973results about "Material flaws investigation" patented technology

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 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 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 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.

The invention discloses a visual combined infrared precast beam appearance defect detection method, and relates to the technical field of civil engineering structure detection and nondestructive testing, and the method comprises the following steps: establishing an illumination time sequence observation layer, collecting multi-dimensional optical parameters aiming at the surface of a precast beam, and generating a dynamic reflection fingerprint baseline; and performing coherent phase decomposition based on the dynamic reflection fingerprint baseline, separating the specular reflection signal from the material texture signal, and calibrating light spot track and intensity evolution data in the unified baseline. According to the method, an illumination time sequence observation layer is constructed to generate reflection fingerprints, coherent phase decomposition and anti-fact playback are combined, reflection interference is stripped, and crack boundaries are recovered; time coordinates are reconstructed through double-mirror-image anchor points, and crack evolution is accurately recovered; phase conjugate projection and light field traction are combined, a dynamic threshold optical fence is established, the exposure rhythm is controlled in a closed-loop mode, and precise detection of the defects of the precast beam is achieved.

The invention provides an intelligent pipe surface quality detection method and system based on machine vision, relates to the technical field of industrial automatic detection and machine vision, and aims to establish a pipe surface feature library and mark shape abnormal features. The method comprises the following steps: collecting an image of a pipe in a bright and dark composite light field, extracting gray and texture features after polarization filtering processing, reconstructing a three-dimensional point cloud covering a mortar layer and a concrete layer by matching a principal component analysis dimensionality reduction fusion feature set with a feature library, and converting the point cloud into a two-dimensional expansion graph through cylindrical projection; a mortar abnormal area and a concrete abnormal area are segmented, the hole volume is calculated through point cloud residual errors in the mortar area, and internal hollowing is detected in combination with acoustic vibration excitation and thermal response; the concrete area locates defects based on point cloud features; according to the sequence of the concrete covering process before the mortar covering process, a correlation model is constructed to match and coincide the defect sites, and the detection result is output, so that the detection automation degree and accuracy can be improved.

The invention discloses a rubber product quality detection method and system, and relates to the related technical field of rubber quality detection.The method comprises the steps that rubber products are conveyed to a target detection table, and data acquisition of the rubber products is executed; inputting the wave impedance distribution data and the surface heat distribution gradient field into a defect causal atlas channel to execute phase inversion fusion processing, establishing a cross-modal coupling phase tensor, and identifying deep defect response characteristics by solving a phase difference matrix; activating a pressure spectrum sensing unit to apply control pressure, reading a local mechanical response spectrum, sending the local mechanical response spectrum to a cross-modal calibration layer, and executing joint error authentication; and performing quality detection report according to a self-calibration defect mapping result. The technical problems that in the prior art, multi-modal sensing data fusion efficiency is low, deep defects are difficult to comprehensively recognize, and consequently the quality detection precision and reliability of rubber products are insufficient are solved, and the technical effects that cross-modal cooperative detection is achieved, and the precision and reliability of defect recognition results are improved are achieved.

The invention relates to the technical field of surface defect detection, in particular to a structure surface disease diagnosis method and system based on multi-modal edge calculation, and the method comprises the following steps: obtaining an infrared thermal image frame image and constructing a temperature difference image group, enhancing visible light texture features to generate an enhanced image group, carrying out image registration, extracting a combined feature vector, and carrying out classification and recognition. And mapping the boundary of the defect area to generate a coordinate set, counting disease information and generating a visual display layer. According to the method, thermal anomaly features in different areas can be visually expressed through temperature mapping and space division operation of the infrared thermal imaging image, accurate judgment of defect types is realized through a trained neural network model, a boundary coordinate point set of structure surface defects is extracted through an image mapping means, and the accuracy of the structure surface defects is improved. And in combination with connectivity operation and a rectangular frame construction mode, defect positions are labeled and positioned, so that the accuracy of building surface disease identification, the reliability of boundary extraction and the integrity of zoning risk presentation are effectively improved.

The invention discloses a bridge structure low-altitude inspection and disease assessment system and method based on deep learning, and belongs to the technical field of bridge disease detection and structure health monitoring. The system comprises a multispectral adaptive image acquisition module, a multi-scale disease detection and feature extraction module, a digital twin mapping and disease positioning module and a disease evolution prediction and maintenance decision module. The system dynamically adjusts acquisition parameters according to environmental conditions, accurately identifies diseases of different scales based on a multi-scale convolutional neural network, realizes centimeter-level accurate positioning of the diseases through a three-dimensional digital twinborn model, analyzes a disease time sequence evolution trend and generates graded maintenance suggestions, and realizes adaptive optimization through a closed-loop feedback mechanism. The method is high in environmental adaptability, high in detection precision and accurate in positioning, has disease evolution analysis capability, and provides comprehensive technical support for bridge health monitoring and intelligent management.

The invention discloses a quality detection method in a functionally graded material preparation process, and relates to the technical field of functionally graded materials.The quality detection method comprises the following steps that the temperature of a green body in the functionally graded material preparation process is collected, and green body temperature distribution data is obtained; performing real-time thickness measurement on the green body according to the green body temperature distribution data to obtain a green body thickness change curve; identifying an abnormal fluctuation section in the thickness change curve of the green body, and carrying out phase detection on a physical region with the abnormal fluctuation section of the green body to obtain regional phase composition data; performing internal defect scanning on the green body according to the regional phase composition data to obtain an internal defect map of the green body; performing quality evaluation on the green body based on the green body internal defect map to obtain a quality grade judgment result; according to the method, the ultrasonic flaw detection scanning range is guided based on the phase abnormal region, and high-resolution internal flaw detection can be performed on the high-risk region of the functionally graded material in a targeted manner.

The invention relates to the technical field of industrial vision, in particular to a ceramic glazed tile internal defect detection method based on image recognition, which comprises the following steps: acquiring multi-modal data including an X-ray tomography sequence, a surface heat map, a visible light image and production process parameters; constructing a three-dimensional digital twin based on X-ray data, fusing surface image information, and utilizing a three-dimensional segmentation network to accurately identify three-dimensional geometric features of internal and underglaze defects; carrying out physical causal reasoning on the geometrical characteristics of the defects through the constructed defect-cause knowledge graph reasoning model, and intelligently matching and identifying the physical causes of the defects; targeted correlation analysis and traceability are performed in combination with physical causes and production process parameters, production links causing defects are accurately positioned, and a control instruction for optimizing production is generated. According to the method, closed-loop defect detection of'diagnosis-traceability-control 'is realized, the defect problem can be accurately positioned, and automatic decision support is provided for optimizing production and improving product quality.

The invention provides a textile cloth defect real-time detection method and system based on multi-modal feature fusion, and the method comprises the steps: collecting visual image data, infrared thermal imaging data and ultrasonic acoustic data of textile cloth through a multi-sensor array, and forming multi-modal input; performing time sequence alignment and noise filtering preprocessing on the multi-modal data to eliminate motion artifacts and environmental interference; a parallel feature extraction module is used for extracting texture features from the visual data, extracting temperature distribution features from the thermal imaging data and extracting acoustic impedance features from the acoustic data. By deeply fusing complementary information of three modes of vision, thermal imaging and ultrasonic wave, the detection capability is improved, the vision mode captures surface texture details, the thermal imaging mode reveals thermodynamic anomalies related to friction and materials, the ultrasonic wave mode perceives subcutaneous structure defects, hidden flaws which cannot be recognized by a single mode can be found, and the detection efficiency is improved. Therefore, the omission ratio is greatly reduced, and flaw types are distinguished more accurately.

The invention discloses a cable channel heating defect checking method based on a multi-legged inspection robot, and relates to the technical field of radiation temperature measurement, and the method comprises the steps: controlling the multi-legged inspection robot to autonomously advance in a cable channel, and collecting multi-source sensing data; constructing spatial-temporal feature representation of the cable channel based on the multi-source sensing data, and constructing a multi-modal graph neural network model to perform heating anomaly detection to obtain an initial detection result; when a suspected defect exists in the initial detection result, a multi-legged inspection robot is scheduled, a reinspection path and a detection mode are selected according to a reinforcement learning strategy, and a multi-modal graph neural network model is used for performing close-range reinspection on the position of the suspected defect; and performing fusion analysis on the initial detection result and the reinspection result, and outputting positioning information, defect type and confidence of the heating defect of the cable channel. According to the method, high-sensitivity identification of potential heating anomalies is realized, the missing report rate is remarkably reduced, the reinspection efficiency is improved, and repetition and omission are effectively avoided.

The invention discloses a photovoltaic panel defect inspection method and system based on hydrogen energy unmanned aerial vehicles, and the method comprises the steps: scheduling two hydrogen energy unmanned aerial vehicles to work synchronously, and collecting visible light and infrared thermal image sequences of the front and back surfaces of a double-sided photovoltaic panel; analyzing a photovoltaic array CAD / BIM drawing, extracting geometric coordinates, topological structures and numbers of components, and establishing a local coordinate system; edge contours are extracted from front and back visible light images in a differentiated mode and matched with drawing coordinates to construct pixel-physical coordinate mapping; an abnormal area is positioned through the temperature gradient of the infrared thermogram, and an assembly number is bound; a thermoelectric coupling decoupling model is constructed, real defect data is obtained through heat conduction compensation and current balance correction, and a defect type is identified in combination with a double-current convolutional network; and superposing the front and back defect data to a view layer corresponding to the drawing, generating an independently labeled defect distribution diagram, and outputting the defect distribution diagram to a management platform. The problem of misjudgment caused by thermoelectric interference during inspection of the double-sided photovoltaic panel is solved, and the defect positioning and recognition precision is improved.

An inspection system detects defects within a component using eddy current thermography. An infrared camera detects heat from a defect within the component generated by the eddy current interfering with the defect. The eddy current is generated in the component by a magnetic field applied to the component by an inductor assembly. The inductor assembly includes a magnetic core having magnetic core material and two arms, each arm including a coil to generate the magnetic field. The inductor assembly is configured to apply the magnetic field at an angle offset from a horizontal axis of the magnetic core such that the eddy current flows at an angle within the component relative to the offset angle. Further, the magnetic core material can be shaped to apply the magnetic field at the offset angle or to accommodate complex component shapes.

The invention relates to the technical field of spinning quality detection, and discloses a jute spinning product quality detection method and system based on an intelligent sensor. The method comprises the following steps: acquiring real-time process parameters of a jute spinning production line, and synchronously capturing yarn surface temperature field distribution in combination with an infrared thermal imager; performing space-time alignment processing on the yarn tension fluctuation data and the temperature field distribution to generate a fused dynamic process characteristic matrix; according to the tension gradient change rate and the temperature field abnormal region coordinates in the dynamic process characteristic matrix, dividing potential occurrence sections of yarn quality defects; a microstructure image of the yarn in the potential generation section is extracted, a fiber arrangement angle deviation value is measured through a polarized light interferometer, and fiber molecular bond vibration frequency data is obtained in combination with a Raman spectrometer; and inputting the fiber arrangement angle deviation value and the molecular bond vibration frequency data into a cascade quality analysis model, and outputting a quantitative index set of the internal structure defects of the yarns.

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 relates to the technical field of image data processing, in particular to a workshop filter defect detection method and system based on image processing, and the method comprises the steps: calculating the real-time rotating speed of a fan based on image analysis; dynamically adjusting the frame rate of the camera according to a rotating speed comparison result; reconstructing a three-dimensional surface model of the equipment; performing camera vibration compensation through the coding mark points; fusing the visible light image, the thermal imaging and the three-dimensional model data to generate a comprehensive diagnosis result; compared with the prior art that blade images are blurred and illumination and shadow interference cannot be accurately distinguished when a fixed frame rate camera fluctuates in rotating speed, the method has the advantages that the frame rate is dynamically adjusted by tracking the fan rotating speed deviation in real time, and multiple groups of images of each blade at the same rotating angle position are captured; the blade tip gradient vector is combined to analyze illumination shadow distribution characteristics, real defects and shadow artifacts are effectively separated, the microcrack recognition accuracy is remarkably improved, and meanwhile misjudgment caused by sudden change of environment illumination is avoided.

The invention discloses an online defect detection method for a main-grid-free photovoltaic cell panel, which comprises the following steps of: obtaining a life distribution diagram of a main-grid-free photovoltaic cell through time-resolved photoluminescence, and obtaining a heat dissipation diagram of the main-grid-free photovoltaic cell through optically-induced phase-locked thermal imaging; an ultrasonic diagram of the main-grid-free photovoltaic cell is obtained through laser ultrasound; preprocessing and registering the life distribution diagram, the heat dissipation diagram and the ultrasonic diagram so as to uniformly map the life distribution diagram, the heat dissipation diagram and the ultrasonic diagram to a cell coordinate system, extracting minority carrier life, heat dissipation power, ultrasonic propagation time delay offset and scattering energy, and forming a multi-modal feature together with a thermal phase; and based on the abnormal score, performing defect detection and classification on the multi-modal features of the possible defect area to obtain classification results of electrical defects, thermal defects and mechanical defects. The invention also discloses a system for realizing the method. According to the invention, full-coverage detection of electrical, thermal and mechanical defects is realized.

The invention discloses a textile fabric production on-line monitoring system based on intelligent manufacturing, and relates to the technical field of textile intelligent detection. By obtaining a textile microstructure image sequence, a tension disturbance signal and infrared thermal imaging data, an energy-structure coupling multi-dimensional feature matrix is constructed, a defect candidate area is extracted in combination with a textile weave structure map, and the defect of the textile fabric production on-line monitoring system is obtained. Constructing a perturbation evolution path set; a dynamic tension response model is introduced to fit a tension energy curved surface, and spatial probability distribution of structural instability points is output; in combination with loom state parameters, a long-short-term memory recurrent neural network is constructed, and abnormal score calculation and dynamic threshold comparison are achieved; when the score value exceeds the limit, triggering a high-frequency image sampling and enhanced recognition process, extracting micro-scale defect features and completing classification and recognition; outputting a defect visual thermodynamic diagram to realize real-time early warning of defects; the method has the advantages of high identification precision, fast early warning response and low false alarm rate, and is suitable for intelligent quality monitoring in a complex weaving scene.

The invention provides a lithium ion battery defect detection method and system, and relates to the field of battery manufacturing and quality inspection, the method comprises the following steps: acquiring and preprocessing a CT scanning image and infrared thermal imaging data of a lithium ion battery to obtain multi-modal fusion data; performing multi-modal feature extraction based on the multi-modal fusion data to obtain a fourth feature; and based on the fourth feature, obtaining a detection defect log of the lithium ion battery. According to the method, the limitations of low precision, difficulty in type distinguishing, dependence on manual judgment and the like in the aspect of identifying the internal structure defects of the battery in a traditional method are overcome, the accuracy, the automation degree and the result traceability of defect detection are improved, and the method is suitable for high-precision defect detection scenes in links such as lithium ion battery production manufacturing and quality detection.

The invention discloses an automatic identification method for hot spot defects of a photovoltaic module based on thermal infrared imaging, relates to the technical field of defect identification, and aims to improve the environmental adaptation precision and the multi-frequency thermal wave penetration coverage dimension in the detection process of the hot spot defects of the photovoltaic module, realize efficient and accurate multi-frequency excitation parameter configuration and environmental thermal noise suppression and improve the detection accuracy of the hot spot defects of the photovoltaic module. The defect identification accuracy and the fault diagnosis reliability level in the operation and maintenance process of the photovoltaic module are remarkably improved, the situation that response of fixed excitation parameter setting to actual thermophysical property changes is insufficient is effectively prevented, depth deviation and defect misjudgment caused by experience judgment are avoided, the pertinence and defect identification effect of final heat source distribution field reconstruction are effectively improved, and the method is suitable for large-scale popularization and application. And determining the packaging layer to which the defect influencing the performance of the photovoltaic module belongs, and ensuring that the defect layering identification measure is highly matched with the actual heat source depth distribution risk.

The invention relates to an insulator image segmentation method based on an infrared enhancement image of an unmanned aerial vehicle, and relates to the technical field of image processing, and the method comprises the steps: collecting an infrared image sequence through an infrared enhancement camera, obtaining a standard infrared image sequence, and carrying out the state recognition through an image perception prior engine, outputting a predicted insulator attention heat map and a predicted boundary confidence map; optimizing and adjusting the initial threshold segmentation algorithm and the initial region growing algorithm, and obtaining an adaptive threshold segmentation algorithm and an adaptive region growing algorithm; and carrying out image segmentation on the standard infrared image sequence to output an insulator image sequence, and carrying out early warning judgment on the insulator image sequence based on an adaptive early warning mechanism. The method solves the problems that a traditional insulator image segmentation method cannot effectively deal with the problems of low infrared image temperature contrast ratio, large noise interference and complex background, so that the insulator segmentation is easy to cause mistaken segmentation and missing segmentation, and the high-precision requirement of fault detection is difficult to meet.

The invention provides a steel shell concrete interface void detection system and method, and relates to the technical field of civil engineering structure health detection, and the system comprises a mobile scanning unit, an excitation unit, an infrared imaging unit, a signal processing and acquisition unit and a comprehensive analysis terminal. The method comprises the following steps: controlling a mobile scanning unit to carry out step-by-step impact excitation and signal acquisition so as to obtain a frequency diagram, and synchronously sampling by using an infrared thermal imager so as to obtain a thermal analysis diagram; and then binarizing the two images respectively, fusing the two images by adopting a noise suppression algorithm to generate a comprehensive image, and judging that the interface is void according to the fused image. According to the method, the high sensitivity of the impact echo method to the layering defect and the accurate description capability of the infrared thermal imaging method to the defect shape are fused, so that nondestructive, efficient and high-precision detection of the steel shell concrete interface void is realized, and the misjudgment and leak detection risks of a single method are effectively reduced.

The invention belongs to the technical field of boiler heating surface high-temperature corrosion monitoring, and relates to a boiler heating surface high-temperature corrosion online monitoring method and system based on data fusion. The method comprises the following steps: acquiring an electrochemical signal, infrared thermal image data and acoustic emission characteristics of a key corrosion sensitive area of a boiler heating surface; based on data collected by a sensor network, a corrosion rate dynamic prediction model composed of a physical driving sub-model and a data driving sub-model is adopted to obtain a final corrosion rate prediction value; fusing the infrared thermal image data and the acoustic emission features, calculating a regional corrosion risk confidence coefficient according to the fused infrared thermal image data and acoustic emission features, and outputting a confidence coefficient level according to the regional corrosion risk confidence coefficient; and outputting an early warning signal according to the final corrosion rate predicted value and the confidence level, and dynamically adjusting combustion parameters. Accurate prediction of high-temperature corrosion of the boiler heating surface is realized, and corrosion development of the boiler heating surface is actively inhibited.

The invention relates to the technical field of wallboard detection, and discloses an online visual detection system and method for a multilayer bamboo wallboard, and the system comprises a multi-dimensional information fusion module, a reference field texture analysis module, a statistical anomaly identification module, a time sequence thermal excitation module, an isotherm form recognition module and a feature matching and grading module. Pixel-level multi-dimensional information fusion is carried out on the visible light band signal and the near-infrared band signal of the multispectral image set to obtain a fused image; taking the typical trend of the bamboo natural fiber as a reference field, and comparing and analyzing the fiber texture trend to generate a texture feature map; identifying a texture abnormal area of which the fiber trend consistency is lower than that of the reference standard; applying thermal excitation in the texture abnormal area to adjust an intensity time sequence to obtain a time sequence gradient temperature field; recognizing a fracture form to obtain defect boundary and depth features, and outputting a quality grade after matching the defect boundary and depth features with a grading standard; according to the invention, the detection efficiency of the multi-layer bamboo wallboard can be improved.

The invention relates to the technical field of image recognition, and discloses a glass fiber board production quality detection method and system, and the method comprises the steps: collecting a continuous infrared thermal image sequence through an infrared thermal imaging device; constructing a temperature difference image set; identifying a primitive region with an abnormal diffusion characteristic; further performing fitting enhancement and significance detection on the abnormal primitive region; and performing defect classification and quality judgment based on the regional feature vector. Compared with the prior art, the technical problems that in the prior art, high-precision parting judgment cannot be achieved under the conditions that the surface texture of a glass fiber board is complex, hot-pressing disturbance exists, particularly, hot diffusion behaviors of dry filaments, degumming and cavitation bubble defects are similar, and the false detection rate of a conventional algorithm is high are solved. Due to the fact that a multi-channel thermal diffusion consistency analysis mechanism and parallel discrimination logic based on feature vectors are introduced, infrared primitive anomaly enhancement detection and defect classification recognition under the complex background are achieved, and the accuracy of glass fiber board production quality detection is improved.

The invention discloses a magnesium alloy electric drive main shell mold wear detection method and system based on machine vision, and belongs to the technical field of mold predictive maintenance. According to the method, three-dimensional point cloud, a surface texture image and an infrared thermogram of a mold are synchronously collected, and point cloud data fused with multi-modal information is generated through multi-sensor joint calibration and high-precision registration; geometric, texture and thermodynamic features are extracted and fused from the features, and a cross-modal feature vector is constructed; intelligent judgment of the wear level of the mold and accurate prediction of the remaining service life are achieved through the trained LightGBM ensemble classification model and the long-short-term memory network time sequence regression model; and finally, associating and visualizing the evaluation result and the three-dimensional model, and automatically generating a structured detection report. According to the method, comprehensive quantification, intelligent evaluation and accurate prediction of mold wear are realized, and the detection efficiency and the scientificity of maintenance decision are remarkably improved.

The invention discloses a chip packaging body defect detection method, and relates to the field of chip detection.The chip packaging body defect detection method comprises the steps that a feeding position, a visible light detection position, a material connection position, an infrared light detection position and a discharging position are arranged on a rack side by side; the first conveying module lifts the carrier plate at the visible light detection position so that the scanning head can carry out line scanning imaging on the carrier plate units, the second conveying module lifts the carrier plate at the infrared light detection position so that the infrared system can carry out imaging, and physical separation and process relay of visible light and infrared detection are achieved. On the basis, position offset and surface defects are obtained based on visible light images of the carrier plate units, internal subfissures are recognized in combination with infrared images of the chip bodies, and unit-level judgment is generated by integrating multi-source results, so that the efficiency bottleneck of a traditional start-stop mode is avoided, and the coverage blind area of single-mode detection is overcome; and the takt, the integrity and the accuracy of chip packaging body defect detection under a high-productivity production line are remarkably improved.

The invention relates to the field of chip detection, and discloses a high-efficiency chip detection system and method, which are used for realizing long-term performance degradation detection of a chip. Comprising the following steps: synchronously acquiring power pin electric signals and chip surface thermal imaging data to generate an electrothermal coupling characteristic data set in a working state of a to-be-tested chip, and extracting impedance and heat conduction characteristic parameters through impedance spectroscopy analysis and thermal time constant calculation. And analyzing an internal signal transmission path based on impedance characteristic parameters to obtain path characteristic parameters, establishing a mathematical model of dynamic current and working voltage of the chip by combining the two, generating a system identification result, and finally generating a chip detection result and fault positioning information by integrating various parameters. According to the invention, system benchmark testing and environmental interference monitoring and compensation are carried out, and high precision and high robustness of a detection result are realized.