1083 results about "Damage detection" patented technology

The invention discloses a metal structural part surface damage identification method based on machine vision, and belongs to the field of machine vision, and the method comprises the steps: obtaining reference image data with known damage features, carrying out the preprocessing, analyzing the change trend of a system detection state, and judging whether there is a deviation correction demand or not. And if the deviation exists, carrying out geometric correction processing on the lens distortion error to obtain a corrected reference image. Further separating the real change of the damage from the system deviation, and combining low-resolution and high-resolution detection to obtain the distribution data of the suspected damage area and the specific characteristic parameter data of the damage. According to the method, quantitative data of damage levels are obtained through automatic classification, detection differences among multiple devices are calibrated, visual presentation information of damage positions and levels is generated, and finally camera parameters and algorithm thresholds for subsequent detection are optimized and adjusted, so that high-precision damage detection and evaluation are realized.

The invention relates to the technical field of civil engineering monitoring, and discloses a structure health monitoring system based on civil engineering. A high-density sensing network of the system collects strain field distribution, vibration spectrum and environmental corrosion parameters of a structure through a distributed multi-mode sensor array; performing clock drift compensation and space coordinate normalization on the asynchronous sampling data by a space-time alignment engine to generate an original feature tensor of a unified space-time reference; the semantic modeling unit is combined with a design drawing and a material parameter library, the original feature tensor is mapped to a component semantic space, and hierarchical structure features with topological marks are output; the adaptive fusion core executes dynamic weight distribution, eliminates sensor conflict data and generates an anti-interference fusion diagnosis index; the edge computing node operates a lightweight damage detection model according to the fusion diagnosis index, and outputs a local component health state level; and the cloud collaborative analyzer aggregates multiple edge node results and predicts the overall residual life of the structure in combination with historical degradation data.

The invention discloses a curtain wall structural adhesive damage detection method and device based on modal difference and digital twinning, and belongs to the technical field of digital twinning structural adhesive damage detection and evaluation. The problem that in the prior art, a traditional glass curtain wall structural adhesive damage detection and evaluation method based on the first-order inherent frequency is not sensitive to local boundary adhesive failure, and consequently the specific damage position cannot be positioned is solved. The method comprises the following steps: acquiring vibration data of a group of hidden framing glass curtain walls to be detected and a group of hidden framing glass curtain walls in a lossless state to obtain normal acceleration time history data; primarily screening second-order and third-order inherent frequencies, and judging whether the structural adhesive is damaged or not according to a difference constraint condition; further positioning the damage by using the boundary relative curvature modal difference, and judging whether the current measuring point is a damage point or not; and constructing a digital twinborn model and damage early warning, and outputting a parameter report and a visual damage evaluation result. The method effectively improves the boundary damage positioning precision, and can be applied to glass curtain wall structural adhesive damage detection.

The invention provides a concrete bridge damage detection and residual life prediction method based on machine vision. The method comprises the following steps: acquiring a concrete bridge surface image and a corresponding structure coordinate thereof; the structure coordinates are explicitly embedded into the concrete bridge surface image, and a coordinate surface image is obtained; the coordinate surface image is input into an image recognition model, image types and corresponding structure coordinates are recognized, the image types comprise an extrinsic damage type image and a mark type image, and the mark type image is a mark image generated by manual testing; determining each first life influence parameter according to the external display damage image and the corresponding structure coordinate; determining each second life influence parameter according to each mark type image and the corresponding structure coordinate; and estimating the remaining life of the concrete bridge according to each first life influence parameter and each second life influence parameter. By implementing the method, the comprehensiveness of concrete bridge damage detection and the accuracy of residual life prediction can be improved.

The invention provides an alloy bearing unsteady-state damage detection and evaluation method based on digital twinning. The method comprises the following steps: constructing an alloy bearing multi-physical field digital twinning model; the method comprises the following steps: acquiring operation state data of a bearing under an unstable working condition based on a sensor network, preprocessing the acquired data to obtain multi-source features, and fusing the multi-source features to generate a comprehensive health index; inputting the preprocessed data into a digital twin model for forward simulation, generating a prediction observation vector, comparing the prediction observation vector with a sensing measurement value to generate a residual error, and performing damage state assimilation based on the residual error; and based on the assimilated state, predicting a damage evolution trajectory under an unsteady state working condition, and evaluating probability distribution of residual life. According to the method, the reduced-order proxy model for dynamic mode switching is constructed, the mode basis is automatically expanded and the sub-models are switched when the load suddenly changes or the rotating speed jumps, and the problem of feature drift of a traditional fixed basis function model under variable working conditions is effectively solved.

The invention belongs to the technical field of wall painting restoration, and discloses a damaged image automatic digital restoration method comprising the following steps: a data collection module collects geological data and image data; the three-dimensional structure scanning unit obtains a three-dimensional structure of the surface of the mural and space coordinates of a damaged area; the infrared imaging unit extracts a bottom layer pattern; the intelligent damage detection module locates a damage area; the multi-modal generative repair module complements a damaged physical form through a structural layer repair unit, and the repair scheme planning module plans an entity repair route and a material scheme; the damaged area color partitioning and boundary extraction module is used for performing partitioning and boundary extraction on the color for repairing the damaged area; and the entity repair execution module completes physical repair. According to the invention, through linkage of entity repair and digital achievements, the entity repair execution module performs physical repair based on a digital repair model, so that excessive dependence of traditional repair on artificial experience is broken through, and historical authenticity and artistic integrity of the mural are guaranteed by a scientific method.

The invention relates to the technical field of image data processing, in particular to a chemical fertilizer packaging bag damage detection method and system based on machine vision, and the method comprises the steps: obtaining a packaging bag image and environment dust concentration, calculating the transmissivity through combining with the image local texture complexity, and carrying out the dynamic dust removal enhancement through employing an atmospheric scattering model; performing complementary detection on the enhanced image by adopting a frequency domain analysis first detection model aiming at structural damage and a texture analysis second detection model aiming at unstructured defects in parallel; and finally, according to the local periodic texture intensity of the image, carrying out adaptive weighted fusion on a double-model result to obtain a final defect score, and determining damage. According to the method, the interference of dust and complex textures is effectively overcome through a whole-process self-adaptive strategy from preprocessing, detection to fusion, and accurate and robust detection of various damages is realized.

The invention discloses a road disease detection method based on an unmanned aerial vehicle, electronic equipment and a program product. The method is realized based on a trained road disease detection model, a C3k2-MDDSC module is introduced into a backbone network of the model, the feature multiplexing capability is enhanced through gradient shunting and multi-scale fusion, the gradient disappearance problem is relieved, and the robustness of the model is improved by means of jump connection and packet convolution. An ACFP module is introduced into the tail end of the backbone network, dynamic fusion of local and global features is realized by using multi-scale cavity convolution and a channel-space attention mechanism, and the complex scene modeling capability is improved. And the neck network is integrated with an SGF module, so that the spatial perception of the model to a tiny target can be improved. Besides, the ES-FPN proposed based on the SGF module not only can enhance the utilization of shallow spatial information, but also can optimize the complementarity of cross-level features. During training, regression loss, namely fast high-quality intersection-to-union ratio loss, is proposed, and angle punishment is introduced to improve the alignment precision of the rotating frame and the convergence speed of the model.

The invention belongs to the technical field of fan damage detection, and particularly discloses an unmanned aerial vehicle-based offshore fan blade damage detection system, which comprises a detection partition determination module, an unmanned aerial vehicle acquisition control module, a damage image acquisition module, a fan damage analysis module and a fan damage feedback terminal. According to the method, the damage point and the blade area are positioned through preliminary cruising of the unmanned aerial vehicle, and the fan array is divided, so that the dilemma that a traditional detection mode is easily interfered in an offshore high-wind-speed environment is changed, and the complicated offshore environment can be flexibly coped with; meanwhile, the shooting distance and the shooting time point of the unmanned aerial vehicle are adjusted by combining the space coordinates of the damage point, the real-time blade rotating speed and the real-time offshore wind condition to generate a shooting parameter instruction, so that the limitation and the monitoring dead angle limitation of traditional fixed-view-angle monitoring are broken through; and finally, crack analysis of different depth layers is carried out on the optical image and the ray image, so that the damage degree can be evaluated more comprehensively and accurately.

The invention discloses an ultrasonic guided wave signal noise reduction method based on HHO-SVMD and singular spectrum analysis, and the method comprises the steps: obtaining a to-be-processed ultrasonic guided wave signal, optimizing a penalty factor of variation mode decomposition (SVMD) through employing an improved Harris eagle algorithm, initializing a population through Circle chaotic mapping, introducing chaotic disturbance and weight, and carrying out the noise reduction of the to-be-processed ultrasonic guided wave signal. Determining an optimal alpha value and decomposing the signal into an optimal modal component; calculating a kurtosis value of each modal component, and screening effective components containing damage information according to a threshold value; singular spectrum analysis denoising is carried out on the effective components, and a self-adaptive window mechanism is introduced to dynamically adjust the window length and the truncation strength; reconstructing the de-noised effective component to obtain a de-noised signal; according to the method, the parameter optimization precision and efficiency are improved, damage characteristics and noise are effectively separated, different dominant frequency signals are adapted, the damage characteristics can still be reserved in a low-signal-to-noise-ratio environment, the noise reduction effect of ultrasonic guided wave signals and the damage detection reliability are improved, and the method is suitable for nondestructive detection of components such as ultra-long small-diameter heat absorption pipes.

The invention discloses a ship intelligent operation robot based on artificial intelligence, and relates to the technical field of ship equipment. The device comprises a mobile platform, and an operation execution mechanism is arranged at the top of the mobile platform; the operation executing mechanism comprises a driving arm mounted at the top of the movable platform, and a spray gun and a flaw detector which are mounted at the output end of the driving arm. The mobile platform is of a crawler-type structure and is matched with sensing assemblies such as a laser radar and a camera, so that the robot can navigate autonomously and adapt to the complex ship environment, the potential safety hazard that workers enter a dangerous area for operation is avoided, a driving arm in the operation executing mechanism can accurately control the positions of a spray gun and a flaw detector, and the working efficiency is improved. Automatic paint make-up and damage detection are achieved, errors of manual operation are reduced, the rust removal assembly forms a set of complete automatic rust removal system through reciprocating knocking of an impact rod and rotary sweeping of a cleaning roller, and the risk that operators make contact with poisonous and harmful gas and dust is effectively avoided.

The invention relates to the technical field of wind power equipment detection, in particular to a wind turbine generator blade fracture damage detection method applying an inspection robot, which comprises the following steps of: S1, constructing a blade three-dimensional point cloud model and positioning the position of the robot in real time through a laser radar and a visual SLAM (Simultaneous Localization and Mapping) module carried on a robot body; s2, generating a spiral detection path covering the surface of the blade based on a path planning algorithm optimized by the genetic algorithm; s3, synchronously collecting blade surface data by adopting a multi-mode sensor array, wherein the blade surface data comprise pulse thermal imaging data, ultrasonic guided wave signals and high-resolution visible light images; s4, performing feature fusion on the acquired data through a deep convolutional neural network; through multi-modal sensor cooperative detection, deep learning feature fusion and digital twinborn evaluation technologies, full-process automatic detection from damage identification to life prediction is realized, the detection precision and efficiency are significantly improved, and reliable guarantee is provided for safe operation of the wind turbine generator.

The invention discloses a tunnel structure full life cycle damage detection and safety evaluation method, and belongs to the technical field of damage detection and safety evaluation. The method comprises the following steps: completing installation configuration of a mobile detection platform along the axial direction of a tunnel; generating a moving track and a detection point location distribution scheme of the mobile detection platform; controlling the mobile detection platform to sequentially move to each detection section position according to the detection path, and synchronously starting each detection module to perform continuous data acquisition in the moving process; generating a tunnel three-dimensional mathematical model, a lining structure thickness distribution diagram, a heat radiation anomaly distribution diagram and a crack damage distribution diagram; equally dividing the tunnel three-dimensional model into N detection segments according to a preset interval, and counting the lining thickness mean value, the number of heat radiation abnormal points, the crack length density and the damage level of each segment as safety evaluation indexes; statistical analysis and trend judgment are conducted on the safety indexes of all the segments, abnormal segments exceeding a safety threshold value are recognized, and the risk level of the abnormal segments is determined.

The invention belongs to the technical field of intelligent AI for rainfall analysis and landslide early warning, and discloses an intelligent AI-based rainfall data analysis highway landslide early warning system, which comprises a slope hydrologic monitoring module, a slope damage detection module, an anti-slide pile dynamic monitoring module, a landslide risk analysis module and a risk early warning output module, a slope landslide risk prediction model is constructed by collecting rainfall, water seepage amount, slope structure damage state parameters, pressure difference value of two sides of an anti-slide pile and pile-soil gap variation key data in real time; and the pressure difference value of the two sides of the anti-slide pile, the structural damage state parameters and the rainfall capacity are subjected to cross dynamic association through time sequence characteristics, the water seepage amount and the pile-soil gap variation are fused, and the landslide risk level is output through the dynamic risk amplification factor and the soil saturation critical index. The real-time performance and accuracy of landslide early warning can be remarkably improved, and the method is suitable for highway slope safety monitoring under complex geological conditions.

The invention provides a bridge structure damage detection method based on image processing and CNN-LSTM, relates to the field of graphic image processing, and solves the problems that bridge vibration monitoring in the prior art is time-consuming, labor-consuming, high in cost and limited in precision, and adopts the technical scheme that the method comprises the steps of obtaining a recorded vibration video of a to-be-detected bridge structure; extracting the vibration video frame by frame to obtain bridge image data; detecting angular points by using a Harris algorithm, screening image data, and calculating vertical displacement of each feature point one by one; using a Lucas-Kanade optical flow algorithm to calculate the motion vectors of the feature angular points of the adjacent frame images; calculating the actual vibration displacement of the bridge; and carrying out standardization processing on the actual vibration displacement to obtain a two-dimensional matrix, inputting the two-dimensional matrix into the trained CNN-LSTM model, and outputting a final identification result of each type of damage. According to the scheme of the invention, high-precision bridge vibration displacement can be calculated, and efficient, accurate and high-precision bridge damage detection can be realized through the CNN-LSTM damage identification model.

The invention discloses a bridge disease recognition and maintenance method and system based on a multi-modal large model. The method comprises the steps of S1, building a general scene bridge disease training annotation data set based on historical bridge disease images and artificial disease annotation; s2, constructing a damage detection model based on YOLOv5, and training the damage detection model by using a general scene bridge disease training annotation data set to obtain a detection result of the damage detection model; s3, pre-training the general scene bridge disease training annotation data set to obtain pre-processed data; s4, inputting the detection result and the preprocessed data into a segmentation model for training to obtain a segmentation mask graph, and calculating the number and area of bridge diseases based on the segmentation mask graph; and S5, based on the number and the area of the bridge diseases, using an Llama3 model to generate a maintenance scheme, and completing the bridge disease identification and maintenance method and system based on the multi-modal large model.

The invention provides an aero-engine blade damage detection method based on an improved ultrasonic sparse reconstruction algorithm, and relates to the technical field of aero-engine damage detection positioning, and the method comprises the steps: S1, setting a sensor array and a detection region of a to-be-detected blade, and obtaining an actual measurement damage reflection signal dictionary through a sensor; s2, wave number distribution of a detection area is determined according to a variable thickness structure Lamb wave propagation model, signal reconstruction is carried out, and a theoretical damage reflection signal dictionary is obtained; s3, an aero-engine blade damage detection model is constructed, an improved sparse reconstruction algorithm is adopted to solve and optimize, and damage judgment is carried out; and S4, according to the pixel value distribution condition of the aero-engine blade detection area, generating an aero-engine blade damage detection image, and positioning a damage position. According to the method, the reflected wave amplitude information can be obtained from the array sensor signals, and the reflected wave amplitude information is compared with the reconstructed theoretical reflected signals, so that the damage condition of the aero-engine blade is identified and positioned.

The invention discloses an improved automobile paint surface damage detection method based on RT-DETR. The method comprises the following steps: firstly, constructing a high-quality paint surface damage data set, performing data enhancement, and introducing a negative sample and an interference sample to improve the generalization ability and the interference resistance of a model; secondly, in a Backbone network of RT-DETR-ResNet18, an improved dynamic convolution hybrid module (DCMB) is introduced, and on the basis of an original structure, the sensing ability of a strong model to complex textures, edges and heterogeneous regions is optimized; and finally, adding a full-scale frequency attention structure (CSPO) into the Neck part to improve the identification capability of the model on a fine-grained damage region. Compared with the prior art, the method of the invention realizes high-precision detection of eight types of damages such as scratches, recesses and corrosion on the automobile paint surface through a deep learning technology, has the advantages of high detection speed, high accuracy and the like, can be widely applied to the fields of automobile maintenance, second-hand automobile evaluation and the like, and has a wide application prospect. And the automation level and the detection efficiency of automobile paint surface damage detection are obviously improved.

To provide a method for predicting a strain distribution map and determining damage based on deep learning.SOLUTION: The present invention comprises: a step S1 of establishing an image dataset of finite element analysis results for strain distribution map prediction; a step S2 of building a deep learning model for strain distribution map prediction based on a DeepLabv3+ network and performing learning and validation; a step S3 of establishing a dataset for damage determination of a structural analysis model and performing preliminary processing and data enhancement operations; a step S4 of building a binary classification deep learning model for damage determination based on a convolutional neural network and performing learning and validation for transition learning; and a step S5 of performing an interpretability analysis on the learned binary classification structural analysis model and outputting a region in an image that more contributes to classification.SELECTED DRAWING: Figure 1

The invention discloses a sidewalk damage detection method and device based on deep learning, electronic equipment and a program product. The method is realized through a trained detection model, a DS-HAF module is introduced into a model neck network, and multi-scale feature fusion, channel-space joint attention enhancement and bidirectional residual error guided deep and shallow feature dynamic enhancement fusion are performed, so that the collaborative effect of detail and semantic expression is optimized, and complex background interference is effectively inhibited. An MAGRDet detection head is introduced into the detection network, multi-branch feature extraction and alignment fusion are realized through an MAGR module, and the detection capability of small targets, low-contrast targets and shielded crack targets is improved through a channel-space cascade attention modulation fusion result. And meanwhile, an MDPAR module is introduced into the backbone network, so that efficient and robust cross-scale feature extraction is realized, and the perception capability and the anti-interference performance of the model on multi-scale sidewalk cracks and damages are improved while low calculation overhead is maintained.

The invention relates to the technical field of concrete pole damage monitoring, and discloses a concrete pole damage monitoring method based on modal recognition, comprising the following steps: S1, multi-modal data acquisition; s2, edge side real-time processing and micro-damage identification; s3, performing model diagnosis based on cloud depth; s4, performing digital twin driven damage evolution prediction; and S5, performing dynamic risk assessment and decision support. According to the method, through synchronous acquisition and fusion of multi-source data, the limitation of a single sensing mode is overcome, multi-dimensional and cross-modal collaborative recognition of the microcrack germination stage is realized, and the sensitivity and reliability of early damage detection are remarkably improved; a lightweight damage identification model is deployed on the edge side, so that the instant early warning requirement of micro-damage germination is met; damage positioning and degree evaluation are carried out through the cloud based on a multi-modal fusion deep learning model, an efficient collaborative architecture of edge real-time early warning and cloud accurate diagnosis is formed, and the system response speed and decision accuracy are greatly improved.

The invention discloses a structural damage detection method based on a bridge digital twinning group, and the method comprises the steps: building a general damage detection model through a multi-dimensional digital twinning technology: building a reference bridge model through Abaqus, and generating a digital twinning group containing a single / multiple damage scene; training the ethnic group by improving a WGAN-GP architecture, outputting a marked damage image by a generator, introducing a multi-scale attention module into a discriminator, and constructing an ethnic group characteristic spectrum (EFM) matrix through characteristic distillation; then collecting feature parameters to train a CNN model, and fusing features through SENet channel attention; through Gaussian white noise verification and n-fold cross optimization, the F1 score of the model is greater than or equal to 0.89 in a 40% noise environment, and the average accuracy rate reaches 95% or above. According to the technology, the limitation of single structure detection is broken through, the model generalization ability is improved by 300% and the detection efficiency is improved by 50% through a dynamic twin-driven convolutional network self-evolution mechanism, and complex scenes with + / -20% size change and 0-100% damage degree can be accurately identified; and an efficient anti-interference solution is provided for health monitoring of structures such as bridges, high-rise buildings and the like.

The invention discloses a multi-modal data fusion-based steel wire rope detection and real-time transmission method and system, and the method comprises the steps: starting a detection system, collecting a steel wire rope damage detection signal, collecting equipment position and operation state data, constructing an original data set, carrying out the preprocessing, building an incidence matrix, dividing a processing unit, and then extracting features, and forming an initial feature data set. And extracting damage features through a multi-branch network, strengthening the weight of a key region, fusing position and equipment state features through a multi-modal fusion module, generating fusion vectors, classifying and identifying, and outputting a structured detection result. And the acquisition equipment end performs grading processing on the data, distributes transmission channels according to priorities, dynamically adjusts parameters, adds integrity and time sequence identifiers, and the upper computer verifies the data integrity and triggers abnormal supplementary transmission. And the upper computer decodes the data, reconstructs the waveform, gives an alarm in real time in combination with preset parameters, and generates details of damage key information. Reliable technical support is provided for safe operation and maintenance of the steel wire rope in industrial scenes.

The invention discloses a road culvert inner wall damage image detection and traffic capacity evaluation method and system, and relates to the technical field of road engineering detection and computer vision, and the method comprises the steps: collecting multi-source data through an annular camera, a laser section scanner and a mileage encoder carried by a detection robot, and carrying out the panoramic expansion and time-space registration; using a multi-scale attention panoramic segmentation network to identify damages such as lining cracks, joint slab staggering, damage and chipping, steel bar exposure and water leakage; calculating the clearance size and the deposition thickness of the culvert by adopting an ellipse fitting method; according to the method, the culvert detection speed reaches 50 m / h, and the damage detection accuracy reaches 89% or above.

The application discloses a curtain wall structure adhesive damage detection method and device based on modal difference and digital twinning, and belongs to the technical field of digital twinning structure adhesive damage detection and evaluation. The application solves the problem that the traditional glass curtain wall structure adhesive damage detection and evaluation method based on the first-order natural frequency cannot locate the specific position of damage due to the insensitivity to local boundary delamination. The application collects vibration data of the test group of hidden frame glass curtain walls and the non-damage state group of hidden frame glass curtain walls to obtain normal acceleration time history data. The second-order and third-order natural frequencies are preliminarily screened, and whether the structure adhesive is damaged is judged through the difference constraint condition. The boundary relative curvature modal difference is used to further locate the damage, and whether the current test point is a damage point is judged. A digital twinning model and damage early warning are constructed, and a parameter report and a visual damage evaluation result are output. The application effectively improves the boundary damage positioning accuracy and can be applied to glass curtain wall structure adhesive damage detection.

The invention discloses a fan blade monitoring system and method based on multi-modal data fusion analysis, and belongs to the technical field of wind power generation. The method comprises the steps that multi-modal characteristic data of a fan blade in operation are acquired; inputting the multi-modal feature data into a pre-constructed multi-modal feature fusion anomaly detection model, a damage factor quantitative calculation model and a health trend prediction model, and respectively performing comprehensive anomaly detection, comprehensive damage detection and health degree prediction on the operation state of the fan blade; based on calculation results of comprehensive anomaly detection, comprehensive damage detection and health degree prediction, comparing the calculation results with a corresponding normal state threshold range; and triggering early warning when any calculation result deviates from the normal state threshold range. According to the method, the multi-modal feature information in the operation of the fan blade is obtained, and the deep learning and machine learning algorithms are combined, so that the early fault under the complex operation condition is effectively identified, and the intelligent operation and maintenance requirements of a modern wind power plant are met.

The invention relates to the technical field of vehicle damage detection and identification based on computer vision, and discloses a vehicle damage detection and identification method based on computer vision. The method comprises the steps of image acquisition, polarization image acquisition, Stokes parameter calculation, polarization feature extraction, anomaly evaluation, connected region analysis and the like. A polarization statistical model is established by setting an image coordinate system, collecting a multi-angle polarization diagram and calculating the polarization degree and the polarization angle of each pixel and taking an artificially selected nondestructive area as a reference, so that anomaly measurement and adaptive threshold segmentation are performed on the pixels, and a structured vehicle damage detection result is output in combination with corrosion, expansion and connected domain analysis. Physical interpretability and detection precision are enhanced by using polarization characteristics, recesses or scratches can be accurately identified even in a vehicle body area with a smooth surface or complex reflection, a standardized report containing position information and abnormality is provided, and automation and accuracy of vehicle damage assessment are effectively improved.

The invention discloses an automobile luminous logo appearance defect detection device, and relates to the technical field of automobile logo appearance detection tools. Through the cooperation of the damage detection mechanism and the brightness detection mechanism, the appearance, damage and brightness defects of the logo can be conveniently and comprehensively detected; the analysis module processes the gray block, draws the contour and marks the abnormity, accurately positions the areas where defects possibly exist on the surface of the logo, transmits the position information of the areas to the execution module, and when the execution module controls laser detection, the abnormal areas are scanned preferentially, so that omission or misjudgment caused by blind laser scanning is avoided, and the detection efficiency is improved. Laser detection is more targeted, and the accuracy of defect detection is improved; according to the technical scheme, the cleaning operation is performed firstly, and then the laser scanning is performed, so that the bulge caused by the attachment or the bulge formed by the material of the logo can be effectively distinguished, the accuracy of bulge defect judgment is ensured, and unnecessary loss caused by misjudgment is reduced.

The invention provides an intelligent wind power mixed tower damage detection method and system based on multi-modal data fusion. The method comprises the following steps: acquiring a vibration signal, an image signal and an acoustic emission signal of a wind power mixed tower through a multi-modal data acquisition layer; performing timestamp synchronization and space coordinate mapping alignment on the vibration signal, the image signal and the acoustic emission signal by adopting a bidirectional LSTM network to obtain multi-modal heterogeneous alignment data; based on multi-scale feature extraction, identifying vibration features of a steel tower section and image features and acoustic emission features of a concrete section in the multi-modal heterogeneous alignment data; dynamically distributing feature weights for the vibration features, the image features and the acoustic emission features by using a cross-modal attention network, and determining the damage probability and the expansion speed of the wind power hybrid tower; based on the damage probability and the expansion speed, graded early warning is executed, and the wind power mixed tower damage detection efficiency is effectively improved.

The invention relates to the technical field of machine tool cutter damage detection, in particular to an IO-LINK bus panel with an intelligent connection function for a machine tool. The bus panel comprises a memory and a processor, and the processor executes computer programs stored in the memory so as to realize the following steps: acquiring current data of a spindle motor and vibration spectrum data of a cutter in the working process of a machine tool; determining the confidence coefficient according to the rising trend of the current data of each period and the overall current data of the adjacent periods; screening characteristic moments based on sudden drop characteristics of current data of a spindle motor in the working process of the machine tool; and according to the rising characteristic of the current in the neighborhood after the characteristic moment, the stability condition of the current in the neighborhood before the characteristic moment and the difference of the vibration spectrum data in the neighborhood before and after the characteristic moment, the tipping moment is screened, and the confidence coefficient and the occurrence frequency of the tipping moment are synthesized to judge whether the cutter is replaced or not. The timeliness of monitoring the damage condition of the machine tool cutter is ensured.