2027 results about "Damage detection" patented technology

The invention discloses a bridge damage detection method and system, and belongs to the technical field of bridge engineering.The method comprises the steps that image data, point cloud data and spectral data of a to-be-detected bridge are obtained; inputting the obtained image data, point cloud data and spectrum data of the to-be-detected bridge into an input module of the constructed bridge damage detection model, and determining feature vectors of the image, the point cloud and the spectrum; and a feature fusion module of the constructed bridge damage detection model fuses feature vectors of images, point clouds and spectrums, interacts and fuses obtained fused feature representations of all modals on different levels to obtain fused feature vectors, and outputs a bridge damage detection type through a classifier in a DNN classification layer. The method can improve the accuracy of bridge damage detection.

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 provides a machine vision-based heavy-load large holding tool driving wheel multi-mode damage detection method, and relates to the technical field of automatic detection, and the method comprises the steps: collecting the image, vibration and temperature data of a driving wheel, extracting the visual features of the image through a multi-scale pyramid network, and obtaining the multi-mode damage of the driving wheel; inputting a cross-modal feature fusion network in combination with eccentricity and runout features of the vibration signals and stress concentration features of the temperature data to obtain fusion features; inputting the fusion features into a multi-task detection module to identify the damage, and measuring the damage area, depth, crack growth rate and stress concentration degree based on image segmentation, a photometric stereo method, vibration signal analysis and temperature gradient analysis; and finally, in combination with the equipment operation condition and the damage evolution law, an improved evidence theory is adopted to fuse the evaluation indexes, and an evaluation report including the damage state, the development trend and the maintenance suggestion is generated.

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 a pavement structure damage detection method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring image detection data, radar detection data and sound wave detection data of a to-be-detected road surface; performing structural damage dual-channel identification on the image detection data, the radar detection data and the sound wave detection data to obtain initial structural damage detection data of the to-be-detected road surface; according to the preliminary structural damage detection data, determining physical model use guide information and physical model setting parameters corresponding to the to-be-detected road surface; and according to the physical model use guide information and the physical model setting parameters, performing mechanical analysis and fluctuation propagation analysis on the to-be-detected pavement by using each physical analysis model to obtain pavement structure damage detection data of the to-be-detected pavement. By adopting the method, the accuracy and reliability in pavement structure damage detection can be effectively improved.

The invention discloses a hanging basket transportation safety management method and system, and relates to the field of damage detection. Collecting weight data of the hanging basket; establishing a steel wire rope health state prediction model by using a multi-layer perceptron (MLP) algorithm, predicting the damage degree of the steel wire rope, and obtaining a health state prediction result of the steel wire rope; according to the health state prediction result of the steel wire rope, a bidirectional long-short-term memory neural network BiLSTM is used for prediction, and a damage trend prediction result of the steel wire rope in a future period of time is obtained; comparing the collected weight data of the hanging basket with a preset safety weight range to obtain a judgment result of whether the weight of the hanging basket exceeds the limit or not; according to the health state prediction result of the steel wire rope, the damage trend prediction result and the judgment result of whether the weight of the hanging basket exceeds the limit or not, a decision tree algorithm is adopted to generate a hanging basket operation control scheme; aiming at low safety of a hanging basket in the prior art, the safety of the hanging basket is improved.

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 provides a lightweight road damage detection method based on RT-DETR improvement, and the method comprises the steps: obtaining road damage image data, and dividing a data set; a backbone network of the RT-DETR model is modified to be composed of a plurality of multi-scale edge information enhancement modules, a hybrid encoder of the RT-DETR model is modified to be composed of an attention-based intra-scale feature interaction module and a multi-branch cavity convolution pyramid network module, and an RDD-DETR model is obtained after modification; and the RDD-DETR model is trained and is used for road damage detection in the road image data. According to the invention, the end-to-end multi-scale feature extraction and fusion network is used for road damage detection, so that the road damage detection accuracy and detection speed can be greatly improved.

The invention discloses an embedded corn ear damage detection method based on deep learning, and relates to the technical field of agricultural machinery, and the method comprises the steps: obtaining image data in an ear collection box in real time when a harvester works, and carrying out the processing of the image data through a pre-trained deep learning model, and obtaining a damage classification result; and preprocessing each piece of image data in the damage classification result, calculating the proportion of the damaged part of the preprocessed image data, and storing the proportion calculation result. According to the method, the proportion of the corn damage occurring in the corn harvesting process can be timely and accurately determined through the pre-trained deep learning model, and in addition, a part of data support can be further provided for improvement of a subsequent harvesting scheme or a harvester by calculating the proportion of the damaged part.

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 provides a photovoltaic panel damage detection method, a medium and an electronic device, belongs to the technical field of photovoltaic panel damage detection, and aims to acquire a photovoltaic panel surface image and a fluorescence image through an unmanned aerial vehicle, perform image registration and semantic segmentation by using a deep learning algorithm, accurately separate out a photovoltaic panel area and improve the detection accuracy. And then inputting into a pre-trained defect detection model, and detecting and marking various defects and damages on the surface of the photovoltaic panel. The method mainly comprises the steps that 1) an unmanned aerial vehicle collects a surface image and a fluorescence image; 2) image preprocessing and registration; 3) performing semantic segmentation to accurately extract a photovoltaic panel area; (4) detecting and positioning defects; and 5) marking defect information on the image. According to the system, an advanced computer vision technology is utilized, and an intelligent and efficient solution is provided for operation and maintenance of the photovoltaic power station. The technical problem that the existing image analysis method is difficult to accurately capture and identify because some tiny defects often exist on the surface of the photovoltaic panel and the characteristics of the defects are not obvious is solved.

According to the construction method and application of the road damage detection model based on the improved RT-DETR model, a cross-stage part-multi-scale edge feature fusion backbone network CSP-MEFFN is adopted as a backbone network, so that the sensitivity of the model to a road damage edge is remarkably improved; and a rectangular context fusion pyramid network RCFPN is designed as a neck network, so that dynamic adaptive fusion of shallow high-frequency details, middle-layer edge structures and deep semantic features is realized, and context perception of the model and the recognition capability of a target under a complex background are improved, thereby improving the accuracy of road damage detection of a traditional RT-DETR model.

The invention relates to the technical field of composite material damage detection, and particularly discloses a composite material damage intelligent detection and modeling method based on ultrasonic point cloud and deep learning, and the method comprises the steps: S1, data collection, S2, ultrasonic data processing, S3, simulation model construction, S4, PVT-RCNN model processing, S5, RI CP processing, and S6, damage model generation. According to the method, an ultrasonic point cloud data acquisition platform and a data processing method are designed, a sufficient composite laminate ultrasonic point cloud data set with obvious features is constructed, a PVT-RCNN model which is based on PV-RCNN and is improved by Swi n Transformer is designed, the feature extraction capability of the model on point cloud is improved, three-dimensional space information of damage is intelligently obtained, and the accuracy of the damage is improved. An RI CP registration algorithm and a damage model generation method are designed, a finite element model with damage is directly generated according to damage point cloud, and the material performance can be obtained through calculation.

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 relates to the technical field of container damage detection, in particular to a container damage detection method and system based on deep learning. Comprising the following steps: establishing a plurality of container categories, and establishing an image analysis model according to all container categories; setting an image acquisition strategy of the to-be-detected container, and obtaining an image data packet of the to-be-detected container according to the image acquisition strategy; generating a preprocessing result of the image data packet according to the image analysis model; generating a primary detection strategy of the to-be-detected container according to the preprocessing result, and generating a damage risk value of the to-be-detected container; a plurality of container categories are constructed based on different container equipment parameters, a sub-image model of each container category is constructed by using a deep learning technology so as to adapt to the damage conditions of different types of containers in the actual production process of different ports, preliminary damage detection is carried out by collecting image data of a to-be-detected container, and the detection accuracy of the to-be-detected container is improved. Container damage detection efficiency is improved, and detection operation and maintenance cost is reduced.

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 relates to a steel wire rope surface damage detection method and detection device.The steel wire rope surface damage detection method comprises the following steps that a binocular vision detection device is arranged, the binocular vision detection device comprises two cameras, and the positions of the cameras and a light source are adjusted so that the surface of a steel wire rope can be clearly shot; acquiring an image of the surface of the steel wire rope through a camera, and transmitting the image to a computer; preprocessing the image to at least improve the quality and contrast of the image; feature extraction is carried out on the preprocessed image to extract the features of the surface damage of the steel wire rope, and feature extraction comprises edge detection, contour extraction and texture analysis; analyzing the extracted features by adopting a damage identification algorithm, and judging whether the surface of the steel wire rope is damaged or not; outputting a detection result of the surface damage of the steel wire rope, wherein the detection result comprises damage types, damage positions and damage degrees. The detection method can automatically identify the surface damage of the steel wire rope, reduces manual intervention, and improves the detection efficiency and accuracy.

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.