48 results about "Thermal wave" patented technology

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 the technical field of metal surface coating preparation, and discloses a cladding layer surface and deep defect detection method based on pulse laser, which comprises the following steps: clamping and fixing a workpiece; pulse laser excitation and bimodal signal synchronous excitation are carried out; synchronously acquiring and recording dual-channel signals; thermal wave signal processing and surface / near-surface flaw analysis; performing ultrasonic signal processing and deep defect analysis; and carrying out information fusion and three-dimensional flaw reconstruction. According to the invention, the pulse laser beam is utilized to excite the thermal wave and the ultrasonic wave at the same time, various flaws from the surface to the deep layer can be covered through one-time detection, the limitation of a single detection mode is solved, and meanwhile, the depth information of the flaws can be obtained by analyzing the phase diagram of the thermal wave signal; three-dimensional distribution of flaws can be reconstructed by combining ultrasonic time-of-flight (ToF) analysis and infrared sequence images, and accurate positioning and quantitative evaluation are realized.

The invention relates to the technical field of first-aid medical engineering and intelligent medical equipment, and discloses a digestive tract hemorrhage patient liquid resuscitation decision support system which comprises a thermal wave signal generation and infusion execution module, a distributed thermal dispersion perception and vascular stiffness resolving module, a multi-physics field coupling modeling module and a double-target game decision control module. By superposing a micro-amplitude thermal wave signal in resuscitation liquid, the system calculates a vascular stiffness parameter of a patient in real time based on a thermal dispersion principle; and predicting the shear stress of the wall surface of the blood vessel at different flow velocities by utilizing a temperature change viscosity and blood vessel elastic mechanics coupling model. In decision control, the system determines the target flow velocity according to the microcirculation perfusion requirement, and reversely solves the required fluid dynamic viscosity according to the shear stress safety threshold value, so as to dynamically adjust the basic temperature of the infusion fluid. The fluid viscosity is reduced through physical field intervention, the high-flow-rate resuscitation requirement is met, meanwhile, the shear stress of the wall face of the blood vessel is effectively restrained, and the rebleeding risk is reduced.

The invention relates to a mixed porcelain insulator enhanced thermal wave nondestructive testing method, which comprises the following steps: applying photo-thermal excitation to a mixed porcelain insulator, so that the surface temperature of a silicone rubber layer in the mixed porcelain insulator is increased; after photo-thermal excitation of a preset time length is applied to the mixed porcelain insulator, whether the mixed porcelain insulator has defects or not is judged according to temperature distribution of a mixed porcelain insulator thermogram shot by a thermal imager, and if the temperature distribution of the mixed porcelain insulator thermogram is not uniform, the interface of the mixed porcelain insulator has defects; if the temperature distribution of the thermogram of the mixed porcelain insulator is uniform, determining that the interface of the mixed porcelain insulator has no defect; by means of the arrangement, the defect area of the mixed porcelain insulator is judged by additionally applying photo-thermal excitation to the mixed porcelain insulator and calculating the surface temperature difference between the defect area and the normal area, and the method has the advantages that environment changes are not seen, detection is convenient, the detection speed is high, the detection result is visual and visible, and the detection accuracy is high.

The invention relates to the technical field of crack monitoring, and discloses a metal material crack propagation monitoring device and method, a storage medium and a program product.The device comprises control equipment, acoustic emission equipment, ultrasonic equipment and infrared collection equipment, the acoustic emission equipment is arranged on a to-be-detected metal sample, and the ultrasonic equipment is arranged on the control equipment; the control equipment is used for receiving the acoustic emission signal, judging whether the to-be-detected metal sample is deformed or not according to the acoustic emission signal, and controlling the ultrasonic equipment and the infrared acquisition equipment after detecting that the to-be-detected metal sample is deformed; the ultrasonic equipment is arranged on the to-be-detected metal sample and is used for sending an ultrasonic excitation signal to the to-be-detected metal sample; the infrared acquisition equipment is used for acquiring a surface thermal wave signal of a to-be-detected metal sample and generating an infrared image; and the control equipment is also used for receiving and analyzing the infrared image to obtain crack information of the metal sample to be detected. The method can improve the crack propagation detection accuracy.

The invention provides an integrated thickness measurement feedback laser cleaning system, and relates to the technical field of laser cleaning, the integrated thickness measurement feedback laser cleaning system comprises a laser cleaning head, a light path transmission unit, a laser assembly, a non-contact photo-thermal thickness measurement head, a mechanical arm and a controller, the laser cleaning head and the non-contact photo-thermal thickness measuring head are arranged at the tail end of the mechanical arm at intervals, the laser cleaning head is connected with the laser assembly through the light path transmission unit, and the control unit is in communication connection with the mechanical arm, the laser assembly and the non-contact photo-thermal thickness measuring head. And the controller is used for controlling the mechanical arm to drive the laser cleaning head to clean the surface of the workpiece along a preset cleaning route according to a thermal wave signal collected by the non-contact photo-thermal thickness measuring head. The laser cleaning efficiency can be improved.

The application provides a bulletproof vest composite interlayer defect positioning method and system based on multi-modal nondestructive testing, electronic equipment and storage medium, relating to the technical field of nondestructive testing. The application synchronously applies ultrasonic guided waves, pulse eddy current and infrared thermal wave excitation signals to the bulletproof vest composite interlayer, collects multi-modal response signals at each position, and generates an original multi-modal response signal set. The original multi-modal response signal set is input into a pre-trained deep convolutional neural network to determine the target frequency band parameters and target noise suppression parameters of each modal response signal, and output adaptive modulation parameter groups. The original signal set is subjected to frequency domain filtering and noise suppression processing to generate an optimized signal set. The spatial frequency characteristics are input into a cross-modal feature fusion model. The attention mechanism in the model adaptively associates defect sensitive features of different modalities to generate a three-dimensional defect distribution map of the composite interlayer. Accordingly, millimeter-level precision defect positioning is achieved, which can improve the precision and efficiency of bulletproof vest composite interlayer defect positioning.

This invention discloses an adaptive thermally excited infrared thermal wave nondestructive testing method and device. The method includes: querying a pre-stored database based on the scanned position signal to obtain the theoretical standard energy distribution spectrum under the current pose; triggering pre-aiming sensing; acquiring and generating the actual pre-aiming energy distribution spectrum; comparing the two to calculate the actual energy non-uniformity, and comparing it with a preset threshold to decide whether to initiate compensation; driving the actuator to perform physical adjustments and confirming the adjustments; and finally synchronously triggering the main thermal excitation and infrared thermal image acquisition. The device includes a scanning motion and pose feedback module, a high-precision multispectral pre-aiming sensing module, an intelligent decision-making and synchronous control module, a finely adjustable main excitation execution module, and a data management and communication interface module. This invention can proactively correct the uneven excitation energy distribution caused by scanning geometric changes from the physical source, significantly improving the quality and reliability of the detected image.

To provide a thermoacoustic device with improved efficiency.SOLUTION: The thermoacoustic device 1a includes a fluid pipe 2 filled with a working fluid and including a pipe body part 21 and a pipe large diameter part 22 having an inside diameter larger than that of the pipe body part 21, a thermoacoustic converting part 3 disposed inside the pipe large diameter part 22, and an orifice part 4 disposed inside the fluid pipe 2. In the thermoacoustic device 4a, the orifice part 4 includes a through-hole 4a through which the working fluid flows, and a small orifice diameter part 4b in which an inside diametrical size of the through-hole 4b is smaller than other parts, and the small orifice diameter part 1a is arranged in a boundary part 23 between the pipe body part 21 and the large pipe diameter part 22.SELECTED DRAWING: Figure 1

The invention discloses an ion implantation thermal wave monitoring piece and a reuse method. The method comprises the following steps: providing a plurality of monitoring pieces used in the same batch; measuring a first thermal wavefront value of each monitoring sheet; when all the first thermal wave front values meet the consistency requirement, first ion implantation doped with a first gas element is conducted on the multiple monitoring pieces, and first thermal wave rear values of all the monitoring pieces are obtained; performing second ion implantation doped with a second gas element on the plurality of monitoring sheets after the first thermal wave back value is obtained, and annealing, so that the measured second thermal wave front value of each monitoring sheet meets the consistency requirement; the multiple monitoring pieces are used for carrying out first ion implantation again, and second thermal wave back values of all the monitoring pieces are obtained; wherein the second injection dose for performing the second ion injection is greater than the first injection dose for performing the first ion injection. According to the invention, the accuracy of V-shaped curve angle correction and the recycling frequency of the monitoring piece can be improved, and the recycling efficiency can be improved.

The invention relates to the technical field of infrared thermal wave nondestructive testing, and discloses a method, a system, equipment and a medium for improving an infrared thermal wave nondestructive testing effect. Inputting into a physical information neural network model which takes a physical constraint comprehensive model as a constraint and is trained by adopting a multi-task loss function based on a self-adaptive weight adjustment mechanism to carry out atmospheric error correction, and outputting an infrared correction heat map; inputting the correction result into a pre-trained target detection network model for defect identification, and outputting a defect identification result; by optimizing the excitation mode, the image correction algorithm and the defect recognition algorithm, the influence of atmospheric factors on the accuracy of an infrared thermal wave detection result under a long-distance detection condition is reduced, automatic defect recognition is realized, and technical support is provided for engineering application of an infrared thermal wave technology.

This invention discloses a method and system for detecting shallow defects in insulating tie rods based on infrared thermal wave images, belonging to the field of non-destructive testing technology for high-voltage electrical equipment. The method includes: applying thermal wave excitation to the surface of the insulating tie rod and acquiring a sequence of infrared thermal images; preprocessing the acquired infrared thermal image sequence; segmenting the defect region using a multi-scale feature fusion network; classifying and locating the defects using a deep learning model with an integrated attention mechanism, and optionally performing quantitative evaluation of the defect area and depth. The system includes a thermal wave excitation module, an image acquisition module, an image preprocessing module, a feature extraction module, and a recognition and classification module. This invention combines infrared thermal wave technology with deep learning algorithms, effectively improving the detection accuracy and automation level of shallow defects. It has advantages such as high recognition accuracy, comprehensive functions, and strong practicality, and is suitable for condition monitoring and intelligent operation and maintenance of power equipment.

The application discloses a 3D printing device for detecting and regulating integrated insulating material and an intelligent manufacturing method. The device comprises a printing module, a regulating module and a detecting module used in cooperation. The printing module comprises a moving platform, a printing tank, a screen plate, a cleaning rod and a light-curing surface light source. The regulating module comprises an electrode assembly and a regulating power supply. The detecting module comprises a laser thermal excitation assembly and an infrared thermal imager. The laser thermal excitation assembly is used for applying thermal loading to a printing sample. The infrared thermal imager collects surface temperature evolution information of the printing sample in the thermal loading process and transmits the information to a data processing system. The orientation and distribution of the thermally conductive filler are regulated by using the auxiliary effect of an electric field, and the thermal conductivity distribution state of the material is detected in real time by means of a thermal wave imaging technology. According to the detection result, the parameters of the applied electric field are dynamically adjusted, the precise closed-loop control of the distribution and orientation of the filler is realized, and thus an effective path is provided for the lean manufacturing of high-thermal-conductivity-low-loss insulating material.

The invention relates to the technical field of optical detection, in particular to a rapid optical detection method and system after chip cutting, and the method comprises the following steps: applying frequency-modulated continuous thermal excitation to a cut wafer, and synchronously collecting a space-time coupling response matrix generated by thermal wave transmission and a photoacoustic effect on the surface of the wafer; identifying a deep subsurface crack extension track according to the thermal wave reflection coefficient distribution, and identifying a metal residue enrichment area according to the photoacoustic quality factor distribution; and performing spatial cross-correlation operation on the thermal wave reflection coefficient distribution and the photoacoustic quality factor distribution, constructing a defect composite index diagram, and dividing a risk interval according to a numerical gradient of the defect composite index diagram. According to the method, the problems that penetration is difficult and the structure continuity cannot be judged in a traditional surface optical image method are solved, and the non-contact property, the space resolution and the defect type recognition capability of a detection system are remarkably improved.

The utility model belongs to the technical field of batteries, and discloses a composite protection piece, a battery cell, a battery module, a battery pack and a power utilization system. The composite protection piece comprises a first heat insulation layer, a heat dissipation layer, a second heat insulation layer, a first blocking layer and a second blocking layer, wherein the first heat insulation layer, the heat dissipation layer and the second heat insulation layer are sequentially stacked, the first blocking layer partially or completely isolates the contact face of the first heat insulation layer and the heat dissipation layer, and the second blocking layer partially or completely isolates the contact face of the second heat insulation layer and the heat dissipation layer. The heat dissipation layer and the heat insulation layer (namely, the heat dissipation layer and the first heat insulation layer and the heat dissipation layer and the second heat insulation layer) are separated, and heat dissipation materials in the heat dissipation layer are prevented from being embedded into the heat insulation materials in the heat insulation layer, so that the heat insulation performance of the heat insulation materials is reduced. The method is used for relieving the situation that thermal runaway of adjacent batteries is triggered by thermal waves due to transverse transfer of a large amount of heat generated by thermal runaway of single batteries in a battery module or a battery pack, and the thermal runaway can be caused by mechanical abuse, thermal abuse or electricity abuse and the like.

This invention relates to the field of industrial measurement and control, and more particularly to a wireless sensing system for the temperature field of a roasting furnace. The system includes a wireless sensing node module, an adaptive sampling control module, and a temperature field reconstruction module. The wireless sensing node module acquires real-time temperature data with spatial location information; the adaptive sampling control module calculates a heat wave sensitivity factor based on the real-time temperature, determines the adaptive sampling period by combining the heat wave propagation tendency of neighboring nodes, and collects key temperature data according to the adaptive sampling period; the reconstruction module receives the data and generates an isothermal surface cloud map through three-dimensional inverse distance weighted interpolation, completing the three-dimensional visualization of the temperature field and anomaly early warning. This invention adaptively adjusts the sampling period by using the heat wave sensitivity factor and heat wave propagation tendency, and accurately reconstructs the temperature field using a three-dimensional mesh and high-density interpolation, avoiding the distortion and high power consumption problems of fixed sampling, and improving the accuracy of temperature field reconstruction.

The utility model relates to the technical field of infrared thermal wave detection, and discloses a portable infrared thermal wave detection device which comprises two sliding pipe frames and a detection box, sliding holes are formed in the tops of the two sliding pipe frames, sliding plates are connected in the sliding holes in a sliding mode, clamping grooves are formed in the tops of the sliding plates, hanging lugs are fixed to the two sides of the detection box through bolts, and the two sides of the detection box are provided with clamping grooves. The top of the sliding plate is fixedly provided with a fixing frame through a bolt, the hanging lug is located between the fixing frame and the sliding plate, the opposite sides of the two sliding pipe frames are provided with locking assemblies for fixing the height of the sliding plate, and a folding assembly for connecting the two sliding pipe frames is arranged between the two sliding pipe frames. Through the arrangement of the universal wheels, the friction force between the device and the ground can be reduced, the device can move more easily and stably, when a large object is detected, the detection of different positions of the large object can be realized only by dragging the device to move, and the detection convenience of the device is improved.

The invention discloses a thermal wave intelligent nondestructive detection method for defects of a power battery GFRP type protection structure based on lightweight YOLOv8. The method adopts a high-accuracy lightweight detection model YOLOv8-GFRP, and compared with YOLOv8-n, firstly, EfficientNet is used for replacing a trunk structure, the parameter quantity of the model is greatly reduced, and the complexity of the model is reduced; then, the number of target detection heads is reduced, and the calculated amount and the parameter amount of the model are reduced to the minimum; secondly, C2fECA is introduced to replace C2f, and the detection precision of the model is improved by adding an ECA attention mechanism into a C2f module; thirdly, designing a DetectSE detection head by adopting an SE module, and extracting key features of GFRP defects, so as to improve the detection precision; finally, an Adamax optimizer is adopted, the optimization process of the improved model is fully played, and the target detection performance is improved. The model improved through the detection method is improved to a certain extent in the aspects of parameter quantity, calculation speed and calculation precision, and therefore the method has important significance in the field of power battery detection with GFRP as a protection structure.

The invention relates to the technical field of sludge drying devices, in particular to a sludge drying device in a sewage treatment process, which comprises a closed thermal wave carbonization furnace, and one side of the top of the closed thermal wave carbonization furnace is provided with a feed chute. The microwave preheating type slitting machine, the conveying and crushing mechanism and other components are arranged, and all the components are coordinated and matched, so that the microwave preheating type slitting machine can preheat, crush and extrude sludge into strips, the viscosity of the sludge is remarkably reduced, and the heating area of the sludge is increased; the conveying and crushing mechanism continuously crushes sludge cakes through a ratchet strip crushing rod in the conveying process, and meanwhile, an eccentric wheel drives a sliding block and a reciprocating plate to move, so that a pushing separation plate uniformly distributes sludge and separates uncrushed cakes, a scraping block timely cleans residues on the surface of the conveying device, and sludge adhesion, accumulation and hardening are effectively prevented; further, the effects of improving the drying uniformity and the treatment efficiency of the sludge and ensuring smooth conveying and stable treatment are achieved.

The invention discloses a fiber metal laminate defect detection method based on synthetic defects and deep learning, and the method comprises the following steps: S1, collecting an infrared thermal wave image of a normal sample of a fiber metal laminate, and carrying out the preprocessing of the infrared thermal wave image; s2, on the preprocessed image, generating a defect area with a regular shape through geometric modeling, and performing simulation to obtain a synthetic defect image; s3, simulating temperature distribution characteristics of the defect area by adopting a finite difference method; s4, performing data enhancement processing on the synthesized defect image; s5, performing defect classification training on the constructed deep convolutional neural network model by using the enhanced image, and storing an optimal network model; and S6, deploying the obtained optimal network model to an embedded processing unit of the infrared detection system to realize defect identification. According to the method, the dependence on real defect data can be reduced, the defect identification precision under the small sample condition is improved, and the method can be applied to defect detection of the fiber metal laminate.

The invention discloses a zinc dross mode identification method and system for a hot galvanizing unit, and relates to the technical field of zinc dross mode identification, and the method comprises the steps: collecting the original data of each coordinate point in a zinc liquid level region, the original data comprising polarization thermal wave radiation, eddy current complex impedance and ultrasonic echo; performing baseline differential correction on the original data to obtain correction data of each coordinate point; according to the correction data, characteristic parameters of polarization thermal wave radiation, eddy current complex impedance and ultrasonic echoes are generated respectively; calculating a paillette layer peak ratio according to the characteristic parameters of the polarized thermal wave radiation, calculating a double-frequency impedance amplitude difference according to the characteristic parameters of the eddy current complex impedance, and calculating a kernel double-reflection time difference according to the characteristic parameters of the ultrasonic echo; and generating an index vector according to the paillette layer peak ratio, the double-frequency impedance amplitude difference and the kernel double-reflection time difference. The slag removal efficiency of the continuous hot galvanizing production line can be improved.

The invention discloses an infrared thermal wave detection and three-dimensional modeling fused AI defect visualization method and device. The method mainly comprises the following steps: acquiring a target three-dimensional model and pulse infrared thermal imaging sequence data; preprocessing the data; mapping the multi-view thermal imaging data to the three-dimensional model; extracting low-dimensional feature representation from the fused data; performing unsupervised clustering on the features, and identifying potential defects; carrying out defect positioning and characterization based on a clustering result and statistical analysis; and carrying out visualization and report generation. The method can adapt to any curved surface structure model, high-robustness heat map mapping can be achieved through a small number of 3D-2D corresponding points, and free view angle collection is supported; the method realizes autonomous division and analysis of defect areas, significantly improves the intelligent level of the system, and is suitable for automatic defect screening in large-scale and repetitive tasks.

The invention relates to the technical field of climate event analysis, in particular to a regional composite thermal wave population exposure risk analysis method based on multi-source data. The method comprises the following steps: acquiring day-by-day temperature observation data, month-by-month reanalysis data of various meteorological elements and population distribution data in a target area; judging a composite heat wave event by adopting a percentile threshold method, and counting the composite heat wave frequency; on the basis of an inverse algorithm, calculating a whole-layer atmospheric heat source according to monthly meteorological reanalysis data; analyzing the correlation between the composite heat wave frequency in the target area and an atmospheric heat source, and establishing a linear regression equation; and calculating the population exposure degree of the composite heat wave in the target area caused by the abnormal change of the atmospheric heat source, and quantitatively evaluating the population exposure risk level. The method disclosed by the invention reveals the formation mechanism of the composite heat wave event from a new perspective, assists in improving the prediction capability, provides a solid scientific basis for accurately identifying the distribution of related high-risk crowds, and has important scientific significance and application prospects.

The invention discloses a defect detection method and system based on multi-mode light-induced thermal wave imaging, and the method comprises the steps: carrying out the multi-mode light excitation of a honeycomb sandwich structure through employing a pulse light source and a modulation light source as excitation modes, and generating a multi-mode infrared thermal wave signal; infrared thermal wave signals are continuously collected based on an infrared thermal imager, and timestamp alignment is carried out; based on the time domain features and the frequency domain features of the multi-modal infrared thermal wave signals, signal-level fusion and image-level fusion are carried out respectively, and a signal-level fusion image and an image-level fusion image are obtained; and inputting the original image, the signal-level fusion image and the image-level fusion image into an improved YOLO-World model, and realizing automatic identification and classification of defects through multi-scale feature extraction and a RepVL-PAN module. According to the method, the multi-mode optical excitation technology and the YOLO-World deep learning model are combined, efficient recognition of complex defect types in the honeycomb sandwich structure is achieved, detailed information about defects is provided from different angles, and the detection precision is remarkably improved.

The present application relates to the technical field of household appliances, in particular to an inner shell assembly of a cooking appliance and a cooking appliance with the same. The present application aims to solve the technical problem of slow heating of the existing cooking appliance. To this end, the present application provides an inner shell assembly of a cooking appliance, the inner wall of the inner shell assembly surrounds a heating cavity of the cooking appliance, the inner wall is made of a high reflectivity material and / or the inner wall is coated with a high reflectivity coating, and the inner wall protrudes into the heating cavity with a plurality of heat dissipation protrusions, the inner shell assembly further comprises a heating element for emitting heat waves into the heating cavity, the heat waves emitted by the heating element are uniformly scattered into the heating cavity after being reflected by the plurality of heat dissipation protrusions. The cooking appliance of the present application can improve the reflection effect of heat waves by using high reflectivity material and / or high reflectivity coating, at the same time, the plurality of heat dissipation protrusions can improve the scattering effect of heat waves, so as to improve the heating rate of the cooking appliance.

The application discloses a dark-field thermal wave confocal microscopic measuring device based on circular dichroism, which comprises the following steps: a time division multiplexing circular polarized light generating light path alternately generates left-handed circular polarized light and right-handed circular polarized light in the same period as thermal wave detection light; a thermal wave detection light shaping light path shapes the thermal wave detection light; a thermal wave pump light generating light path generates thermal wave pump light, and after the thermal wave pump light is modulated, the thermal wave pump light is combined with received thermal wave detection light to obtain combined light; a light beam illumination light path irradiates the combined light to a sample to be measured, and receives thermal wave reflection and scattering light; a dark-field balanced detection light path receives the shaped thermal wave detection light, extracts the scattering light of the thermal wave detection light in the thermal wave reflection and scattering light, and obtains a circular dichroism thermal wave dark-field signal according to the difference frequency signal between the reference light and the scattering light of the thermal wave detection light. The application can obtain the chiral information of various physical properties and micro-nano structures of sample defects.

The utility model belongs to the technical field of detection, particularly relates to a thermal wave detection device for the damage of a glue joint structure of a hidden framing glass curtain wall, and aims at solving the problems that although the detection can be carried out through an infrared imager, only the detection height of the infrared imager can be adjusted, and the use is inconvenient. Regular anti-skid marks are arranged on wheels of the movable vehicle body, a first electric push rod is fixedly installed at the top of the movable vehicle body, a U-shaped mounting frame is fixedly arranged at the output end of the first electric push rod, and a T-shaped fixing frame is arranged in the U-shaped mounting frame. An infrared imager used for detecting and recording the surface temperature distribution condition of the glass curtain wall gluing structure is arranged at the top of the T-shaped fixing frame. When the device is used, automatic parts such as the electric push rod and the driving motor are adopted, so that the height, the inclination angle and the position of the infrared imager and the flash lamp are flexibly adjusted, the operation process is simplified, and the operation difficulty is reduced.

This invention relates to the field of electronic component testing technology and discloses a non-destructive testing system for internal layering of electronic components. The system includes a frequency conversion thermal excitation module, a phase detection module, a surface phase reference extraction module, and a phase signal differential decoupling module. The system utilizes the frequency conversion thermal excitation module to apply dual-frequency thermal flux excitation to the surface, while the phase detection module acquires the corresponding thermal wave phase signal. The surface phase reference extraction module extracts background phase parameters characterizing the thermal inertia of the surface material, and the phase signal differential decoupling module performs differential subtraction on the detection signal based on these background phase parameters. This invention eliminates background phase noise caused by heterogeneous surface traces, ensuring that the phase signal purely points to the deep interface thermal impedance mismatch. It overcomes the signal aliasing constraints between ultra-thin packaging layers and improves the physical detection resolution for micron-level closed layering defects.

The application belongs to the technical field of material analysis, and particularly relates to a method and device for obtaining the performance of a two-dimensional structural material. The method comprises the following steps: for a two-dimensional structure, the two-dimensional structure is subjected to a thermal wave and an acoustic wave with a constant amplitude and a constant surface, and the axial stress and the shear stress are free; a fractional order heat conduction model is established, and a motion model, a constitutive model and a thermo-acoustic coupling model are established without considering body force; the fractional order heat conduction model, the motion model, the constitutive model and the thermo-acoustic coupling model are subjected to dimensionless processing to obtain a control equation; the control equation is solved by using a potential function method and wave theory to obtain the mechanical performance and acoustic performance distribution results of the two-dimensional structure. The establishment of the fractional order heat conduction model and the solving of the control equation by using the potential function method and the wave theory can improve the accuracy of the solving results of the mechanical performance and acoustic performance distribution law of the two-dimensional structure.

The application relates to the technical field of infrared thermal wave nondestructive testing and material thermal physical property measurement, in particular to a high-precision thermal diffusivity inversion method based on a multi-terminal deep neural network. The application aims to solve the problems of low calculation efficiency, strong noise sensitivity and insufficient robustness in the traditional thermal diffusivity measurement method, and proposes a high-precision thermal diffusivity inversion method based on a multi-terminal deep neural network for materials containing damage defects. Specifically, the application uses a phase-locked thermal imaging technology to extract the amplitude and phase of the thermal wave, and obtains stable characteristics describing the thermal diffusion process. A deep learning network is constructed with spatial coordinates, excitation frequency, surface temperature information, amplitude and phase as inputs. Through multi-modal data fusion, advanced direct current component removal technology and multi-terminal deep neural network architecture, efficient and accurate measurement of the thermal diffusivity of complex materials is realized.