3661results about "Material magnetic variables" patented technology

The invention belongs to the technical field of photovoltaic panel surface defect detection, and discloses a photovoltaic panel surface defect detection method and system based on physical property analysis. The method comprises the following steps: firstly, acquiring a surface temperature distribution image, surface deformation data, ultrasonic echo data, a spectral image, spectral characteristic data and eddy current signal characteristic data of the photovoltaic panel by respectively utilizing an infrared thermal imager, a laser speckle interferometer, an ultrasonic flaw detector, a visible light multi-band imager and eddy current detection equipment; various abnormal regions such as temperature, deformation, ultrasonic echo, spectral characteristics and eddy current signals are determined; and then determining defect positions by integrating various abnormal regions, and determining the types and sizes of the surface defects of the photovoltaic panel by combining various data corresponding to the defect positions. According to the method, accurate positioning and identification of the surface defects of the photovoltaic panel are realized through a multi-physical property detection means, and the detection accuracy and reliability are effectively improved.

The invention provides a color steel plate coating thickness dynamic monitoring method and system based on artificial intelligence. According to the method, the temperature distribution data of the surface of the color steel plate coating is obtained, the thermal response map is generated in a pulsed eddy current excitation mode, then the temperature distribution data and the thermal response map are subjected to time sequence correlation processing, the heat conduction characteristics are extracted, and then the heat conduction characteristics are synchronously analyzed; color steel plate coating defect types and thickness abnormal grades are distinguished through a dynamic weight distribution mechanism, a detection result containing defect positions, sizes and thickness deviation values is generated, and finally a coating thickness dynamic compensation instruction is generated according to the detection result; according to the technical scheme provided by the invention, the online control precision and the production efficiency of the coating quality are remarkably improved, and a closed-loop feedback system from detection to control is formed.

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 provides an intelligent material damage diagnosis method and system based on magnetic characteristic data fusion, and relates to the technical field of nondestructive testing, and the method comprises the following steps: collecting magnetic characteristic data by using a magnetic stress detection robot; performing multi-scale decomposition on the data to construct a magnetic domain evolution characteristic spectrum; inputting the characteristic spectrum into a composite neural network to extract a material damage characteristic matrix; constructing a fusion feature vector by using tensor dimensionality reduction and a multilayer belief transfer network; and realizing damage calculation through temperature field modulation and self-adaptive weight distribution to obtain the damage grade and the residual life. According to the invention, accurate diagnosis of the material damage in the welding seam area of the pressure vessel is realized.

The invention relates to the technical field of aircraft structure detection, and discloses an aircraft structure crack intelligent identification method based on deep learning. The method comprises the following steps: acquiring original vibration response signals and electromagnetic field distribution data on the surface and inside of an aircraft structure in parallel through a multi-source sensor network; synchronously processing the data by using a multi-scale convolutional neural network, and extracting time-frequency domain abnormal fluctuation features and space magnetic field distortion features; constructing a cross-modal correlation model, analyzing a topological dependency relationship of the two types of features through a graph attention mechanism, and generating a fused damage sensitive feature vector; inputting the vector into a pre-trained deep belief network to obtain a probability distribution mapping relation for different crack types; and according to the mapping relation, carrying out adaptive weighted fusion on original multi-sensor data, inhibiting environmental noise and structural background interference, and separating and reconstructing an accurate three-dimensional morphology map of the target crack. According to the method, multi-source data information can be effectively fused to improve the accuracy of aircraft structure crack identification.

The invention relates to the technical field of nondestructive testing, and particularly discloses an automatic detection system and method based on a titanium plate welding part, and the method comprises the steps: obtaining an original physical field signal of a to-be-detected part under the excitation of a single energy field, and extracting a space energy attenuation gradient and time phase lag distribution through wavelet packet decomposition and Hilbert transform; constructing a dynamic propagation model for describing a signal propagation path evolution rule, and performing space-time registration and vector difference calculation with a preset ideal reference model to generate a difference evolution graph; high-dimensional topological feature mapping, density clustering and multi-scale persistence analysis are carried out on the atlas, and a stable abnormal mode caused by defects is identified and confirmed; and backtracking a dynamic evolution path of an abnormal mode, extracting defect core parameters, and completing three-dimensional positioning, type classification and security level evaluation in combination with process information.

The invention provides a body armor composite interlayer defect positioning method and system based on multi-modal nondestructive testing, electronic equipment and a storage medium, and relates to the technical field of nondestructive testing. Ultrasonic guided wave, pulsed eddy current and infrared thermal wave excitation signals are synchronously applied to a body armor composite interlayer, and multi-modal response signals of all positions are collected; and generating an original multi-modal response signal set. And inputting the signal into a pre-trained deep convolutional neural network, determining a target frequency band parameter and a target noise suppression parameter of each modal response signal, and outputting an adaptive modulation parameter group. Performing frequency domain filtering and noise suppression processing on an original signal set to generate an optimized signal set, inputting spatial frequency features into a cross-modal feature fusion model, adaptively associating defect sensitive features of different modals through an attention mechanism in the model, and generating a three-dimensional defect distribution diagram of the composite interlayer, thereby realizing millimeter-level precision defect positioning. The precision and efficiency of body armor composite interlayer defect positioning can be improved.

The invention relates to the technical field of copper pipe defect detection, in particular to a copper pipe internal thread secondary machining defect detection method and system, and the method comprises the steps: obtaining thread surface feature data based on multi-mode optical parameter measurement, and the multi-mode optical parameters at least comprise a polarized light inhibition reflection parameter and a multi-band spectral reflection parameter; temperature gradient parameters in the machining process are synchronously obtained, and deformation error correction is conducted on thread surface feature data in combination with dynamic compensation; according to the corrected thread surface feature data, a multi-dimensional defect judgment model is pre-trained to execute hierarchical defect detection; and integrating the coupling relationship between the multi-mode optical parameters and the electromagnetic parameters to output a defect measurement result, wherein the coupling relationship realizes multi-physical quantity fusion judgment through a preset weight distribution matrix. According to the invention, the problems of optical detection signal distortion and high microcrack omission ratio caused by surface high specular reflection in copper pipe internal thread secondary processing defect detection are effectively solved.

The invention provides an oil and gas pipeline defect three-dimensional contour determination method and device. Acquiring a three-axis magnetic flux leakage detection signal of a to-be-detected target oil and gas pipeline and corresponding space coordinate information of the three-axis magnetic flux leakage detection signal; constructing multi-channel input data according to the three-axis magnetic flux leakage detection signal and the space coordinate information; determining a defect contour prediction result of the target oil and gas pipeline according to the multi-channel input data by using a pre-trained defect contour inversion model; wherein the pre-trained defect contour inversion model comprises a multi-axis feature extraction and fusion module, a feature coding module and a multi-task decoding module; the defect contour prediction result is used for indicating whether the target oil and gas pipeline has defects or not, and the defect contour prediction result is further used for indicating the three-dimensional contour shape of the defects under the condition that the target oil and gas pipeline has the defects. Therefore, high-precision visual reconstruction of the defect position and the three-dimensional form of the oil and gas pipeline is realized, and the accuracy of defect identification and the reliability of evaluation are improved.

The invention relates to the technical field of marine pipelines, in particular to a marine pipeline inner wall defect detection and evaluation method and system, an electronic device and a storage medium, and the marine pipeline inner wall defect detection and evaluation method comprises the following steps: collecting marine pipeline operation data and marine pipeline inner wall magnetic field data in real time; carrying out data preprocessing on the collected marine pipeline operation data to obtain accurate data; constructing and integrating a pipeline entity three-dimensional geometric model, a pipeline physical behavior model and a pipeline performance evaluation model to obtain a digital twin model, and outputting a pipeline performance evaluation result; constructing a defect identification model by adopting a convolutional neural network architecture, and outputting a pipeline inner wall defect evaluation result; and constructing a visual interface, and generating a pipeline inner wall defect coping scheme in combination with the pipeline entity three-dimensional geometric model, the pipeline performance evaluation result and the pipeline inner wall defect evaluation result. The marine pipeline inner wall defect detection and evaluation system can perform real-time monitoring and predictive maintenance on the state of the marine pipeline, and can simulate behaviors and performance of the pipeline under different working conditions, so that potential structural damage, corrosion risks and maintenance requirements are predicted, the maintenance basis is optimized, and the operation cost is reduced.

The invention provides an intelligent eddy current nondestructive testing method and system based on a flexible GMR sensor array, and relates to the technical field of nondestructive testing. According to the invention, a GMR sensor array and a micro excitation coil array are integrated on a flexible substrate, and synchronous acquisition under multi-frequency and multi-phase excitation is realized by establishing a probe-workpiece unified space coordinate system and a time synchronization reference; performing temperature drift compensation, attitude correction and gap normalization processing on the original response tensor to generate a corrected response tensor; the intelligent inversion model based on the prior constraint of the electromagnetic field jointly identifies the conductivity, the thickness and the crack parameters, and outputs a defect parameter map and a confidence map; and a detection report containing the position, the size, the depth and the confidence coefficient is generated through spatial clustering and connectivity analysis, so that high-sensitivity and high-reliability nondestructive detection under a complex curved surface structure is realized.

The invention discloses a nondestructive testing method and system and a data processing method, and relates to the technical field of nondestructive testing. Comprising the steps of obtaining initial detection data of a detected component; establishing at least one reference model according to the initial detection data, and obtaining simulation data corresponding to each reference model; determining target data according to the initial detection data and simulation data; and obtaining subsequent detection data, and comparing the subsequent detection data with the target data to form an evaluation result. According to the invention, high and low frequency mixed excitation signals are adopted, so that the penetrating power and resolution of detection are improved, surface defects and buried defects can be detected at the same time, and the comprehensiveness and accuracy of detection are remarkably enhanced; a picked complex response signal is decoupled by using an orthogonal phase locking method, a magnetic field signal and an electric field signal are effectively separated, the signal-to-noise ratio and the detection precision are improved, and accurate discrimination of defect types, depths and orientations is realized by establishing a reference model and acquiring analog data.

The invention discloses a pulsed eddy current detection method and system for a thermal power pipeline with a coating layer, and relates to the technical field of pulsed eddy current nondestructive detection.The pulsed eddy current detection method comprises the steps that pulsed eddy current detection simulation and experiment are conducted under the condition of a three-dimensional simulation model and a test piece, and excitation current waveform, amplitude and frequency parameters are determined; an induced voltage signal is collected, peak values, time points and attenuation characteristics are extracted, the corresponding relation between the wall thickness, the coating layer thickness and the welding seam structure and detection signals is constructed, a parameterized database is constructed, and temperature correction is completed; and optimizing a sensor structure and a signal acquisition scheme according to a parameterized database, obtaining detection data in a pipeline operation state, completing two-dimensional imaging of wall thickness distribution and welding seam positioning, and forming detection information covering a straight pipe, an elbow and a welding seam area. According to the method, wall thickness detection and welding seam positioning of the thermal power pipeline under the conditions of a thick wall and a thick coating layer are achieved through the full-process design of test piece modeling, simulation experiment and imaging collection.

The invention relates to the technical field of monitoring, in particular to a cable aging monitoring and early warning method and system. The method comprises the following steps: acquiring cable monitoring data, and performing electrical characteristic analysis to obtain electrical characteristic data; performing conductor oxidation analysis based on the electrical characteristic data to obtain conductor oxidation data; cable image feature extraction is carried out according to the cable monitoring data, cable morphology type division is carried out, and curve segment cable data and winding segment cable data are obtained; performing bending fatigue analysis on the curve section cable data to obtain bending fatigue data; performing winding stress analysis on the winding section cable data to obtain winding stress data; performing material damage evaluation according to the bending fatigue data and the winding stress data to obtain material damage data; and carrying out damage position identification according to the material damage data to obtain damage position data. The cable aging monitoring accuracy and the early warning response rate are improved based on the monitoring technology.

A system for providing an interactive inspection map of an inspection surface inspected by an inspection robot includes an inspection circuit structured to interpret inspection data of the inspection surface from the inspection robot and a user interaction circuit structured to interpret a user focus value from a user device. The user focus value includes an activation state value. The system further includes an inspection visualization circuit structured to provide the interactive inspection map to the user device in response to the user focus value. The interactive inspection map is based on the inspection data. Also, the system includes an inspection data validation circuit that determines an inspection data validity description of the inspection data. The inspection data validity description is provided as a display layer on the interactive inspection map to indicate whether the inspection data is valid.

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

The invention relates to the technical field of power batteries, in particular to a power battery pack health state detection method and system. The power battery pack health state detection method comprises the following steps: acquiring open-circuit voltage change data and terminal voltage fluctuation data of a battery under micro current pulse excitation, recording a change curve of voltage along with time through high-precision time sequence acquisition equipment, and obtaining an initial data set of voltage dynamic response; acquiring magnetic field intensity distribution and magnetic field gradient change data of the surface of the battery in a weak alternating magnetic field, and acquiring spatial magnetic field information through a magnetic sensor array to obtain a three-dimensional distribution diagram reflecting magnetic field nonuniformity; through a three-dimensional distribution diagram of magnetic field intensity distribution and magnetic field gradient change, a defect detection algorithm is adopted to analyze a mapping relation between magnetic field non-uniformity and internal micro cracks, and correlation distribution of defect positions and internal resistance growth rates is determined.

The embodiment of the invention provides a real-time defect detection method and device based on carbon fiber winding and a medium, and relates to the technical field of carbon fiber laying layer detection.The method comprises the steps that real-time environment data of carbon fiber winding are obtained, and multi-mode cooperative detection is conducted on the real-time environment data of carbon fiber winding, obtaining a carbon fiber real-time defect data set; performing multi-mode space-time synchronous calibration on the carbon fiber real-time defect data set to determine carbon fiber defect synchronous data; performing strategy-level fusion on the synchronous data of the carbon fiber defects to obtain winding state preliminary judgment data; based on the winding state preliminary judgment data, multi-modal weight update data are determined through carbon fiber winding process stage analysis; and according to the modal weight updating data, determining the real-time winding defect of the carbon fiber through carbon fiber defect type analysis. According to the method, the technical problem of poor anti-interference capability in a single parameter analysis process in carbon fiber winding detection is solved.

The invention provides a magnetostriction-based multi-physical-field defect detection method and system for pressure-bearing equipment, and relates to the technical field of detection, and the method comprises the steps: constructing a reconfigurable magnetic field excitation array and a multi-physical-quantity sensing network through an intelligent excitation and sensing device, and dynamically adjusting the excitation phase to improve the detection precision. According to the layered coupling calculation scheme, a coupling relationship among a magnetic field, a stress field and a temperature field is established, and multi-physical field data calculation is carried out in combination with physical constraint conditions. After multi-level wavelet packet decomposition and noise reduction, the deep neural network is utilized to extract features and generate a defect feature dictionary, so that efficient defect positioning and size evaluation are realized, and the accuracy and reliability of detection are remarkably improved.

The invention relates to the technical field of magnetic ring detection, discloses a magnetic ring multi-dimensional nondestructive detection system and method based on multi-frequency composite electromagnetic excitation, and solves the defects of existing magnetic ring detection in the aspects of information acquisition, composite excitation and comprehensive diagnosis. The method comprises the following steps: applying multi-frequency composite excitation, and collecting and preprocessing multi-dimensional response; and extracting features, and carrying out deep learning fusion inversion to obtain magnetic characteristics, material components and defect information. The system comprises a composite excitation module, a multi-mode sensing module, a data acquisition preprocessing module, a data fusion inversion module and a control module. By adopting the technical scheme, comprehensive and efficient nondestructive detection of multi-dimensional electromagnetic information of the magnetic ring is realized, the existing limitation is overcome, and the detection precision and the comprehensive diagnosis capability are remarkably improved.

The invention discloses a pulse eddy current signal late period slope feature extraction method and system based on segmentation fitting, and belongs to the technical field of nondestructive testing. The method comprises the following steps: performing logarithmic transformation and filtering processing on a to-be-detected eddy current signal; intercepting a characteristic signal segment based on the filtering signal; dividing the feature signal segment into a plurality of sub-segments, performing piecewise linear fitting, determining a linear segment, and extracting a slope as a feature value; repeating the steps to obtain characteristic values of the test pieces with different thicknesses, performing curve fitting according to the characteristic values of the test pieces with different thicknesses and the corresponding thicknesses, and calculating a fitting error; and finally, iteratively optimizing the division number of the sub-segments by taking the minimum fitting error as a target. Through adaptive filtering, segmentation fitting and an intelligent optimization algorithm, the anti-interference performance, the linearity and the measurement precision of pulse eddy current signal feature extraction are remarkably improved, and efficient and reliable thickness detection of the high-temperature and high-pressure industrial pipeline under the condition that a heat preservation layer does not need to be disassembled is achieved.

The invention discloses a concrete structure steel bar corrosion rate magnetic evaluation method for eliminating stress interference, and relates to the technical field of civil engineering. Comprising the following steps: scanning spontaneous leakage magnetic field intensity of reinforcing steel bars before and after concrete structure corrosion, and calculating spontaneous leakage magnetic field change information of the corroded reinforcing steel bars; determining the concrete structure reinforcement corrosion type according to the spontaneous leakage magnetic field change information of the corroded reinforcement; calculating a concrete structure steel bar magnetization intensity change rate caused by stress change before and after concrete structure corrosion; calculating a spontaneous magnetic flux leakage quantitative index according to the corrosion type of the concrete structure steel bar; the spontaneous magnetic flux leakage quantitative index is corrected according to the concrete structure steel bar corrosion type and the concrete structure steel bar magnetization intensity change rate; and calculating a concrete structure steel bar corrosion rate evaluation value for eliminating stress interference. According to the method, the existing spontaneous magnetic flux leakage assessment method for the corrosion rate of the bare steel bar is greatly improved, the stress change interference can be quantitatively corrected, and the high-precision estimation of the corrosion rate of the steel bar in the concrete structure is realized.

The invention discloses a bonding silver wire quality detection method and system, and relates to the technical field of bonding quality detection.The bonding silver wire quality detection method comprises the steps that a thermal infrared imager is adopted to scan a bonding area, and temperature field distribution data in the bonding process is obtained; scanning the bonding area by adopting a magnetic conductivity measuring probe to obtain magnetic conductivity distribution data of the bonding area; performing 10 * 10 grid division on the bonding area, and performing feature extraction on the temperature field distribution data and the magnetic conductivity distribution data based on divided grids to obtain thermal feature parameters and magnetic feature parameters; and calculating a bonding silver wire quality score according to the thermal characteristic parameters and the magnetic characteristic parameters, and judging the bonding quality of the silver wire according to a score result to obtain a bonding quality evaluation result. According to the method, the limitation of traditional single parameter detection is broken through, the accuracy and reliability of bonding quality evaluation are remarkably improved, and a systematic technical support is provided for improvement of the bonding process level.

The invention discloses a rotating wheel nondestructive testing robot and a testing method thereof, and belongs to the technical field of specialized robots and nondestructive testing. The rotating wheel nondestructive testing robot comprises a permanent magnet adsorption walking mechanism, a multi-degree-of-freedom mechanical arm, a module flaw detection unit, a multi-sensor positioning system and a central controller; the adsorption walking mechanism is used for realizing all-attitude stable crawling on the curved surface of the rotating wheel; the multi-degree-of-freedom mechanical arm is arranged above the middle part of the permanent magnet adsorption walking mechanism and is used for moving the module flaw detection unit to a specified position; the module flaw detection unit is used for performing high-resolution detection on the damage of the composite material on the rotating wheel; the multi-sensor positioning system is used for real-time pose feedback of the robot; the central controller is used for controlling all the components to work cooperatively. According to the nondestructive detection robot for the runner and the detection method thereof, the problems that in the prior art, detection of internal cracks of turbine runner blades depends on a manual frame erecting mode, the high-altitude operation risk is large, the number of detection blind areas is large, and efficiency is low can be effectively solved.

The invention discloses a crane steel wire rope flaw detection device and a flaw detection method, and belongs to the technical field of nondestructive flaw detection. The device comprises a magnetic memory flaw detection module and a wheel type rope climbing robot device for driving the magnetic memory flaw detection module to move along a crane steel wire rope, the magnetic memory flaw detection module comprises a hollow flaw detection shell, the flaw detection shell is longitudinally provided with a central through hole for a crane steel wire rope to penetrate through, and a plurality of giant magneto-impedance effect magnetic sensors are evenly arranged in the flaw detection shell in the circumferential direction of the central through hole. Therefore, a sensor array used for detecting the leakage magnetic field intensity of the crane steel wire rope is formed, and the giant magneto-impedance effect magnetic sensors are arranged in a coplanar mode. The device is mainly based on the giant magneto-impedance effect magnetic sensor and the metal magnetic memory principle, extremely weak magnetic nondestructive testing is conducted on the crane steel wire rope, and the device is convenient to use, high in detection precision, simple in structure and suitable for various steel wire rope detection environments.

The invention relates to a defect detection device for a traction steel belt, the defect detection device comprises a rack assembly, a guide wheel assembly, a visual detection module, a magnetic field excitation module and a magnetic field detection module, the rack assembly comprises an upper rack and a lower rack which are oppositely arranged, and an accommodating space is formed between the upper rack and the lower rack; the guide wheel assembly is arranged on at least one of the upper rack and the lower rack, and the guide wheel assembly is used for guiding the traction steel belt to move relative to the rack assembly; the visual detection module is arranged on the rack assembly and used for conducting visual recognition detection on the traction steel belt. The magnetic field excitation module is arranged between the upper rack and the lower rack and is used for forming a magnetic field in the accommodating space; the magnetic field detection module is arranged between the upper rack and the lower rack and used for detecting magnetic field signals. According to the defect detection device, a multi-mode defect detection method is fused, comprehensive detection of internal and external damage of the traction steel belt is achieved, and the detection precision and applicability are improved.

The invention relates to the technical field of pipeline detection, and discloses a pipeline magnetic flux leakage detection method and device, and the method comprises the steps: obtaining sensor array arrangement parameters and original magnetic field data; establishing a three-dimensional sensor network according to the sensor array arrangement parameters to obtain space coordinate information; performing adaptive signal conditioning and feature extraction according to the original magnetic field data and the space coordinate information to obtain a magnetic flux leakage defect candidate region; and comparing the magnetic flux leakage defect candidate region with a magnetic flux leakage characteristic database to obtain a defect position and a severity level. According to the method, accurate positioning and automatic grading evaluation of pipeline defects can be realized.

A wide-field dynamic magnetic imaging device and method. The wide-field dynamic magnetic imaging device comprises: a probe assembly (1), comprising a diamond (101) containing NV centers; a microwave magnetic field assembly (2) configured to generate a uniform microwave field and a uniform bias magnetic field in the vicinity of the diamond (101), so as to regulate the spin energy level of the diamond (101) and the quantum state evolution process; a laser modulation assembly (3) configured to output an initial uniform pulsed laser to excite the diamond (101) to emit fluorescence; a fringe light modulation assembly (4) configured to modulate the initial uniform pulsed laser to output a fringe pulsed laser, causing the fluorescence emitted by the diamond (101) to be fringe fluorescence, or to modulate the fluorescence emitted by the diamond (101) into fringe fluorescence; a fluorescence detection assembly (5) configured to perform spatial imaging on the fringe fluorescence to obtain magnetic field information of a sample under test (7), thereby obtaining an initial magnetic image of said sample (7); and a data processing assembly (6) configured to perform Fourier transform and inverse Fourier transform on the initial magnetic image to obtain a first magnetic image.

The invention relates to the technical field of copper rod detection, in particular to a copper rod grain size detection method and system based on double-frequency eddy current analysis, and the method comprises the steps: synchronously obtaining a high-frequency eddy current response signal and a low-frequency eddy current response signal of the surface of a moving copper rod according to an orthogonally arranged double-frequency eddy current probe, and collecting the moving speed of the copper rod in real time; performing phase demodulation on the high-frequency eddy current response signal and the low-frequency eddy current response signal to respectively obtain a first phase angle and a second phase angle; calculating an instantaneous difference value between the first phase angle and the second phase angle, and generating an original phase difference characteristic quantity; performing dynamic compensation on the original phase difference characteristic quantity based on the movement speed of the copper rod to generate a speed compensation phase difference characteristic quantity; and inputting the speed compensation phase difference characteristic quantity into a pre-calibrated grain size mapping relation model to obtain a real-time grain size grade value. According to the invention, eddy current response interference caused by speed change in the continuous movement process of the copper rod is effectively eliminated, and high-precision detection of the grain size grade is realized.

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.