2326results about "Material flaws investigation" 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 discloses a PCBA circuit board welding spot detection method based on multi-modal data fusion, and relates to the technical field of electronic manufacturing quality detection.The PCBA circuit board welding spot detection method comprises the steps that a distributed sensing network is constructed, multi-modal data are collected, welding spot information is obtained in an omnibearing mode, and time-space alignment of the multi-modal data is carried out; performing feature extraction on the multi-modal data, dynamically weighting each modal feature through an attention mechanism, and highlighting key defect characterization; a welding spot spatial topological graph is constructed by using a graph neural network, and a spatial relationship between welding spots is modeled. By integrating optical, X-Ray, thermal, mechanics, electricity and other multi-dimensional data, the information limitation of single-mode detection is broken through, the complementation of different mode data is utilized, the attention mechanism is combined to dynamically weight each mode feature, the complex defect is accurately identified, the graph neural network is utilized to model the welding spot space topological relation, the associated defect is further captured, and the defect detection accuracy is improved. And the defect classification accuracy is improved.

The invention relates to the technical field of non-destructive testing, and discloses a non-destructive testing method for enhancing defect detection efficiency through deep learning, which comprises the following steps: step 1, acquiring multi-modal data of a target material, including data acquired through X-ray imaging, ultrasonic detection and infrared thermal imaging; 2, signal denoising processing is carried out on the obtained multi-modal data, signal denoising is carried out through wavelet transformation, and the wavelet transformation comprises the steps that multi-scale decomposition is carried out on signals, and high-frequency noise is removed through a soft threshold method; and step 3, carrying out image enhancement processing on the de-noised multi-modal data. The internal structural features of the material are captured from multiple physical dimensions by fusing multi-modal data of X-ray imaging, ultrasonic detection and infrared thermal imaging. The multi-modal fusion can make up for the deficiency of a single detection technology, so that the defect identification in complex structures and materials is more comprehensive and accurate, thereby significantly improving the detection precision.

The invention belongs to the technical field of welding quality detection, and particularly relates to a visual image-based welding seam defect detection method, which comprises the steps of image acquisition, image preprocessing, data analysis, data output, defect classification and decision making, system closed-loop optimization and the like. According to the method, by synchronously collecting two-dimensional images, three-dimensional shapes and heat distribution data of metal welding seams and adopting a polarization filter and annular LED light source combination scheme, multi-dimensional conjoint analysis of physical defects and thermodynamic characteristics is achieved, basic characteristic data are extracted through primary processing, quantifiable defect coefficient indexes are generated through secondary processing, and the detection accuracy is improved. And finally, generating a comprehensive defect index through a multi-modal fusion algorithm, constructing a well-arranged intelligent analysis decision chain, and establishing a self-evolution mechanism of data acquisition-analysis decision-model iteration through real-time interaction of a detection result and an algorithm model. The system can continuously optimize the detection threshold value and the characteristic weight parameter according to the actual working condition of the production line, and the continuous improvement of the detection sensitivity is kept.

The invention relates to the technical field of intelligent detection, in particular to a container surface damage detection method and device based on machine vision, and the device consists of a multi-modal data acquisition module, a dynamic compensation processing module, a multi-modal data fusion module and a damage classification and positioning module. The multi-modal data acquisition module generates a high-density three-dimensional point cloud through double-laser line scanning, and acquires a multispectral image, an infrared thermogram and a real-time motion state. The dynamic compensation processing module integrates an optical flow method and acceleration data to realize sub-pixel-level motion compensation, and combines adaptive exposure control to optimize the imaging quality under complex illumination. The multi-modal data fusion module strengthens defect feature expression through space-time alignment and a double-branch collaborative attention mechanism, and model parameters are reduced through lightweight network design. And the damage classification and positioning module adopts an improved deep network to realize defect classification and complete millimeter-level three-dimensional positioning. The automatic detection requirement of a port is met, and the problems of poor container detection precision, poor adaptability and the like are solved.

A detection method for infrared thermal image damage area of coal rock includes: step 1: collecting and recording the infrared thermal image of coal rock in the process of loading destruction; step 2: processing a gray-scale transformation on the collected infrared thermal image of coal rock; step 3: denoising an infrared thermal image of coal rock after the gray-scale transformation by the dense residual image denoising algorithm of autocorrelation network; step 4: conducting an area segmentation on the infrared thermal image of coal rock after denoising to extract eigenvalues by using a coal-rock infrared thermal image damage area segmentation algorithm of improved encoder-decoder network; step 5: observing the damage area of coal rock. The detection method improves the denoising effect of the infrared thermal image of coal rock, strengthens the extraction of the characteristics of the damage area, and improves the accuracy of the segmentation.

The invention relates to the technical field of chip testing, in particular to a chip testing method and a chip testing system. The method comprises the following steps: acquiring a thermal imaging image of a working state of a chip; global color temperature offset optimization is carried out on the thermal imaging image of the working state of the chip, and spatial heat distribution mining is carried out, so that a chip heat distribution diagram is constructed; performing multi-moment heat distribution tracking processing on the chip heat distribution diagram, performing dynamic heat distribution evolution fitting, and constructing a dynamic heat distribution trend evolution diagram; acquiring a high-definition image of a to-be-tested chip; performing chip assembly visual monitoring on the high-definition image of the to-be-tested chip, performing heat distribution trend rendering based on the dynamic heat distribution trend evolution graph, and constructing a chip assembly heat distribution trend rendering model. According to the invention, through limit working condition test analysis, the evaluation efficiency and accuracy of the chip performance are improved.

The invention discloses a display defect detection method and device. According to the scheme, visible light image data and infrared thermal imaging data of a to-be-detected display are obtained, multi-modal feature extraction and fusion are performed, a fusion feature matrix is generated, and defect identification is performed based on the fusion feature matrix; according to the scheme, the visible light image data and the infrared thermal imaging data are acquired, and defect detection is performed by combining data of two modes, so that defect characteristics and the like of the display can be captured more comprehensively; the accuracy of defect detection can be effectively improved through the combination of two kinds of data; besides, through multi-modal feature extraction and fusion, the incidence relation between the edge feature and the heat distribution feature which can more accurately represent the pixel unit is generated, and defect detection is carried out based on a fusion feature matrix, so that false alarm and missing alarm of defect detection of the display can be reduced, and the reliability of defect detection is improved; and various types of defects can be effectively detected.

The invention discloses an LCD display screen display fault judgment method, and relates to the related technical field of LCD display screen detection. Comprising the steps of synchronous acquisition and preprocessing of multi-source signals, feature extraction, establishment of a fault judgment model, generation of a dynamic test mode, dynamic generation of a targeted test image according to a preliminary detection result to excite a potential fault, iterative optimization of a test sequence by a Q-learning algorithm, and fault diagnosis. The invention also discloses a system for judging the display fault of the LCD screen. The system comprises a multi-source data acquisition module, a central processing unit, a memory and a user interface. Various data including display data, driving voltage / current monitoring data, temperature distribution, environment parameters and the like are synchronously collected through the optical sensor, the electric signal probe, the thermal imaging module and the environment sensor, all-directional information of the LCD display screen in the operation process can be obtained, and limitation and misjudgment possibly caused by a single data source are avoided.

The invention relates to the technical field of building appearance defect detection, in particular to a building appearance defect detection method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining the building information of a target building, and generating a hierarchical scanning path and a three-dimensional obstacle avoidance flight path, a visible light image, an infrared thermodynamic diagram and laser radar point cloud information are collected for space-time alignment processing, and an attention mechanism neural network is used for extracting multi-scale features to generate a detection report containing defect three-dimensional coordinates, damage levels and safety risk assessment. The method achieves the purpose of efficiently and accurately detecting the building appearance defects, can adapt to complex building structures and environmental conditions, supports defect trend prediction and maintenance decision, and remarkably improves the building safety management efficiency.

The invention relates to the technical field of automatic detection, and discloses a high-precision AOI detection system and method for complex industrial product surface defects, and the high-precision AOI detection system for complex industrial product surface defects comprises a multi-modal sensor module, an image processing and data fusion module, and a feature extraction and high-dimensional data analysis module. A deep learning detection module; the high-precision AOI detection method for the surface defects of the complex industrial product comprises the following steps: acquiring multi-dimensional image data of the surface of the product by using a multi-modal sensor module, and carrying out optimization, geometric correction and illumination correction on the multi-dimensional image data through an image processing and data fusion module. According to the method, a multi-mode sensor fusion technology is adopted, image data of visible light, infrared light, hyper-spectrum and a 3D depth sensor are comprehensively collected, surface defects of complex industrial products are comprehensively sensed, and the accuracy and robustness of defect detection are remarkably improved.

The present disclosure discloses an infrared thermal imaging defect detection method and apparatus for a substation insulator. An infrared module is bidirectionally connected to a core computing module, and an infrared module is unidirectionally connected to a high-rate battery. The core computing module is unidirectionally connected to a touch screen. The infrared module is configured to obtain infrared radiation information through a lens, and calculate and generate a temperature distribution video. The method includes infrared video acquisition, thermal region segmentation, device type determination, key temperature information extraction, defect diagnoses, result display and result saving.

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 the technical field of additive manufacturing, and particularly discloses a real-time defect detection and in-situ repair method and device in the additive manufacturing process, and the method comprises the steps that a multi-mode sensing system is integrated on additive manufacturing equipment, the multi-mode sensing system comprises a high-resolution CCD camera, a laser scanner and a thermal infrared imager, and data acquisition is synchronized; a hierarchical scanning strategy is adopted, two-dimensional surface images, three-dimensional point cloud data and thermal field distribution information are collected before and after each layer is machined, and the data synchronization precision is smaller than 0.1 ms; according to the invention, a multi-modal sensing system and an advanced algorithm are integrated, so that real-time detection and accurate repair of defects are successfully realized; a high-resolution CCD camera, a laser scanner, a thermal infrared imager and other sensors are adopted, a cross-scale RANSAC algorithm and a depth feature fusion network are combined, surface and internal defects are accurately detected, and laser remelting parameters are adjusted in real time for in-situ repair.

The invention discloses an industrial surface defect detection method based on multi-scale feature fusion, and the method comprises the steps: collecting the multi-source data of a detected surface in real time through a multi-modal sensor array, and forming a structured data set through time-space synchronization and denoising; constructing an adaptive geometric correction model to realize spatial transformation and scale normalization of multi-scale features, and cooperatively realizing cross-modal alignment and preliminary fusion through texture and physical attribute branches of a double-flow decoding network; dynamically reweighting the fusion features based on a defect physical model, strengthening physical mechanism defect characterization and suppressing interference; combining optical flow compensation and three-dimensional convolution to extract spatio-temporal evolution characteristics, and forming dynamic defect characterization; and finally outputting defect type and severity evaluation through the classification model in combination with the process parameter library. Therefore, the adaptability of the method to a complex industrial environment is enhanced, and the detection stability can be maintained under different materials, illumination conditions and dynamic interference.

The invention belongs to the field of data analysis and processing, and discloses a building facade defect detection system based on unmanned aerial vehicle exogenous thermal excitation compensation. Comprising the steps of generating a building facade three-dimensional model, predefining a heterogeneous modal joint optimization framework, selecting an automatic connection port according to a mode, and operating an intelligent control center; unmanned aerial vehicle formations are deployed based on a master-slave mode, and the formations share detection data through federal learning; the intelligent control center comprises the steps of deploying an airborne lightweight model, processing thermal imaging data and RGB images in real time at an unmanned aerial vehicle end, identifying a suspected defect area, generating a light spot distribution thermodynamic diagram through a pre-detection model deployed by the intelligent control center, and performing a heating task in a dynamic light-heat cooperative and integrated manner; the intelligent control center further comprises ground analysis centralized control, a cloud actuarial model is used for constructing a thermal diffusion space-time map and hollowing expansion trend prediction, a three-dimensional defect distribution map is finally generated and displayed through an interactive interface, and defect detection of the unmanned aerial vehicle on the building facade under exogenous thermal excitation compensation is achieved.

The invention discloses an egg surface microcrack detection system and method based on image analysis, and relates to the field of surface defect nondestructive detection.The method comprises the steps that a process identification interface module obtains a processing process identifier of an egg in real time; the multi-angle annular light source module is provided with a multi-band annular light source array comprising visible light and near-infrared LED lamp beads which are independently controlled, a polaroid group and a beam splitter prism are integrated, and visible light polarization images and near-infrared polarization images on the surfaces of the eggs are synchronously collected; the thermal excitation enhancement unit obtains a thermal infrared image; the process filtering module extracts the axial elongation of the mechanical stress microcrack after graded transportation, and calculates the mesh fractal dimension of the thermal stress microcrack after UV disinfection; the multi-modal feature fusion module generates a dual-channel crack probability graph; and the dynamic feedback control module outputs a micro-crack risk grade and adjusts the rotating speed of the objective table and the light source intensity. The method has the advantages that accurate judgment of mechanical and thermal stress cracks is realized, and curved surface reflection interference is broken through.

The invention relates to the technical field of visual identification, and discloses a belt tearing detection method, device and equipment based on visual identification, and the method comprises the steps: carrying out the dual-channel image collection and preprocessing of the surface of a conveying belt, and obtaining a multi-channel preprocessing image; extracting a thermal difference feature and a texture structure feature of the multi-channel preprocessed image through a double-flow feature network, and inputting the thermal difference feature and the texture structure feature into a quaternion material deformation analysis model for deformation gradient tensor and invariant parameter calculation to obtain a tear feature description vector; performing hierarchical progressive identification and three-dimensional reconstruction analysis to obtain target tearing feature data; and risk assessment is carried out based on the target tear feature data to obtain a tear grade classification result, the interference of ambient temperature drift and non-uniform illumination is effectively eliminated, the calculation efficiency and accuracy of tear detection are improved, and false alarms and missing alarms are reduced.

The invention belongs to the technical field of construction crack detection, and particularly discloses a crack detection method and system for expressway construction, and the method comprises the steps: synchronously collecting multiband data through a multispectral thermal imaging sensor, and integrating the surface morphology, material reflection and deep thermal radiation characteristics through a multisource registration fusion technology; the crack identification capability in a complex environment is enhanced, the crack edge continuity and thermal radiation fluctuation characteristics are further analyzed through dynamic correlation, transient interference is accurately stripped, the problem of high false alarm is solved, meanwhile, a scoring model is constructed in combination with geometric and thermodynamic parameters, the damage degree and the expansion trend are quantified, and the accuracy of crack identification is improved. And finally, the crack expansion risk is pre-judged through the dynamic change trend of the thermodynamic parameters, a hierarchical early warning signal is triggered to form a sensing-anti-interference-assessment-decision closed loop chain, the whole process optimization from data fusion and interference suppression to risk assessment and maintenance response is realized, and the accuracy of road crack detection is guaranteed.

The invention belongs to the technical field of microwave measurement, and discloses an outer wall hollowing microwave reflection detection method based on multi-modal fusion, which comprises the following steps: carrying out multi-modal scanning on a building outer wall to be detected, obtaining visible light image data and infrared temperature distribution data of the outer wall surface, and carrying out space registration and coordinate mapping; a unified multi-modal fusion data set is formed; performing anomaly screening on the multi-modal fusion data set, and identifying a thermal anomaly region by analyzing infrared temperature distribution data; detecting a bump or crack area in combination with texture and morphology anomaly features of the visible light image data; performing information fusion on the thermal anomaly region and the bump or crack region, and extracting candidate detection regions of suspected hollowing; high-precision recognition and quantitative evaluation of the outer wall hollowing are achieved, and the precision and stability of outer wall hollowing detection are improved.

The invention discloses a wind power blade defect data automatic acquisition and analysis method based on an unmanned aerial vehicle, and particularly relates to the technical field of data acquisition and analysis. The method comprises the following steps: collecting blade surface heat distribution data, locally applying a heat source to a blade, collecting a temperature attenuation curve, calculating a heat diffusivity gradient difference value and a temperature attenuation time index, integrating a multi-parameter space, and classifying a heat distribution abnormal region into a normal region and a real defect region in combination with a deep learning model; cross validation is carried out through optical images and ultrasonic data, so that the detection accuracy is improved; dynamically adjusting a sensitivity threshold value and model parameters of the thermal imager according to a classification result, and generating a detection report; through data feedback iterative optimization model and task planning, the problems of misjudgment and missed judgment of the thermal infrared imager in complex material distribution are effectively solved, the accuracy, reliability and automation level of wind power blade defect detection are improved, and important support is provided for efficient wind power operation and maintenance.

The invention relates to the technical field of cable nondestructive testing, in particular to a cable defect detection system. The method mainly solves the problems that in the prior art, early-stage tiny defects in a cable are insufficient in recognition sensitivity, the omission ratio is high, and accurate classification cannot be achieved. According to the system, signals are synchronously collected through a multi-physics field composite sensing array, excitation parameters are optimized through a genetic algorithm, a three-dimensional defect probability graph is generated through fusion, a double-current Transform model is adopted to deeply analyze features and automatically recognize defect types, and finally, maintenance is guided through AR visualization, so that non-intrusive online accurate diagnosis and intelligent operation and maintenance of cable defects are achieved.

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 discloses a high-precision defect detection system for a track inspection robot, which relates to the technical field of track inspection and comprises a multi-mode visual acquisition module, a time sequence synchronization module, a data fusion processing module and a defect identification output module, by adopting a multi-modal visual fusion detection technology, various modal visual technologies of visible light vision, infrared vision and ultraviolet vision are combined, fusion processing is carried out on all modal image data, visual defects such as cracks and abrasion on the surface of a track can be clearly recognized through visible light vision, and the visual performance of the track is improved. The infrared vision can detect temperature abnormity of a track component to find out potential poor contact and other problems, the ultraviolet vision can be used for detecting corona discharge and other phenomena, track information is comprehensively obtained, the accuracy and reliability of defect recognition are effectively improved, defects which are difficult to find in a single mode can be detected, and the detection efficiency is improved. And powerful support is provided for safe operation and maintenance of the track.

The invention discloses a method, device and system based on acoustic-thermal multi-mode fusion imaging, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly, collecting ultrasonic and thermal imaging data of target industrial equipment through an acoustic imager, and obtaining acoustic and thermal imaging features respectively; performing feature integration processing to obtain a first acoustic-thermal joint feature so as to determine a first modal integration unit corresponding to the acoustic feature, completing cross-modal fusion to obtain a first feature descriptor, similarly obtaining a second acoustic-thermal joint feature, a second modal integration unit and a second feature descriptor, and determining a fault evaluation result in combination with a fault type label. And then calibrating image data to construct a three-dimensional acoustic-thermal fusion substrate image and marking a result to obtain a diagnosis fusion image so as to realize accurate diagnosis of the target industrial equipment.

The invention provides an on-line monitoring method and system for crack propagation of silicon-based new material equipment in a high-temperature environment, and relates to the technical field of crack detection, and the method comprises the steps: collecting stress distribution data through arranging a stress sensor, and generating a stress field distribution diagram by using an attention mechanism deep neural network model; identifying a stress concentration region based on a region growing algorithm, when a stress value exceeds a preset threshold value, acquiring temperature distribution data, extracting temperature distribution characteristics through a deep mixed probability model, performing multi-modal data fusion in combination with a stress field distribution diagram, and establishing a crack propagation prediction model by using a graph structure neural network. The crack propagation rate and direction are solved through a swarm intelligence optimization algorithm, when the crack propagation rate exceeds a preset threshold value, a control system adjusts equipment operation parameters, the sampling frequency of a sensor is adjusted according to the crack propagation direction, and model parameters are updated through an incremental learning method to achieve real-time monitoring.

The invention provides a thermal wave imaging detection method based on nonlinear frequency modulation microwave excitation induction, and the method comprises the steps: constructing a composite excitation module of Gaussian beam and dynamic phase compensation, decomposing high-frequency / low-frequency sub-pulses for alternate emission, synchronously collecting infrared non-uniform sampling and microwave polarization diversity data, and carrying out the detection of the thermal wave imaging. And through feature fusion of a time domain CNN and a frequency domain graph neural network, propagation parameters are iteratively corrected in combination with a thermal diffusion equation model, and defect and deep structure detection is realized. According to the method, the composite excitation structure is combined with high-frequency / low-frequency sub-pulse alternate emission, the micro-defect detection resolution and the deep structure penetrating capacity are remarkably improved, the feature robustness is enhanced through multi-modal data acquisition and cross-modal neural network fusion, detection depth self-adaptive optimization is achieved through a depth compensation model and Kalman filtering, and the detection precision is improved. The parameter precision is improved with the assistance of a terahertz spectrum coupling matrix and Landweber iterative inversion, and finally an advanced detection system integrating efficient excitation, multi-dimensional perception and intelligent analysis is formed.

The invention discloses an operation fault monitoring method and system for a new energy distributed photovoltaic power station, and relates to the related technical field of photovoltaic power station monitoring, and the method comprises the steps: obtaining the photovoltaic array information of a target distributed photovoltaic power station, and laying a plurality of monitoring units based on the photovoltaic array information; a photovoltaic image sequence and an infrared image sequence of the photovoltaic panel are collected, and abnormal temperature distribution is obtained; performing area detection on the photovoltaic panel difference image to obtain an abnormal area of the photovoltaic panel; analyzing and checking the abnormal detection result of the photovoltaic panel to generate abnormal distribution of the photovoltaic panel; and transmitting the abnormal distribution of the photovoltaic panel to a central monitoring system through wireless communication, and carrying out operation and maintenance planning. The technical problem that the accuracy and the reliability of fault identification are insufficient due to the fact that the surface damage abnormity of the photovoltaic panel is difficult to accurately identify and locate in the operation fault monitoring of the existing new energy distributed photovoltaic power station is solved, and the technical effect of improving the accuracy and the reliability of the operation fault monitoring of the photovoltaic power station is achieved.

The invention provides an automatic intelligent defect analysis method and system based on power transmission and distribution line unmanned aerial vehicle image acquisition. The method comprises the following steps: acquiring infrared thermal imaging time sequence data and multispectral imaging data of the surface of a wire through an unmanned aerial vehicle carrying an infrared thermal imager and a multispectral camera; calculating a temperature gradient direction of a conductor main body area, screening an abnormal hot spot area whose axial included angle with the conductor is smaller than a preset angle, and positioning an oxidation area boundary by using a spectral absorption attenuation curve of an oxidation layer in the multispectral data; performing space matching on the hot spot migration direction and the oxidation boundary to generate a visual map; establishing a dynamic association rule according to an included angle variable quantity of a hot spot path in the map and a spectrum abrupt change point in the oxidation region; according to the critical angle threshold value and the sudden change point number threshold value, the defect grade is judged, and a defect analysis report is generated. According to the technical scheme provided by the invention, the automation degree and accuracy of defect detection are improved.

The invention relates to the field of insulator nondestructive testing, and discloses an insulator product surface defect nondestructive testing method based on AI identification, and the method comprises a data acquisition module, a preprocessing module, an AI analysis module, a decision output module, a self-optimization module, and an edge calculation node. Through multi-modal data fusion and a deep convolutional neural network technology, accurate detection of surface defects such as cracks, dirt and damage is realized, the omission ratio and the false detection rate are reduced, and the detection precision is improved compared with the traditional manual inspection efficiency; visible light, infrared thermal imaging, ultrasonic waves and hyperspectral data are combined, the surface and internal defects of the insulator are comprehensively covered, the detection rate of tiny cracks and hidden dirt is increased, and the technical limitation of a single sensor is broken through.