253 results about "Equipment Defects" patented technology

The invention provides a defect identification method and device for substation equipment and electronic equipment, and relates to the field of image identification. According to the method, an infrared image, an electric field leakage map and a visible light image are obtained through a multi-channel imaging system deployed in a substation site, and a multi-channel image tensor is generated and input into a multi-channel recognition model to extract fusion features. And fusing the features, inputting the fused features into a YOLOv8 backbone network, constructing a joint attention domain in combination with an equipment prior structure, generating a high-confidence candidate box, and performing non-maximum suppression to obtain a detection result. And constructing an inter-frame residual tensor for a detection result to perform time sequence modeling, thereby improving the detection effect. And for equipment with complex shielding, complementing a structure contour through an edge prediction path, and finally outputting target boundary and defect positioning information. By implementing the technical scheme provided by the invention, defect identification of the substation equipment is facilitated.

The invention relates to a thermal power plant equipment defect intelligent monitoring and early warning method based on a multi-modal large model. The method comprises the following steps: collecting operation data, namely multi-modal data, of equipment in real time; the method comprises the following steps of: constructing a multi-mode Transform model; carrying out cross-modal data fusion and analysis; calculating the abnormal degree of the equipment; future equipment state prediction is carried out, and the probability of potential fault occurrence is predicted; and carrying out optimization on the multi-mode Transform model. The invention further discloses a thermal power plant equipment defect intelligent monitoring and early warning system based on the multi-mode large model. According to the method, the multi-modal Transform model is adopted, cross-modal feature fusion is carried out in combination with the image, sound, sensor and text data, the accuracy is higher, and the false alarm rate and the missing report rate are lower; trend analysis is carried out on the equipment state, the fault occurrence time can be predicted, the prediction advance is greatly improved, and the probability of sudden faults is reduced through combination of anomaly detection and prediction. In different thermal power plants and different devices, the migration adaptability is high.

The invention discloses a transformer substation defect detection model optimization and detection method based on YOLOv11, and relates to the technical field of computer image processing. The method comprises the following steps: on the basis of YOLOv11, replacing a down-sampling convolution module of a backbone network with an ADown module, replacing a C3K2 convolution block of a neck network with a ContextGuided module, introducing an AUX module in front of a detection head to obtain an improved model, and training the improved model through an equipment defect data set to obtain an optimized transformer substation defect detection model. The improved model significantly improves the detection efficiency and precision, and can be more suitable for defect detection tasks and safety standardization in the power industry.

The invention relates to the technical field of partial discharge detection, in particular to a leak detection and partial discharge detection method and system based on artificial intelligence, and the method comprises the following steps: obtaining a partial discharge pulse signal, extracting a phase mutation coordinate, analyzing an incremental feature, reconstructing a propagation waveform, constructing an amplitude gradient matrix, and screening a direction vector; and a backtracking path calculates a change rate to generate a time track, and track mapping and space positioning are completed in combination with image data. According to the method, the accuracy and the response speed of partial discharge detection are improved through multi-stage signal processing, extreme points and phase abrupt change are analyzed, more accurate discharge feature capture and equipment defect positioning are achieved, phase and amplitude data are combined, the discharge mode is recognized, a signal propagation path is reconstructed, the fault source positioning accuracy is improved, and the fault source positioning accuracy is improved. Through dynamic rendering, the intuition of a detection result is enhanced, real-time decision is supported, the method is more intelligent and efficient, the equipment fault risk is greatly reduced, and the safe operation level of power equipment is improved.

The invention provides an unmanned aerial vehicle automatic inspection method for a transformer substation, and the method comprises the steps: transmitting shot image data, flight state data and equipment state data back to a nest in real time through a wireless communication link in the inspection process of an unmanned aerial vehicle, carrying out the primary processing of the received data through the nest, and carrying out the butt joint with a remote intelligent inspection system, uploading the data to a remote intelligent patrol system; the remote intelligent inspection system receives and displays the inspection data of the unmanned aerial vehicle in real time, analyzes and processes the image data by using an intelligent analysis algorithm, identifies equipment defects, fault hidden dangers, abnormal operation states and environmental abnormalities, and generates a preliminary analysis result; according to the intelligent analysis algorithm, deep learning target detection, time sequence analysis and other technologies are utilized to carry out deep processing on image data, and various equipment defects, fault hidden dangers and environment anomalies can be accurately recognized. The defect finding capability is greatly improved, measures are taken in time for repairing, equipment faults are avoided, and safe and stable operation of the transformer substation is guaranteed.

The invention relates to a power equipment defect detection method and system. The method comprises the steps of firstly collecting historical defect image data of power equipment, and preprocessing to obtain a data set; s2, a deep learning model is built, the model architecture comprises a quantum neural network, a backbone network, a multi-scale feature fusion layer and a target position and category prediction layer, the deep learning model is trained by using the data set in S1, and a defect detection model is obtained; and finally, deploying the defect detection model to edge equipment, inputting the current target power equipment image into the defect detection model by the edge equipment, and outputting a power equipment defect detection result, thereby realizing power equipment defect detection. Compared with the prior art, the method has the advantages of being suitable for a complex inspection environment, accurate in detection, high in confidence degree and the like.

The invention provides a robot electrical equipment defect detection system based on multi-modal image processing. The system improves the accuracy and reliability of electrical equipment defect identification. Infrared and visible light images are jointly collected, and through a registration algorithm of multi-source features and equipment structure priori, space-time alignment of multi-modal images is achieved. Then, a dynamic weighted fusion strategy is utilized to generate fusion features with higher discriminative ability, and abnormal features are extracted through a double-branch mechanism to be verified with thermophysical consistency; and finally, constructing a neural network model fused with physical prior, and performing defect classification and positioning output on the verified feature data. According to the method, the structure and thermal information are fused, a physical constraint mechanism and a joint training strategy are introduced, the robustness and engineering interpretability of the system under complex working conditions are remarkably improved, and the method has a wide application prospect.

The invention belongs to the technical field of railway inspection, and particularly relates to a space-air-ground integrated railway power transformation and distribution intelligent inspection method and system which comprises a sensing layer, a platform layer, a data and intelligent center, an application layer and a user and presentation layer. The sensing layer is composed of an aerial inspection layer, a ground inspection layer and a fixed monitoring layer; the platform layer is based on a cloud computing center and edge computing node collaborative architecture, combines data and an intelligent center, integrates multi-source heterogeneous data through a big data platform, carries an AI algorithm engine to realize automatic identification of equipment defects, analyzes images, synchronously predicts the images, and realizes automatic identification of the equipment defects. And the application layer is combined to complete the functions of automatic generation of an inspection report, automatic distribution of a maintenance work order, equipment health state evaluation and the like. According to the method, functions of railway power place inspection, power supply line inspection, power supply and distribution facility operation and maintenance emergency and the like can be realized, deep fusion analysis of multi-dimensional data is realized, and meanwhile, the problems of insufficient training samples, sparse defect types and the like of an existing AI model are solved.

The invention discloses a transformer substation equipment defect automatic identification and positioning method based on digital twinning, and belongs to the technical field of intelligent operation and maintenance of digital twinning in power equipment. The method comprises the following steps: constructing a three-dimensional digital twin model of substation equipment, obtaining equipment point cloud data through laser scanning, carrying out registration on the equipment point cloud data and a BIM model, and establishing a virtual model containing equipment physical attributes; a multi-source heterogeneous sensor network is deployed, the multi-source heterogeneous sensor network comprises an optical fiber temperature measurement sensor, an ultrahigh frequency partial discharge sensor and an infrared dual-light camera, and mu s-level time synchronization is achieved through a PTP protocol; an edge-cloud cooperative processing architecture is established, a lightweight MobileNetV3 model is operated at an edge node to carry out preliminary defect screening, and a multi-task deep learning model is deployed at a cloud to carry out accurate analysis. By optimizing the data acquisition and processing flow and adopting efficient data transmission and real-time processing, it is ensured that the system can quickly respond to the equipment state change, and real-time monitoring of the equipment state is achieved.

The invention discloses a GIS equipment defect accurate positioning method and system based on acoustic visualization, and relates to the technical field of defect localization, and the method comprises the steps: collecting a vibration acoustic mixed signal in an equipment operation state, and separating the vibration acoustic mixed signal into a pure acoustic signal and a vibration signal through independent component analysis; an acoustic characteristic spectrum is constructed for the pure acoustic signals, a sound field propagation path model is generated, and meanwhile a corresponding relation graph of equipment components and the acoustic characteristic spectrum is established; and determining a source component and a specific position of the abnormal acoustic characteristic spectrum through an acoustic reverse tracking positioning algorithm in combination with the sound field propagation path model and the corresponding relation map. According to the method, by combining vibration acoustic conjoint analysis and acoustic reverse tracking, high-precision positioning of defects of hidden parts in the GIS equipment is achieved, the positioning precision and the recognition accuracy are improved, recognizable defect types are expanded, and the method is suitable for GIS equipment of different structures.

The invention discloses an equipment defect diagnosis method and system based on voiceprint features, equipment and a medium, and relates to the technical field of industrial equipment fault diagnosis, and the method comprises the steps: S1, obtaining equipment operation sound data; s2, decomposing equipment operation sound data through a sub-band weighted wavelet packet, and extracting industrial voiceprint feature data in a layered manner; s3, according to the industrial voiceprint feature data, constructing a multi-scale Mel-frequency cepstrum coefficient on the basis of a standard MFCC; and S4, establishing a bimodal reference voiceprint library, and reducing the voiceprint distance of the same kind of defects through triple loss. Compared with the prior art, the method has the advantages that the industrial strong-noise environment is optimized; features are accurately extracted; constructing a multi-scale voiceprint feature; establishing a bimodal reference voiceprint library; and the defect diagnosis decision is accurate.

The invention relates to a construction method of a power grid equipment defect training sample set and a defect detection method thereof in the field of computer vision and power inspection. The construction method comprises the following steps: potential space mapping; injecting noise and conditions; performing condition denoising; defects are directionally generated. The defect-free background and structure information is reserved under the guidance of a diffusion model, and defect features consistent with text vector description are generated in a specified area, so that a piece of defect-free image is converted into an image with a known fault state for forming a power grid equipment defect training sample set. According to the method, the controllably generated diffusion model is introduced to enhance scarce defect category samples, the sample diversity is improved in combination with a traditional and adversarial generation method, and the problems of incomplete data, scarce defect samples, unbalanced category distribution, insufficient fine granularity detection precision and the like generally existing in existing power grid equipment defect detection are solved.

The invention discloses a power grid equipment defect identification system based on a visual large model, and the system comprises the following parts: an image collection module which employs a high-definition industrial camera and an infrared thermal imager to collect an appearance image and a temperature image of power grid equipment; the image preprocessing module is used for preprocessing the appearance image and the temperature image; the defect recognition module is used for recognizing defects, including cracks, corrosion, deformation and discharge traces, on the power grid equipment through a deep learning model and image processing according to the appearance image and the temperature image; and the defect analysis report generation module is used for scoring the defects and generating a defect analysis report according to a scoring result, and the defect analysis report comprises defect types and defect severity. According to the method, more detailed and accurate power grid equipment image information is acquired, so that the accuracy of power grid equipment defect identification is improved.

Methods, apparatus, and processor-readable storage media for predicting device components for repair and / or replacement using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining information pertaining to at least one device defect; defining multiple device component categories related to the device defect(s); determining one or more of the device component categories as associated with the device defect(s) by processing at least a first portion of the information using one or more artificial intelligence techniques; identifying one or more device components associated with at least a second portion of the information; predicting at least one of the identified device component(s), based on comparing the identified device component(s) and the one or more determined device component categories, as needing to be repaired and / or replaced in connection with at least a portion of the device defect(s); and performing one or more automated actions based on the predicting.

The invention is suitable for the technical field of equipment defect identification, and provides a GIS equipment insulation defect identification method based on a recurrent neural network, and the method comprises the steps: collecting a multi-channel time sequence monitoring signal of GIS equipment; extracting time sequence features by using a recurrent neural network, and enhancing effective signal segments in the time sequence features by using an attention mechanism; processing through a pre-trained position classification model, and outputting defect position probability distribution vectors of the GIS electrical component units at preset candidate positions; constructing a graph structure, inputting the defect position probability distribution vector as a node feature into a graph neural network, and generating defect position probability distribution corrected based on spatial correlation through neighborhood feature aggregation; and comparing the defect probability value corresponding to each corrected candidate position with a preset dynamic threshold value to generate an insulation defect identification result. The false detection rate and the omission rate of equipment defect detection are effectively reduced, and the requirement of a power system for high-precision intelligent detection of GIS equipment is met.

The invention discloses a power transformation equipment defect processing method and system based on edge-cloud collaboration, and belongs to the technical field of power system automation, the power transformation equipment defect processing method based on edge-cloud collaboration comprises the following steps: synchronously acquiring and preprocessing a visible light image and an infrared image of power transformation equipment; the edge processing layer performs equipment part positioning on the preprocessed image, extracts structural features in the visible light image and temperature features in the infrared image according to a positioning result, preliminarily screens out a suspicious area meeting a suspected defect condition, and uploads the result to the cloud analysis layer; after the cloud analysis layer receives the data, visible light features and infrared features of the visible light image and the infrared image are extracted respectively, and cross-modal feature fusion is carried out by adopting an attention mechanism. Through the edge-cloud collaborative cross-modal recognition and dynamic optimization mechanism, the problems that efficiency and precision are difficult to consider at the same time, the single-modal omission ratio is high, and the model adaptability is poor in the prior art are effectively solved.

The invention provides a hydropower station full-scene intelligent inspection and safety supervision method and device. A two-legged inspection robot with anti-explosion and anti-vibration capabilities is deployed to realize all-terrain autonomous inspection of equipment nodes in the hydropower station; based on a digital twinborn model and an edge computing platform, fusing equipment state data and personnel behavior data acquired by a robot in real time, identifying equipment defects and personnel violation behaviors through an AI algorithm, and generating graded early warning information; the robot is controlled to carry out real-time intervention through a voice module, early warning information is pushed to a safety management system, and an equipment operation authority locking mechanism is linked; through optimizing a path planning algorithm and a multi-robot cooperative scheduling strategy, rapid response and autonomous obstacle avoidance of the robot under an emergent task are realized. According to the method, full-scene automatic inspection of the hydropower station and real-time supervision of personnel behaviors can be realized, the equipment defect identification accuracy and the emergency response efficiency are remarkably improved, and the manual inspection cost and the non-planned shutdown risk are reduced.

The invention relates to the technical field of thermal power plant safety production, and discloses a thermal power plant inspection and maintenance work site risk assessment method and system based on an AI visual technology, and the method comprises the steps: obtaining the AI visual data and environment perception data of an inspection and maintenance site through a data collection module; the risk factor extraction module identifies three types of risk factors including personnel violation, equipment defects and environment abnormity from the visual data; the risk assessment module calculates the weight of each risk factor through a subjective-objective fusion weight method, obtains a dimension risk value in combination with a correction coefficient, and further solves a comprehensive risk value; and the risk early warning and disposal module divides early warning levels according to the comprehensive risk values and generates targeted disposal measures. According to the invention, automatic and precise assessment of the multi-dimensional risk of the thermal power plant inspection and maintenance site is realized, the problems of missed judgment, misjudgment and early warning lag of traditional manual inspection are solved, the safety management cost is reduced, and the safety guarantee capability of inspection and maintenance operation is improved.

The invention provides an industrial inspection-oriented defect autonomous decision-making method, device and equipment and a medium, and belongs to the technical field of intelligent manufacturing. The equipment defect autonomous decision-making method comprises the following steps: acquiring equipment operation state information of industrial equipment by using a multispectral fusion terminal, and uploading the equipment operation state information to a cloud server; a built-in cross-modal deep analysis model of the cloud server is used to carry out cross-modal learning on the equipment operation state information according to the double-flow heterogeneous deep network architecture, and joint distribution of visual-text features of the industrial equipment is learned to obtain defect feature vectors of the industrial equipment; performing multi-dimensional matching on the defect feature vectors and similar cases in a maintenance knowledge base, and screening treatment schemes corresponding to the defect feature vectors to obtain a maintenance strategy; and issuing the defect information corresponding to the defect feature vector and the maintenance strategy to a communication terminal of a maintainer. According to the method and the device, the problem of low cross-modal information utilization rate caused by multi-modal data splitting and no feature level fusion in the prior art can be solved.

The invention discloses a GIS equipment defect three-dimensional visual positioning method based on acoustic imaging, and relates to the related technical field of GIS equipment, and the method comprises the steps: constructing a sound intensity detection dual system; sound source signals of the GIS equipment are synchronously acquired, and near-field acoustic signals and far-field acoustic signals are output; performing frequency band separation through a self-adaptive band-pass filter, and identifying near acoustic abnormal features and far acoustic abnormal features of the separated frequency band; carrying out spatial positioning fusion and outputting a positioning point; and importing a GIS equipment three-dimensional model according to the sound source fusion positioning point, and outputting a GIS equipment visual positioning result. The technical problems that in the prior art, the GIS equipment defect detection range is limited, the positioning precision is insufficient, the defect space position is difficult to visually present, the equipment defect positioning is not rapid and accurate enough, and the equipment operation and maintenance efficiency and reliability are affected are solved. The technical effects of quickly and accurately positioning the equipment defects and effectively improving the operation and maintenance efficiency and reliability of the equipment are achieved.

The embodiment of the invention provides a multi-scene equipment defect detection method, a control device and equipment, and the method comprises the steps: S1, obtaining an RGB image, an infrared thermogram and environment parameters of to-be-detected equipment through a multi-source sensor, and the environment parameters comprise one or more of illumination intensity, dust concentration and humidity; s2, inputting the RGB image into an improved YOLOv8 model, wherein the improvement comprises the step of additionally arranging a dynamic feature calibration module at the tail end of the Backbone; an anti-interference decoupling head is deployed in front of the detection head; s3, calculating cross-scene feature alignment loss, and constraining feature vector cosine similarity of the same type of defects under different environment parameters; and S4, fusing the abnormal region of the infrared thermogram and the detection result of the improved YOLOv8 model, and outputting defect positioning and classification information. According to the method, the problem of feature drift is solved through cooperation of the dynamic feature calibration module, the anti-interference decoupling head and cross-scene feature alignment loss, and the detection precision in a complex scene is improved.

The invention relates to the technical field of electrical equipment defect detection, and discloses an electrical equipment defect detection method and system. Voiceprint information and image information of electrical equipment are synchronously collected, and voiceprint features and image features are extracted respectively after preprocessing; and performing dimension raising interaction and low-rank compression on the two types of features by using a low-rank multi-modal data fusion model to generate a low-dimensional feature vector fusing voiceprint and image complementary information, and inputting the low-dimensional feature vector into a defect detection model to realize power equipment fault judgment and early warning output. According to the method, multi-modal data fusion and low-rank feature optimization are adopted, acoustic anomalies of the operation state of equipment are captured through voiceprint features, appearance structure defects are reflected through image features, multi-dimensional representation of faults is formed through combination of the multi-modal data fusion and the low-rank feature optimization, and the adaptability of the model to different fault modes (such as early hidden faults and dominant structure damage) is improved. The technical problems that an existing power equipment defect detection model is limited in generalization ability and poor in robustness are solved.

The invention relates to a power transformation equipment defect text standardization method and system in combination with deep semantic matching and a knowledge graph, and the method comprises the steps: inputting an actual power transformation equipment defect text into a text entity recognition model, and extracting an entity in the defect text as a to-be-standardized entity, querying a device model and a defect level corresponding to the defect text, and obtaining a defect device type according to the device model; all the standard descriptions corresponding to the entity types in the defect standard are used as candidate standard entities, the candidate standard entities and the to-be-standardized entities form entity pairs in sequence, the formed entity pairs are input into the FTS-BERT, and the standard descriptions of the entities are obtained through matching; constructing a power transformation equipment defect knowledge graph; in combination with FTS-BERT and knowledge graph search, standardizing a power transformation equipment defect text into a unified form; and adding the new knowledge into the original knowledge graph through incremental updating. The method and the system can automatically and effectively complete a power transformation equipment text standardization task, and solve the problem that defect text data is not standard due to manual recording.

The invention discloses an equipment defect analysis method and system based on a voiceprint system and transfer learning, and relates to the field of power plant equipment fault diagnosis and health monitoring, and the method comprises the steps: collecting an audio signal of equipment operation in real time; performing data representation and feature mapping based on the preprocessed audio signal to generate a first equipment defect analysis model; training the first equipment defect analysis model based on a transfer learning convolutional neural network to obtain a second equipment defect analysis model; analyzing through a second equipment defect analysis model, and outputting an equipment defect analysis result; according to the method, the problems of insufficient data and distribution difference in the target field are solved, and the generalization ability of the model and the defect detection precision are improved. The method can be widely applied to fault monitoring of power plant key equipment such as a fan, a pump and a steam turbine, and has high efficiency, adaptability and reliability.

The invention relates to the technical field of power equipment, and particularly provides a photovoltaic power equipment defect detection method and device based on multi-modal information, and the method comprises the steps: obtaining first multi-modal information of target power equipment; wherein the first multi-modal information comprises a first defect image and a first defect text of the target power equipment; inputting the first multi-modal information into a trained defect detection model, and outputting a first defect detection result of the target power equipment through the defect detection model; wherein the training data of the defect detection model comprises data of a power equipment defect grading rule knowledge base; the defect detection model comprises an image recognition module, an information fusion module, a defect query module and a defect reasoning module; the first defect detection result comprises a first image defect description and a first defect classification level. According to the invention, defect detection is realized through multi-modal information, the accuracy is high, and the credibility is high.

The invention discloses a defect detection method and device for substation equipment, equipment and a storage medium, and relates to the technical field of substations, and the method comprises the steps: obtaining a to-be-detected image of the substation equipment, and carrying out the illumination normalization processing of the to-be-detected image, and obtaining a to-be-detected image after space alignment; generating an abnormal probability mask based on the to-be-detected image after space alignment; according to the to-be-detected image of the substation equipment, acquiring an equipment area mask through an equipment segmentation model, and performing defect target detection in an area limited by the equipment area mask to obtain a defect position, category information and a first confidence coefficient; and fusing the abnormal probability mask, the defect position, the category information and the first confidence coefficient to obtain a final defect detection report. According to the method, the defect detection accuracy of the substation equipment can be improved.

The invention belongs to the field of artificial intelligence, and provides a new energy power generation equipment defect identification system and method based on deep learning, and the system comprises edge equipment, a cloud server, and an operation and maintenance terminal. The edge equipment is used for acquiring multi-source heterogeneous data of the new energy power generation equipment in an actual operation state, preprocessing the multi-source heterogeneous data, inputting a pre-constructed defect preliminary screening model to obtain a defect preliminary screening result output by the defect preliminary screening model, extracting a target defect of which the confidence is higher than a preset confidence threshold in the defect preliminary screening result, and sending the target defect to the new energy power generation equipment; the key multi-source heterogeneous data corresponding to the target defect is uploaded to a cloud server; and the cloud server is used for inputting the key multi-source heterogeneous data into the deep defect identification model to obtain a defect identification result output by the deep defect identification model, and sending the defect identification result to the operation and maintenance terminal. According to the scheme provided by the invention, the defect identification precision, the detection safety and the detection efficiency of the new energy power generation equipment are improved.

The invention discloses an equipment defect positioning method and device, a storage medium and computer equipment, and the method comprises the steps: obtaining a visible light image, an infrared image and an acoustic image of to-be-detected equipment collected by inspection equipment, extracting equipment image features from the visible light image, extracting infrared partial discharge features from the infrared image, and obtaining an infrared partial discharge feature of the to-be-detected equipment; and extracting acoustic imaging features from the acoustic imaging; performing feature fusion on the equipment image features, the infrared partial discharge features and the acoustic imaging features to obtain fusion features, and inputting the fusion features into a defect identification model to determine whether the equipment to be detected has defects; if the to-be-detected equipment has the defect, acquiring a first coordinate when the inspection equipment collects an image, and determining a second coordinate corresponding to the defect according to the first coordinate; and according to the second coordinate, positioning the defect on a three-dimensional model corresponding to the to-be-detected equipment.

The invention discloses an equipment defect grading system and method based on a large language model, and belongs to the technical field of power grid simulation. In the system, a large language model responds to a user instruction, generates a command for calling a knowledge base to retrieve and a corresponding retrieval keyword according to equipment and phenomenon description corresponding to the user instruction, and then calls the knowledge base to retrieve a defect grading standard related to the user instruction; the processing module is also used for sending a calling instruction based on the defect grading standard and a user instruction so as to obtain additional information or carry out numerical value comparison to carry out auxiliary grading; obtaining the final predicted defect level according to the defect grading standard and additional information or numerical comparison information; and the planning executor calls the auxiliary plug-in according to a calling instruction sent by the large language model, and feeds back a calling result to the large language model. Accurate grading of equipment defects is achieved, generalization is enhanced, the numerical calculation problem is solved, and the output structure problem is relieved.

The invention provides an electrical equipment defect identification method and system based on machine vision, and relates to the technical field of equipment defect identification, and the method comprises the steps: collecting and preprocessing a standard electroluminescent image, a standard infrared thermal image and a standard visible light image of a target photovoltaic string under different time windows through an imaging device carried by an unmanned aerial vehicle, and carrying out the collection of the standard electroluminescent image, the standard infrared thermal image and the standard visible light image; meanwhile, recording environmental parameters collected each time; analyzing visual defect features and attenuation gradient features in the standard electroluminescent image, and performing fusion verification by combining the standard infrared thermal image, the standard visible light image and the electrical performance data to obtain definite judgment of potential-induced attenuation; for the photovoltaic module which is diagnosed and judged to have potential-induced degradation, based on the environmental parameters, evaluating by using a time sequence analysis method to obtain the evolution rate of the potential-induced degradation, and mapping to generate the risk level of the potential-induced degradation; and generating a structured defect identification report. And the accuracy of defect identification of the power equipment is improved.