module is used for receiving an ultrasonic echo signal and performing feature extraction, a fluid noise elimination and compensation module filters and corrects the signal, and a detection module is used for detecting the leakage point and oil leakage of a marine pipeline. The sensor array attached to the pipe wall can monitor the temperature, pressure and environment change of the marine pipeline in real time, the control and feedback unit automatically adjusts the focus depth and acoustic beam energy according to an analysis result, fine scanning and enhanced detection of a target area are achieved, and finally leakage point space coordinates and leakage rate estimated values are output. The system can stably operate for a long time in a high-noise and high-pressure marine environment, is high in detection sensitivity and strong in anti-interference capability, can achieve millimeter-level leakage point positioning precision, and is suitable for online health monitoring and intelligent safety management of marine oil and gas conveying pipelines, offshore wind power cooling systems and deep sea energy channels.

The invention relates to the technical field of ultrasonic detection, and discloses a robot ultrasonic detection method, device and equipment based on three-dimensional perception and a medium, and the method comprises the steps: obtaining three-dimensional point cloud data of the surface of a target object; generating a scanning path for detecting the target object based on the three-dimensional point cloud data; controlling a motion execution device to drive the detection probe to move along the scanning path to obtain ultrasonic image data of the internal structure of the target object; in the moving process of the detection probe, the contact force between the detection probe and the target object is maintained within a preset force value range; analyzing the ultrasonic image data, identifying a lumen contour and determining a narrowest position point; and adjusting the posture of the detection probe at the narrowest position point and executing secondary detection to obtain hydrodynamic data. The self-adaptive scanning path is generated through the three-dimensional point cloud data, the movement and the contact force of the detection probe are controlled, the ultrasonic image and the space position are associated, and the problems that the detection path is not attached to the curved surface, the contact force fluctuates, and the image space is not synchronous are solved.

The invention provides an ultrasonic detection method for internal defects of an insulating part of a power system based on a piezoelectric ultrasonic transducer, and belongs to the technical field of insulating part detection. After wavelet denoising and deep attenuation compensation are carried out on echo signals, sound path distance time delay and sound pressure amplitude parameters are extracted, defect identification and position estimation are carried out by utilizing a layered variational auto-encoder, a high-frequency transducer is switched to aiming at tiny defects, a harmonic component is extracted by adopting a nonlinear ultrasonic method, and a characteristic size is calculated; a defect distribution space feature matrix is generated based on a synthetic aperture focusing technology, a unified or personalized processing strategy is selected according to an acoustic characteristic homogenization degree evaluation value, a complete defect detection result is output, and the technical problem that deep weak defects in the electric insulation part are difficult to accurately detect and recognize is solved.

The invention discloses in-service gas cylinder ultrasonic detection equipment which comprises a rotary driving module, an axial linear module, a probe module, a main supporting module and an auxiliary supporting module, and the main supporting module and the auxiliary supporting module are installed on the periphery of a to-be-detected gas cylinder at intervals; the rotary driving module is arranged on the main supporting module; the first end of the axial linear module is connected with the output end of the rotary driving module so as to rotate circumferentially along with the rotary driving module, and the second end of the axial linear module is connected with the auxiliary supporting module through a driven part; the probe module is installed on the moving part of the axial linear module. According to the invention, automatic and full-coverage scanning of the ultrasonic probe on the surface of the in-service gas cylinder is realized, the problems of low manual detection efficiency and poor reliability are effectively solved, and the detection efficiency and the data reliability are remarkably improved.

The invention provides a phased array composite material ultrasonic flaw detector control method and system, and relates to the technical field of ultrasonic detection, and the technical scheme is characterized in that multi-point distance data between a phased array probe and a detected curved surface of a composite material component is acquired; calculating local geometric parameters of a measured curved surface below the phased array probe based on the multi-point distance data; the actual transit time of ultrasonic echoes from a characteristic reflector in the composite material component is obtained; according to the calculated local geometric parameters and the obtained actual transit time of the ultrasonic echoes, the equivalent propagation sound velocity for representing propagation of the ultrasonic waves in the composite material component is determined; and calculating excitation delay time for each array element of the phased array probe according to the calculated local geometric parameters and the determined equivalent propagation sound velocity. The phased array composite material ultrasonic flaw detector control method and the phased array composite material ultrasonic flaw detector control system provided by the invention have the advantages of improving the detection sensitivity and the accuracy of defect evaluation.

The invention discloses a transformer substation abnormity intelligent diagnosis method based on a self-attention mechanism, and relates to the technical field of transformer substation abnormity identification, and the method comprises the following steps: S2, employing a computer vision technology, a sensor, an ultrasonic detector, and a current and voltage detector to collect the electrical parameters of equipment, and completing the collection of multi-source data; s3, realizing a virtual mirror image of the physical equipment by using a digital twinning technology; and S5, analyzing and processing the important information in the database through a self-attention mechanism, and predicting the health trend, risk point and service life of the substation equipment. According to the method, image information and equipment parameters of substation equipment are collected in real time, an attention weight matrix of data is obtained through calculation, multi-source data are associated, the fault probability is output, then a regression device is used, key parameter values of multiple time points in the future and the remaining service life are output, and the service life of the substation equipment is calculated. And predicting the health trend, the risk point and the service life of the substation equipment.

The invention discloses a detection method for automatic scanning of a fillet weld phased array of a petrochemical device. The method comprises the following steps: firstly, acquiring pipeline surface information in real time based on an environment sensing sensor, and synchronously generating a real-time three-dimensional environment model containing a fillet weld and a probe pose through an SLAM (Simultaneous Localization and Mapping) technology; extracting a characteristic track of the fillet weld as a reference scanning path; on the basis of the path and the real-time pose, the motion trail of the mechanical arm is dynamically planned, and phased array acoustic beam emission parameters are synchronously calculated and adjusted through the Snell law in a back-stepping mode according to the normal vector of the surface of the pipeline; the mechanical arm is controlled to operate along the planned track and execute ultrasonic detection; and finally, fusing the ultrasonic data with the real-time pose to generate a three-dimensional detection result corresponding to the three-dimensional environment model space. The full-process automation of fillet weld detection is realized, the problems of low efficiency, easy track deviation, inaccurate sound beam regulation and control, difficult data visualization and the like of a traditional method are solved, and the detection precision, efficiency and reliability are remarkably improved.

A system may comprise a neural network trained to predict mechanical damage to field-deployed industrial equipment with a main or supplementary function to transfer heat change phase, or drive / limit a reaction. The neural network may be trained using an idealized geometry. A processor may receive process input data from the equipment and may provide the data to the trained neural network. The processor may generate a prediction of mechanical damage using the trained neural network and the process input data. The process input data may comprise temperature data collected during operation. A scanning device may generate a digital twin of at least a portion of the equipment through scanning and ultrasonic testing. A monitoring module may monitor processes through a control system to collect the process input data. The neural network may comprise a convolutional neural network configured to calculate damage using a surrogate model.

The utility model relates to ultrasonic rapid detection equipment, which comprises a rack, the rack is provided with a plurality of groups of moving seats and a first driving assembly for driving the same group of moving seats to move synchronously, one group of moving seats comprises two moving seats, and the moving seats are provided with a plurality of abutting cylinders. A plurality of moving wheels are rotationally connected to the opposite side walls of the two moving seats, two moving frames and a second driving assembly for driving the two moving frames to synchronously move are further arranged on the rack, rollers and rotating parts for driving the rollers to rotate are arranged on the moving frames, coupling liquid is arranged in the rollers, and the coupling liquid is connected with the rotating parts through a connecting rod. And an ultrasonic probe is mounted on the movable frame and is positioned in the coupling liquid. The effect of improving the detection efficiency of the ultrasonic detection equipment is achieved.

The invention belongs to the technical field of battery health state monitoring, and relates to an unmanned aerial vehicle battery health state on-line prediction method comprising the following steps: exciting a quantum dot tracer agent in a battery monitoring area to obtain an initial fluorescence spectrum, and carrying out real-time comparison to identify fluorescence quenching caused by gas production and generate a preliminary aging signal; further analyzing the spectrum drift characteristics so as to analyze lithium aging and pure gas production; triggering ultrasonic detection based on a diagnosis result, and extracting time-frequency characteristics of acoustic echoes to construct quantitative acoustic fingerprints; and finally, through cross validation of optical and acoustic information, generating a comprehensive health status report containing a risk level and an aging type. According to the method, multi-dimensional on-line diagnosis and early warning on the early aging stage of the battery are realized, and the problems of hysteresis quality, difficulty in positioning and inaccurate risk assessment caused by dependence on macroscopic electrical parameters in a traditional method are solved.

The invention provides a soil sampling device for soil analysis, and relates to the technical field of soil analysis. A depth sensing module, an intelligent sampling rod, an analysis and feedback module and a reinforcement learning module are integrated. The optimal sampling depth is predicted through the ultrasonic detection and near infrared scanning technology and the deep learning model. The intelligent sampling rod controls the sampling depth, the sampling amount, the vertical rotating force and the vertical rotating direction according to set parameters. The analysis and feedback module integrates various types of microsensors, monitors the pH value, conductivity and moisture of soil in real time, pretreats a sample, and obtains a soil component overview through spectral analysis. And the reinforcement learning module continuously updates and optimizes a sampling strategy through real-time data feedback and cloud computing. The display module visually displays real-time data, supports mode switching, planning and recording, and is simple and convenient to operate. According to the method, multiple methods such as deep learning and reinforcement learning are combined, the accuracy and efficiency of soil sampling are improved, and powerful technical support is provided for soil analysis.

The invention relates to the field of track detection, in particular to a multi-track type track detection device and a detection method thereof.The multi-track type track detection device comprises a base, a supporting unit arranged on the base and a detection unit arranged on the base. The outer gear ring is matched to drive the detection piece to carry out surrounding detection on the track, the detection area is enlarged, meanwhile, through cooperation of the arc-shaped contact plate and the connecting round block, through back-and-forth movement of the arc-shaped contact plate and the meshing relation between the arc-shaped contact plate and the connecting round block, precise micro movement of the detection end is achieved, and the incident angle of the light source is locked in the low-angle interval of 10-25 degrees. The deflection angle of a detection end is adaptively adjusted, track reflection is inhibited, a detection blind area is eliminated, an image imaged by a polarization camera is more accurate, angle automatic compensation ultrasound and low-angle glancing polarization difference are coupled in a rotating ring in cooperation with ultrasonic detection, mechanical and optical coupling type difference is combined, and the false detection rate is reduced.

The invention relates to the technical field of ultrasonic detection, and discloses an implicit neural representation-based model generation method and device, equipment and a medium, and the method comprises the steps: obtaining a two-dimensional image sequence collected by detection equipment and spatial pose information corresponding to image frames, analyzing periodic motion, screening the image frames in a stable motion stage, and generating a to-be-reconstructed image sequence. And processing the to-be-reconstructed image frame, and extracting segmentation data of the target object. And training an implicit neural representation model based on the to-be-reconstructed image sequence, the spatial pose information and the target object segmentation data, inputting the three-dimensional space coordinates into the model, generating corresponding image information and semantic information, and generating a three-dimensional model of the target object. According to the method, the stable image frames are screened and the implicit neural representation model is trained by analyzing the spatial pose information, so that the data utilization efficiency and the precision and continuity of three-dimensional model reconstruction are improved, and high-precision continuous three-dimensional reconstruction is realized without external registration or breathing cooperation.

The invention discloses a stamping die process data processing method and system based on big data analysis, and relates to the field of die management, and the method comprises the steps: outputting a crack feature vector; extracting standardized ultrasonic detection data of the stamping die; forming a process comprehensive damage detection result of the stamping die; and dynamically adjusting detection parameters of the electromagnetic field sensor and the ultrasonic sensor. The electromagnetic field frequency and intensity, the ultrasonic transmitting power, the pulse width and the beam focusing are dynamically optimized, adaptive adjustment of parameters of each region is achieved, and it is ensured that the optimal detection effect can be obtained under different material conditions. Meanwhile, closed-loop feedback and multi-source fusion ensure that the system is stable and reliable, the risk of false detection and missing detection is reduced, the controllability and practicability of the detection process are improved, and reliable technical support is provided for efficient and intelligent detection of the stamping die.

The invention discloses a full-automatic steel plate internal defect on-line detection system based on an ultrasonic phased array, and relates to the technical field of nondestructive testing, and the system comprises an intelligent analysis module which is in communication connection with an on-line detection module and is used for operating an artificial intelligence rating model to carry out processing and preliminary evaluation on original ultrasonic detection data, outputting an initial evaluation result containing the defect space position; and the high-precision verification module is in communication connection with the intelligent analysis module, and is used for intercepting a corresponding sample steel plate according to the defect space position in the preliminary evaluation result, and verifying and measuring the sample steel plate through a high-precision detection means to generate defect true value data. According to the invention, the double quality control modules are combined with the look-ahead control and the backtracking control, so that the subsequent steel plate detection quality is guaranteed, the historical detection data deviation is corrected, the whole-cycle quality control is realized, the missing detection rate and the false detection rate are effectively reduced, and the quality stability of steel plate products is improved.