1594 results about "Damage detection" patented technology

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

The invention discloses an asphalt pavement fatigue damage model calibration method based on AI and digital twinning, and relates to the technical field of damage detection. Compared with the prior art, the problems that traditional asphalt pavement design depends on empirical formulas and static parameters, material aging, environmental coupling and load uncertainty are difficult to dynamically reflect, and cross-scale correlation between microscopic interface behaviors and macroscopic structure performance is lacked are solved; the method comprises the following steps: collecting and preprocessing multi-source heterogeneous data in real time through a multi-modal data fusion and dynamic sensing system, simulating and accurately quantifying asphalt-aggregate interface binding energy and adhesion work in combination with molecular dynamics, and constructing a cross-scale damage evolution model; a physical information neural network is used for embedding an improved Paris formula, actually measured data and a physical rule are deeply fused, and the locality hypothesis of a traditional model is effectively corrected.

The invention relates to the technical field of urban infrastructure intelligent detection, and discloses an urban drainage pipe network damage detection system based on intelligent analysis. A multi-source data acquisition module acquires pipe network static structure parameters and real-time operation monitoring data through a distributed sensor network; generating a topological characteristic value, a dynamic operation state characteristic value and a dynamic detection threshold set of each damage type; the data preprocessing module filters noise of the monitoring data, eliminates abnormal values and extracts time domain and frequency domain feature vectors; the feature fusion module fuses and generates a multi-dimensional fusion feature matrix based on the topology and operation feature values; the intelligent analysis engine calculates and outputs a damage type diagnosis result through mode matching; the spatial positioning module generates three-dimensional coordinate positioning data of the damaged area in combination with the topological characteristic value; and the dynamic learning module optimizes the detection rule according to the maintenance data. The system realizes intelligent damage detection and accurate positioning, improves the detection accuracy and efficiency, adapts to a complex environment, and is high in intelligent level.

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 provides a distribution network power transmission line insulator damage detection method based on a YOLO algorithm, and the method comprises the steps: collecting a plurality of insulator defect images, and carrying out the data enhancement of the images; performing image detail enhancement on an insulator defect area in the image by using a super-resolution reconstruction algorithm; constructing a composite loss function to guide an image detail enhancement process; constructing an insulator defect detection model based on a YOLOv8 framework; constructing a multi-task joint loss function as an insulator defect detection model training target; and a real-time feedback mechanism is introduced, a weighted combination loss function is constructed, gradient updating optimization is executed, and the detection capability of the insulator defect detection model is improved. The method has high detection precision and robustness, can effectively improve the efficiency and accuracy of fault detection, significantly reduces the burden and cost of manual inspection, improves the safety and reliability of a power system, and provides powerful support for the operation and maintenance of power equipment.

The invention provides a building damage detection method and device for coupling multiple features of an SAR image and an optical image. The method comprises the following steps: acquiring an SAR image, an optical image and an unmanned aerial vehicle image corresponding to a research area in a pre-disaster scene and a post-disaster scene respectively; constructing a research area change detection image set based on the SAR image and the optical image, and constructing a vector data set corresponding to the research area based on the optical image and the unmanned aerial vehicle image; according to the vector data set, extracting multi-scale features corresponding to the building vector units from the change detection image set to construct a multi-scale feature library; and training the damaged building classification network by using the multi-scale feature library, wherein the trained damaged building classification network is used for carrying out damage detection on the research area. According to the method, the problems of single data source, relatively high feature extraction limitation, weak model generalization ability, insufficient real-time performance and robustness and the like in the prior art can be effectively improved.

The invention relates to an intelligent building damage detection method based on machine vision. The method comprises the following steps: multi-modal data acquisition; preprocessing and feature enhancement: processing the visual data and the acoustic data; damage detection: designing a material constitutive driven GAN architecture, calculating and generating stress distribution of a crack by a generator through a differentiable finite element analysis layer, performing L2 loss constraint on the stress distribution and an ABAQUS simulation result, evaluating image authenticity and physical consistency by a discriminator by adopting a double-branch structure, and outputting damage parameters in cooperation with a lightweight YOLOv8-Nano detection head; acoustics-vision depth fusion detection: aligning a potential space through a double-flow cross-modal encoder in combination with cross-modal contrast learning, and preferentially processing hidden damage by using a gating fusion unit for dynamically calculating a fusion weight through material acoustic impedance; when the visual detection confidence coefficient is smaller than a set threshold value, triggering frequency domain mutation detection and spatial positioning of the acoustic mode; and edge-cloud cooperative processing.

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 relates to the field of intelligent detection, and discloses a breakwater structure rapid scanning diagnosis method based on an unmanned aerial vehicle, and the method comprises the following steps: 1, collecting the multi-modal data of an overwater structure and an underwater structure of a breakwater through a sensor carried by the unmanned aerial vehicle; step 2, carrying out space-time alignment processing on the multi-modal data to generate fusion point cloud data; 3, constructing a multi-dimensional tensor representing damage features based on the fused point cloud data; step 4, uploading the damage classification result to a cloud; 5, dynamically adjusting the scanning path planning of the unmanned aerial vehicle according to the damage classification result and the real-time environment parameters; and 6, outputting a three-dimensional visualization result containing the damage position, the damage type and the emergency scheme. Through cross-medium cooperative acquisition of the millimeter wave radar, the sonar and the multispectral sensor, the coverage range and the information dimension of damage detection are expanded, and reliable data are provided for breakwater structure monitoring in a complex marine environment.

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 concrete-filled steel tube arch bridge detection system and method based on multi-modal fusion and quantum optimization are used for bridge structure health monitoring. The system comprises a multi-modal data acquisition module, a quantum optimization data fusion module, a defect self-growth simulation damage prediction module, a low-power-consumption intelligent sensor network and a quantum safety remote monitoring module. The multi-modal data acquisition module is fused with an ultrasonic radar, a millimeter wave radar, a hyperspectral image, a LiDAR and a wireless sensor, and bridge surface and internal damage detection is realized; the data weight is dynamically adjusted by adopting a quantum optimization algorithm, and the fusion precision is improved; in combination with a graph neural network and deformable grid calculation, crack propagation and fatigue damage development are predicted, the power consumption of the sensor is reduced through an energy collection technology, and the safety of remote monitoring data is ensured by adopting a quantum safety remote monitoring module. Compared with the prior art, the method improves the detection precision, prediction capability and data safety, and is suitable for health monitoring of various bridges and infrastructures.

The utility model discloses a system for large-range high-efficiency detection of a building facade. The system comprises a wall-climbing robot and a remote controller, the wall-climbing robot comprises a robot body, a climbing module, a propeller propelling module, a detection module and a control module, the control module is in communication connection with the remote controller; and the control module is suitable for controlling the climbing module to drive the wall-climbing robot to climb or descend along the rope, controlling the propeller propelling module to propel the wall-climbing robot to be tightly attached to the building facade and controlling the detection module to carry out large-range damage detection on the building facade in the climbing or descending process of the wall-climbing robot under an instruction of the remote controller. According to the utility model, the wall-climbing robot is controlled to climb up and down stably on the vertical facade through the remote controller, and the large-range detection of the crack damage of the building facade is realized.

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 agricultural machinery, and particularly discloses a self-propelled mulching film machine with damage detection and automatic repair functions, the mulching film machine comprises a rack, a walking mechanism and an energy mechanism, the walking mechanism is arranged at the bottom of the rack, and the energy mechanism is arranged at the front end of the top of the rack; a film paving / collecting mechanism and a control system are arranged between the walking mechanisms at the bottom of the rack; a visual sensor is arranged on the rear side of the film paving / collecting mechanism on the rack; a soil covering mechanism is arranged on the outer side of the traveling mechanism at the rear end of the rack; the control system is electrically connected with the walking mechanism, the film laying / collecting mechanism and the visual sensor. By the adoption of the self-propelled mulching film machine with the damage detection and automatic repairing functions, the mulching film machine can detect the damage condition of a mulching film in real time in the mulching film laying process, differentiated repairing strategies are automatically adopted according to the damage severity degree, and then intelligent detection and automatic repairing in the mulching film laying process can be achieved; and the operation quality and efficiency are improved.

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 building engineering, in particular to a building structure damage detection method, system and equipment and a medium thereof. The method comprises the following steps: collecting multi-modal data of a building structure, and preprocessing the multi-modal data; constructing a three-dimensional model of the building; performing damage identification and classification on the multi-modal data through a damage detection classification model to obtain damage information; mapping the damage information to a three-dimensional model, and carrying out visual monitoring on the building damage; analyzing the multi-modal data through the damage prediction model, and performing damage prediction on the building structure to obtain a prediction result; and generating a damage assessment report according to the damage information and the prediction result. The building structure damage detection method provided by the invention has the advantages of high detection efficiency, high detection precision, real-time early warning and high safety.

The invention discloses a composite material wind power blade damage detection method, and aims to solve the problems of difficulty in quantitative analysis and low distinguishing precision of various damage types in composite material wind power blade damage detection in the prior art. Comprising the following steps that acoustic emission signals in the operation process of the wind power blade are obtained in real time and preprocessed, and the acoustic emission signals comprise parameter data and waveform data; performing unsupervised clustering on the preprocessed parameter data to obtain a preliminary clustering result; training the preprocessed waveform data by adopting an improved multi-branch convolutional neural network, and outputting an accurate classification result; the unsupervised clustering result is verified by using the supervised learning classification result, and the accuracy of the clustering result is evaluated; and fusing the quantitative index accumulated energy, the damage type and the frequency, and constructing a comprehensive damage evaluation model to determine the damage degree. According to the method, the unsupervised clustering method and the supervised learning method are combined, and accurate identification and quantification of the damage are realized.

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 discloses an aero-engine gear damage analysis method and system based on image recognition, and relates to the technical field of image recognition, and the method comprises the steps: obtaining a gear surface image of a to-be-detected aero-engine gear at multiple angles; preprocessing the gear surface image; sequentially performing gear region segmentation, damage region positioning and damage feature quantification processing on the preprocessed gear surface image to obtain gear damage features; and performing damage analysis on each damage area based on the gear damage characteristics to obtain a damage analysis result which at least comprises a damage type judgment result, a damage severity grading result and a residual life estimation result. The technical problems that the aero-engine gear damage detection efficiency is low, the omission ratio is high, tooth surface damage is difficult to detect, damage evolution cannot be predicted, the remaining life cannot be evaluated, and the maintenance requirement of the aero-engine gear cannot be met can be solved.

The invention relates to the technical field of spacecraft on-orbit health monitoring, and particularly discloses a spacecraft appearance damage detection method and system based on multi-modal data fusion, and the method comprises the steps: obtaining the current multi-modal data of an outer surface damage region of a target spacecraft, and carrying out the data preprocessing and cross-modal alignment, and obtaining the target multi-modal data; performing feature extraction on the target multi-modal data to obtain a damage feature of each modal, and performing damage detection on the damage feature of each modal to obtain a damage detection result of each modal; and performing cross-modal feature fusion according to the damage detection result of each modal and the dynamic weight to obtain an appearance damage detection result of the target spacecraft. According to the method, through multi-modal fusion and innovative process design, the accuracy of spacecraft appearance damage detection is improved, the method is particularly suitable for on-orbit service tasks in a complex orbit environment, and an efficient solution is provided for real-time evaluation and autonomous decision making of the health state of the spacecraft.

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 a vertical shaft hoisting skip bucket lining plate damage detection method based on machine vision, and relates to the technical field of machine vision, and the method comprises the steps: collecting and preprocessing a multi-modal data stream, carrying out the time-space alignment of the multi-modal data stream through timestamp synchronization, and outputting a multi-modal data set; constructing a multi-scale feature pyramid, extracting local detail features and capturing global deformation features, dynamically allocating weights of different scale features by using reinforcement learning, and generating a dynamic multi-scale damage detection model through pre-training; and according to the three-dimensional deformation report, when an unrecorded damage type is detected, transmitting an abnormal sample to a cloud, and updating the dynamic multi-scale damage detection model through a federated learning framework. According to the method, the robustness and the accuracy of damage detection in a complex mine environment are improved through a multi-modal space-time alignment and cross-modal attention fusion mechanism.

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 locomotive wheel polygon damage detection method based on a lightweight neural network, and the method comprises the steps: obtaining axle box vertical vibration acceleration response signals of a heavy-load locomotive under the combination of different operation speeds and different wheel polygon abrasion degrees, and carrying out the preprocessing of the signals, the method comprises the following steps: acquiring frequency domain characteristics of a signal through fast Fourier transform, constructing a sample data set based on the frequency domain characteristics, constructing a wheel polygon damage detection network model, training the model by using the sample data set, and identifying a wheel polygon abrasion amplitude by using the trained polygon damage detection network model. And completing damage detection of the polygon of the wheel. The method can realize accurate and quantitative detection of the polygon abrasion degree of the heavy-load locomotive wheel, has the characteristics of accuracy, high efficiency and strong robustness, and also has relatively good interpretability.

The invention relates to the technical field of damage detection, and discloses a screen protection film damage detection method and system, and the method comprises the steps: carrying out the laser interference thickness scanning of a screen protection film, and obtaining three-dimensional thickness distribution data and thickness gradient vector field data; performing multi-layer polarization interference detection on the circularly polarized light beam based on the three-dimensional thickness distribution data to obtain polarization phase difference distribution data; according to the thickness gradient vector field data, non-periodic phase unenveloping processing is carried out on the polarization phase difference distribution data to obtain continuous polarization phase distribution data; performing stress optical coefficient tensor calculation and dynamic elliptical polarization modulation based on the continuous polarization phase distribution data to obtain stress damage space positioning data; the edge cutting area and the center bending area of the screen protection film are subjected to damage imaging reconstruction, a three-dimensional stress damage imaging result is obtained, and full-chain detection from geometric thickness change to stress distribution and then to damage positioning is achieved.

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.