4536 results about "Ultrasonic detection" patented technology

The invention discloses a polyethylene plastic hollow plate defect detection method and system, and relates to the technical field of nondestructive testing, the system is composed of a plurality of functional modules, and the system comprises: a multi-mode sensing module for collecting multi-source data including a hollow plate surface image, three-dimensional morphology data and an ultrasonic reflection signal; the historical database construction module is used for performing space-time alignment and feature level fusion on the multi-source data to generate a composite defect feature matrix; collecting ultrasonic signal waveforms of known defects, extracting characteristic parameters, and establishing a historical waveform library; calculating a characteristic parameter classification range of each type of defects based on a historical waveform library; judging whether the detection waveform parameter falls into a classification range corresponding to the processing noise or not, and if so, rejecting the signal; otherwise, keeping; and the defect area identification module is used for dividing the surface of the hollow plate into uniform grids based on the surface image of the hollow plate, and counting the density of the residual ultrasonic detection sources in each grid.

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 relates to the technical field of defect detection, and discloses a flange forging defect detection method and system. The method comprises the following steps: performing three-dimensional scanning and material acoustic characteristic measurement on a to-be-detected wind power flange to obtain layer partition sound isolation path data; generating an array element excitation control file of the double-array phased array ultrasonic detection system; applying the array element excitation control file to a double-array phased array ultrasonic detection system, and performing micro defect feature enhancement on a received echo signal to obtain a feature-enhanced signal data set; performing ultrasonic emission and data acquisition on the wind power flange to obtain a global detection data set; and inputting the global detection data set into the two-stage defect detection model for defect feature extraction and classification evaluation, and generating a defect detection evaluation report. According to the method, the detection rate and the classification accuracy of the micro forging defects are improved.

The invention discloses a railway track damage detection method, and belongs to the technical field of railway track detection. According to the method, an image acquisition module and an ultrasonic detection module are installed at the bottom of a track detection vehicle, the detection vehicle is controlled to run, and track top face and side face image sequences and ultrasonic reflection signals are acquired; preprocessing the image, and respectively inputting the image into a deep convolutional neural network model and a support vector machine classifier to obtain a crack identification result and a wear level; processing an ultrasonic reflection signal, and judging a layering defect; and finally fusing the data, marking a damage position and generating a structured detection report. According to the method, the problems of incomplete detection, low precision and the like in the existing railway track damage detection are solved, efficient detection of track surface cracks, side abrasion and internal layering defects is realized through collaborative acquisition of the multi-modal sensor, intelligent algorithm processing and data fusion, and the comprehensiveness and reliability of detection are improved.

The invention relates to the technical field of data processing, and discloses a steel structure welding seam quality ultrasonic detection method and system. The method comprises the following steps: scanning a welding seam to obtain digital parameters, constructing a virtual model to generate an ultrasonic response database, optimizing a detection parameter forming scheme, executing distributed acquisition to obtain a signal matrix, extracting defect features by using a neural network, and outputting a digital detection report through transfer learning. According to the method, automatic optimization of detection parameters and accurate extraction and intelligent analysis of defect features are realized, the accuracy, efficiency and reliability of detection are improved, and the dependence on artificial experience is reduced.

The invention belongs to the technical field of engineering supervision, and discloses a concrete strength measurement system and method for civil construction engineering, and the method comprises the following steps: a test point planning module determines the number and position of detection points; the route planning module plans a running route of the detection mechanism; the detection mechanism sequentially moves to each detection point position according to the planned route; the positioning module analyzes the distance from the ultrasonic probe to the laser; the ultrasonic probe A is aligned with the ultrasonic probe B; the ultrasonic probe A and the ultrasonic probe B scan a concrete building; detecting the strength of the concrete building by using a rebound apparatus; the humidity acquisition module acquires humidity data of concrete; the temperature acquisition module acquires environment temperature data; and the ultrasonic analysis module comprehensively calculates the concrete strength by combining the rebound value, the concrete humidity, the environment temperature and the ultrasonic detection sound velocity. According to the invention, concrete strength on-site detection can be carried out on a high-rise building; by integrating various data, the strength of the concrete can be evaluated more accurately.

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 provides a metal material defect identification and analysis method and system based on a multiple detection technology, and relates to the technical field of metal defect detection, and the method comprehensively utilizes microscope detection and ultrasonic detection technologies to obtain surface topography data and internal structure data of a target metal material. And performing feature alignment and fusion processing on the two types of data through a multi-grid method to generate a unified feature distribution model, and establishing a mathematical solution model of ultrasonic wave field propagation based on the model. And determining the position, shape and size information of the potential defect by using the solved data, and generating defect positioning data containing three-dimensional coordinates and contour features. And extracting key defect information in combination with an edge detection algorithm, judging defect types by adopting a classification algorithm, and generating a detection result. According to the method, the problems of insufficient detection precision, high data fusion difficulty and low classification efficiency in the prior art are solved, and the applicability and reliability of a defect detection technology are improved.

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

The invention relates to the technical field of stone structure ultrasonic detection, in particular to a stone structure building detection method based on a concrete ultrasonic fault detection scanner, and the method comprises the steps: obtaining the initial signal intensity when an ultrasonic signal is transmitted by using the ultrasonic fault detection scanner; obtaining an ultrasonic reflection signal after the ultrasonic signal is reflected at each reflection point under each scanning layer, the intensity of the reflection signal after reflection, and the signal propagation duration; the method comprises the following steps of: obtaining a signal propagation time difference, a signal intensity difference, a signal difference index, a signal disorder degree, a defect evaluation coefficient, a seam recognition degree and a seam correction coefficient of each reflection point under each scanning layer, correcting the intensity of a reflection signal, performing three-dimensional reconstruction on a constructed tomography graph, and detecting internal defects of a stone structure building. According to the method, the interference of the reflection points on the splicing seams during stone splicing is reduced, the accuracy of detecting the internal defects of the stone structure building is improved, and the stability detection of the stone structure building is improved.

The invention relates to the technical field of engineering project management, in particular to an engineering project construction quality management method and system, and the method comprises the following steps: extracting multiple items of design information of a target building based on project construction drawing information, and simulating and analyzing the stress distribution of a building structure under multiple loads through building a building information model. And evaluating the stability of the target building design, identifying and marking design defects, and obtaining a simulation analysis result. According to the method, by extracting the design information, constructing the building information model, evaluating the stability of building design, recognizing the design problem in the early stage, calculating the actual solidification rate of concrete and detecting the concrete quality, reworking and overhigh cost caused by the concrete quality problem are reduced, and defects in prefabricated parts are detected through ultrasonic waves; the construction safety and quality are improved, the construction deviation is quantified through measurement of an actual construction image and comparative analysis of the actual construction image and a design drawing, and efficient management of the construction quality of a target project is achieved.

The invention discloses an intelligent operation system for aquaculture management, which relates to the field of aquaculture and comprises a water quality detection module for detecting water quality data in an aquaculture unit, a meteorological detection module for detecting meteorological data of the position of the aquaculture unit, and an ultrasonic detection module for detecting quantity data of fishes in the aquaculture unit, the image detection module detects weight data and growth stages of fish in the breeding unit, and the analysis control module analyzes standard water quality and standard feed feeding amount in the breeding unit through an AI analysis model according to meteorological data, quantity data, weight data and growth stages; the analysis control module compares the difference between the water quality data at the current moment and the standard water quality, controls the oxygenation equipment to oxygenate and controls the water treatment equipment to change water; according to the standard feed feeding amount, a control signal is sent to control feeding system equipment to feed. According to the invention, powerful support is provided for modernized development of aquaculture, and more efficient, real-time and intelligent aquaculture management is realized.

The invention provides a double-mechanism cooperative type curved surface component water immersion ultrasonic intelligent detection method and system, and relates to the technical field of ultrasonic detection, and the method comprises the following steps: scanning a workpiece by using a double-mechanism water immersion ultrasonic detection system, collecting and processing an ultrasonic echo signal, and generating an ultrasonic C scanning image. And performing defect identification on the ultrasonic C scanning image by using the constructed multi-scale feature fused deep convolutional neural network model to obtain the type, position, size and confidence of the defect. Quantitative evaluation is carried out based on the defect recognition result, a report is generated, and meanwhile visual display and database management of the defect recognition result are achieved. Through double-mechanism cooperative scanning and deep learning intelligent identification, the precision and efficiency of curved surface component defect detection are improved, and automatic identification, quantitative evaluation and visual display of defects are realized.

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

The invention relates to the technical field of nondestructive testing, in particular to a multi-modal fusion metal microcrack ultrasonic detection system and method, and the system comprises an ultrasonic signal acquisition module, a signal preprocessing module, a feature alignment network module, an image segmentation module, a time-frequency analysis module and a segmentation result fusion module. According to the system, three modes of electromagnetic ultrasound, laser ultrasound and phased array ultrasound are utilized to detect a metal sample, and real-time nondestructive signal data acquisition and pretreatment are realized. The feature alignment network aligns and fuses the three modal signal features, and the U-Net segmentation neural network performs submillimeter crack segmentation on the fused features. And the time-frequency analysis module extracts signal frequency and amplitude information, and evaluates the breaking strength in combination with the crack label. Finally, a segmentation result fusion module integrates crack labels and time sequence features, and whether cracks exist or not is classified through a weighted convolutional neural network. According to the system, the detection performance, the practicability and the adaptability are remarkably improved, and a new scheme is provided for precise detection of metal microcracks.

The invention relates to the technical field of pipeline detection, in particular to a guiding type pipeline welding seam ultrasonic detection device and a detection method thereof. Comprising a mounting base, and a pipeline limiting mechanism movably penetrates through one side wall of the mounting base; two groups of first supporting plates are symmetrically arranged at the edges of the two sides of the top of the mounting seat; and a pipeline guide mechanism and a welding seam detection mechanism are arranged in the first supporting plate. The ultrasonic probe is controlled to abut against the outer wall of the edge of the pipeline welding seam, then the conducting ring is controlled to drive the ultrasonic probe to move in the direction of the welding seam, and therefore the welding seam is comprehensively detected; according to the ultrasonic detection device, the ultrasonic probe can perform regular circular motion in a wave shape to detect a welding seam, meanwhile, the distance between the two sets of limiting rings can be adjusted according to needs so as to adjust the swing amplitude, the problem that the position of the probe deviates in the detection process is avoided, and therefore the using effect of the ultrasonic detection device is improved.

The invention relates to a settlement data analysis and early warning method based on a cloud platform, and the method comprises the steps: collecting settlement data of a basic part of a main transformer in real time through a high-precision electronic settlement observation device array, obtaining deformation stress data through data preprocessing and coordinate calculation, and uploading the deformation stress data to the cloud platform; transmitting an ultrasonic pulse signal to the stress concentration position of the local area through ultrasonic detection equipment, receiving reflected wave data, and identifying the depth and range of the crack initiation position according to the reflected wave data; a sensor array is arranged on the basic surface of a crack initiation position, sound wave signals released in the crack propagation process are captured, and a time sequence of the crack propagation direction and speed is determined; and detecting crack depth data of the repair priority region through ultrasonic waves, calculating an adjusted basic health state value, comparing the adjusted basic health state value with a preset safety threshold, and generating a corresponding safety early warning signal.

The invention relates to a lightweight component surface defect quantitative evaluation method, system and device, and the method comprises the steps: obtaining a multispectral surface image, laser three-dimensional data and ultrasonic detection data of a lightweight component, and carrying out the fusion registration based on a preset multi-sensor synchronization protocol, and obtaining a component joint data set; performing dynamic pruning analysis on the component joint data set to obtain geometric contour features, and performing internal feature extraction on the component joint data set to obtain internal abnormal response features; stress gradient distribution data and volume ratio parameters are carried out on the geometric contour features and the internal anomaly features; and obtaining an original working condition data set of the lightweight component, carrying out service working condition coupling analysis on the multi-dimensional prediction matrix, and carrying out multi-dimensional quality l parameter identification to obtain component comprehensive quality information. According to the method, the morphology mechanical coupling feature extraction precision of composite defects such as cracks and holes can be improved.

The invention relates to the technical field of cylindrical lithium ion battery production, and discloses a method for detecting insufficient welding of a cylindrical lithium ion battery, and the method comprises the following steps: S1, appearance inspection; s2, initial internal resistance benchmark test; s3, applying a short-time large-current pulse to the battery by using a pulse generator, simulating an actual load condition, and recording an instantaneous voltage drop and current response curve of the voltage of the battery end during the pulse period; s4, performing infrared thermal imaging identification and data processing; s5, learning the picture and establishing a model; s6, comparing the test picture and the data with the model; s7, dynamic internal resistance calculation; s8, cold solder joint query and sorting processing; and S9, retesting the electrical performance. According to the invention, the technologies of dynamic internal resistance measurement, nondestructive X-ray or ultrasonic detection, rate discharge test and the like are adopted, so that efficient, accurate and nondestructive detection of the cold solder joint of the cylindrical lithium ion battery is realized, and the production efficiency and the detection precision are remarkably improved.

The invention provides a joint denoising method and system based on multi-source noise suppression and sparse representation, and belongs to the technical field of noise suppression in ultrasonic detection of power equipment, and the method comprises the steps: synchronously collecting an ultrasonic echo signal, a vibration signal and an electromagnetic signal of a composite insulator; decomposing the ultrasonic echo signal into a plurality of modal components and undecomposed signal margins; extracting a dominant frequency component in the vibration signal, and identifying a resonance noise feature overlapped with the frequency band of the ultrasonic signal; detecting a pulse interference event in the electromagnetic signal, marking occurrence time and a frequency range, and generating a pulse event feature; performing frequency band suppression and time domain threshold processing on the modal component according to the resonance noise characteristics and the pulse event characteristics; performing sparse representation coding on the processed modal components, executing multiple rounds of iterative screening and orthogonalization processing, and screening an atom set matched with defect echoes; and reconstructing the de-noised ultrasonic signal by using the screened atom set, and combining the de-noised ultrasonic signal with the undecomposed signal margin to output a final result.

The invention belongs to the technical field of welding seam detection, and particularly relates to a laser welding seam defect detection machine which comprises support plates, adjusting assemblies arranged between every two adjacent support plates, driving assemblies installed on the support plates and balancing assemblies arranged on one sides of the support plates. Wherein three groups of support plates are arranged in a circumferential array, a front-end support is fixedly mounted on one side of the long edge of each group of support plates, an ultrasonic detection probe, a dot matrix laser and a camera are sequentially mounted on the three groups of front-end supports, an electric control module is mounted on the top surface of each support plate, and a power supply module, a data analysis processing module and a control system are arranged in each electric control module. The device can automatically move on a reducing pipeline, perform positioning detection on a welding seam, and perform circumferential uninterrupted detection in the detection process, so that the detection precision is higher, a mode of combining laser detection and ultrasonic detection is utilized, displacement deviation is automatically corrected after reducing, the error probability is greatly reduced, unnecessary workload is avoided, and the detection efficiency is improved. The detection efficiency is improved.

The invention relates to the technical field of wafer detection, in particular to a wafer quality detection method in an ultra-clean environment, which comprises the following steps: performing laser scanning detection on the surface of a wafer on a laser detection table, performing gas flushing on the wafer with an abnormal surface, and performing laser scanning detection again; when the laser detection table detects that the surface of the wafer has defects, performing thermal radiation heating on the surface of the wafer, and detecting the specific defects of the wafer through an infrared thermal imager; performing ultrasonic detection on the interior of the wafer, judging whether the wafer has an internal defect according to an ultrasonic reflection signal, generating an ultrasonic detection image of the wafer with the internal defect, and extracting defect data of the wafer with the internal defect by constructing a neural network; and generating a wafer quality detection report, and displaying a wafer detection result. According to the invention, multiple detection is carried out on the surface and the outside of the wafer, the detection efficiency is improved while the detection accuracy is improved, and the occupied resources during detection are saved.

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

The invention discloses an ultrasonic transducer characteristic parameter test system, which belongs to the technical field of ultrasonic detection and comprises an excitation signal generation module, an impedance matching adjustment module, a resonance characteristic extraction module, a signal acquisition and processing module, a temperature control loading module, a physical vibration isolation module and a frequency spectrum calibration module. The excitation signal generation module is combined with an off-chip frequency locking feedback loop through an on-chip oscillator to generate a multi-frequency-point sweep frequency pulse array, the multi-frequency-point sweep frequency pulse array is connected to the resonance characteristic extraction module after passing through the impedance matching adjustment module, the multi-frequency-point sweep frequency pulse array and the frequency spectrum calibration module construct a bidirectional feedback path and share harmonic node synchronous calibration logic, and coupling offset compensation is achieved; the frequency spectrum calibration module performs bidirectional real-time error correction on the excitation output frequency and the resonance input phase; and the transducer is arranged in the thermal convection temperature equalizing cabin of the temperature control loading module and is overlapped with the physical vibration isolation module. The method has the advantages that high-precision, low-interference and full-frequency-domain dynamic testing of characteristic parameters of the ultrasonic transducer is achieved, and testing stability is improved.

The invention discloses a ring piece ultrasonic detection noise reduction and identification method and system and a computer storage medium. The method comprises the steps that a matched ultrasonic phased array probe is selected according to the shape and size of the cross section of a workpiece, and technological parameters are set and comprise at least one of the probe frequency, the number of array elements, the water layer height and the focusing depth; according to the selected ultrasonic phased array probe and process parameters, ring piece water immersion ultrasonic phased array detection is carried out, full-coverage scanning is carried out in a weld joint area, and the scanning position of the probe is recorded in real time through an encoder; in the scanning process, an ultrasonic image scanned by the phased array probe is collected in real time, and the ultrasonic image is transmitted to a pre-trained adaptive noise reduction YOLOv5 target detection model for defect recognition; and the rotating angle position of the ring piece is recorded in real time through an encoder, and when the target detection model finds defects, the defect position is recorded, and alarm information is generated. The method is suitable for ring piece ultrasonic detection noise reduction and recognition, the defect recognition accuracy can be improved, and the manual labor intensity is reduced.

The invention relates to a high-precision ultrasonic detection device and a high-precision ultrasonic detection method. The device comprises a solid coupling ultrasonic scanning unit and an ultrasonic control detection processing unit, scanning signal detection processing is carried out on an obtained ultrasonic scanning signal group through the ultrasonic control detection processing unit, and object ultrasonic scanning information is generated after the scanning signal detection processing is carried out, the method at least comprises signal-to-noise ratio improvement processing and scanning signal analysis processing, when scanning signal detection processing is executed, signal-to-noise ratio improvement processing is carried out on each ultrasonic scanning signal, scanning signal analysis processing is executed on all the ultrasonic scanning signals subjected to signal-to-noise ratio improvement processing, and scanning signal analysis processing is executed on all the ultrasonic scanning signals subjected to signal-to-noise ratio improvement processing. After the scanning signal is analyzed and processed, object ultrasonic scanning information is generated. When the object to be detected is subjected to solid coupling ultrasonic detection, the signal-to-noise ratio of the received ultrasonic scanning signal can be improved, and the precision and reliability of ultrasonic detection are improved.

The invention belongs to the field of material performance testing of mechanical structure strength, and particularly relates to an ultrasonic fatigue experiment monitoring method based on digital twinning. According to the method, on the basis of an ultrasonic fatigue testing machine platform, an ultrasonic fatigue digital twin model of a tested object is established by collecting frequency, temperature, load and other test data, and real-time online monitoring of ultrasonic fatigue and life prediction are carried out in combination with a fatigue life prediction model. Compared with a traditional test method, the method has the advantages that ultrasonic detection data and a digital twinborn model technology are combined, the fatigue life of the material is predicted and evaluated, the test efficiency is improved, the long-life test cost is reduced, and the accuracy and reliability of test data are improved.

The invention discloses an intelligent deicing method and system based on ultrasonic detection, a medium and equipment, and relates to the technical field of deicing. The intelligent deicing method based on ultrasonic detection is used for the intelligent deicing system and comprises the following steps that the surface temperature of an airplane is detected based on an infrared sensor, and the surface temperature value of the airplane is obtained; comparing the airplane surface temperature value with a temperature threshold value; if the aircraft surface temperature value is greater than the temperature threshold value, continuing to detect the aircraft surface temperature based on the infrared sensor; if the aircraft surface temperature value is smaller than or equal to the temperature threshold value, an ultrasonic pulse echo icing sensor is started to detect the icing thickness of the aircraft surface, and icing signal information is obtained; and based on the icing signal information, whether the automatic deicing system of the airplane is started for deicing treatment is judged. Through the steps, the deicing efficiency of the aircraft during flight is improved.

The invention discloses a welding seam quality detection method and a welding seam detection system. The detection method comprises the following steps: acquiring an image of a detection area of the welding assembly by adopting a visual identification device; an ultrasonic detection device is adopted for scanning along the weld joint, and the ultrasonic detection area of the detection area is obtained; wherein the ultrasonic detection area is the welding area of the second welding piece and the concave bottom wall; comparing the ultrasonic detection area with an area threshold value; if the ultrasonic detection area is larger than or equal to the area threshold value, it is judged that the weld quality is qualified; if the ultrasonic detection area is smaller than the area threshold value, the welding seam quality is judged to be unqualified. The welding seam quality can be quantitatively evaluated, and the welding seam quality detection precision is improved.

The invention discloses a steel plate butt weld pseudo defect ultrasonic detection method, system and device and a medium, and relates to the technical field of weld detection, the method comprises the following steps: setting a plurality of detection areas on a steel plate butt weld based on acquired structure parameters and surface characteristic data of a to-be-detected steel plate; performing multi-frequency and multi-angle ultrasonic scanning according to a preset first detection parameter by taking each detection area as a basic unit to obtain corresponding echo signal data; analyzing and identifying potential pseudo-defect signals, and calculating corresponding pseudo-defect characteristic values according to the pseudo-defect signals; taking each detection area as a basic unit, performing pseudo defect feature analysis according to the second detection parameters, and determining reliability evaluation data of pseudo defects; based on the pseudo-defect reliability evaluation data and the pseudo-defect characteristic value corresponding to each detection area, the pseudo-defect condition of the steel plate butt weld is analyzed, and a final weld quality comprehensive evaluation result is generated. According to the invention, the detection accuracy of steel plate butt weld detection is improved.