206 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 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 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.

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 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 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 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.

The invention discloses a defect type identification method and device for transmission, transformation and distribution equipment, and belongs to the technical field of equipment defect identification, and the method comprises the steps: obtaining a defect region image and equipment operation parameters of to-be-identified abnormal transmission, transformation and distribution equipment; establishing a polar coordinate system for the defect area image, and determining a texture trend and a corresponding texture width in the defect area image; calculating gray difference values between the texture pixel points and adjacent texture pixel points in different directions, and determining a crack area; determining a defect detection image according to the texture trend, the texture width and the crack area; and finally, inputting the defect detection image and the corresponding equipment operation parameters into a preset defect identification model for knowledge reasoning analysis to obtain a defect type. Through the implementation of the invention, the problems that the defect type identification method in the prior art is poor in capability of identifying small-size defects such as cracks, small defects cannot be found and processed in time, and then the stable operation of a power grid is influenced can be solved.

The invention discloses a GIS equipment defect intelligent detection method based on computer vision, and aims to solve the problems that transient defect acquisition in a GIS cabinet is delayed and asynchronous alignment of audio and video is difficult. According to the method, time reference and annular pre-buffering are unified, collection of a streaming voiceprint trigger linkage event camera and a high-speed polarization camera, monotonic alignment Transform of arrival time difference prior constraint and residual time offset calibration, secondary collection closed loop of confidence gating and multi-modal fusion judgment are carried out, and multi-modal fusion judgment is carried out. The technical effects of low-delay triggering, order-preserving alignment, accurate time positioning, evidence chain integrity rate improvement and missing detection and false detection reduction are realized.

The invention discloses a power transmission equipment insulator defect identification method and device, equipment and a medium, and belongs to the field of power equipment defect identification, and the method comprises the steps: respectively extracting corresponding feature data from electric signal monitoring data, infrared data and image data of an insulator string; fusing the characteristic data corresponding to the electric signal monitoring data and the infrared data through a cross-modal attention mechanism, and positioning an abnormal region; determining a defect physical region image of the insulator string according to the abnormal region and feature data corresponding to the image data; performing secondary verification on the defect physical region image according to the environmental parameter data of the space where the insulator string is located; and when the secondary verification is passed, respectively calculating a plurality of defect judgment indexes of the insulator chain according to the defect physical region image, and determining that the insulator chain has defects when any defect judgment index meets a preset condition. According to the invention, the problem of low insulator defect identification accuracy in a complex environment can be solved.

A method, system and device for identifying coal-fired equipment defects based on a large model relate to the technical field of equipment defect identification, and the method comprises the following steps: training labeled target domain data to generate a primary coal-fired equipment defect identification model; migrating the general features and the invariant domain features extracted from the source domain data to the first-level model through transfer learning to generate a second-level model; inputting the sensor data and preset random noise into the generative adversarial network embedded with the thermodynamic residual project to generate standard counterfeit defect data; training the standard counterfeit defect data to generate a third-level model; generating a cross-modal fusion feature based on a preset strategy and adding the cross-modal fusion feature into the third-stage model to generate a fourth-stage model; and inputting a to-be-identified coal-fired equipment picture and working condition parameters into the four-stage model to obtain an identification result and confidence. Through multi-stage model training and cross-modal fusion feature extraction, the problems of low recognition precision and poor generalization ability in related technologies are solved, and the accuracy and reliability of coal-fired equipment defect recognition are improved.

The invention relates to the technical field of equipment defect detection, and discloses an equipment defect comprehensive detection system and method based on three modes. The system comprises a multi-modal data acquisition module which acquires infrared temperature data, X-ray image data and visual image data of equipment in real time; the comprehensive data processing module processes the data and generates standardized defect features; the defect risk assessment module assesses the comprehensive risk level of the defect based on the characteristics; the detection stage division module divides the detection process into a plurality of stages and determines a detection focus of each stage; the parameter rejection degree evaluation module evaluates the rejection degree of the detection parameters based on the equipment material characteristic data and the detection focus; the adjustment amount determination module determines the adjustment amount of the detection parameter based on the rejection degree and the risk level; and the detection regulation and control module regulates and controls the detection parameters according to the adjustment amount and executes defect processing actions. According to the system, integrated detection of multiple types of defects can be realized, and the detection flexibility and practicability are improved.

The invention discloses a GIS equipment defect digital diagnosis operation and maintenance method and electronic equipment, and relates to the technical field related to equipment diagnos.The method comprises the steps that after a UHF sensor and an ultrasonic sensor are deployed, an acquisition signal data set is established; extracting a time sequence signal feature set; performing operation state analysis according to a time sequence equipment task, and establishing tolerance partial discharge behavior compensation; executing time sequence clustering of the time sequence signal feature set; performing authentication defect identification according to the independent anomaly identification result, and establishing a first anomaly identification result; inputting to an anomaly recognition channel, and establishing a second anomaly recognition result; and generating a diagnosis result. The technical problems that in the prior art, signal collection is prone to interference, so that feature extraction deviation is caused, normal partial discharge fluctuation and abnormal defects of equipment are difficult to distinguish, misjudgment and missed judgment are prone to being caused, and the equipment state cannot be accurately evaluated in real time are solved, and the technical effects that the accuracy and the real-time performance of GIS equipment defect diagnosis are improved, and the equipment fault risk is reduced are achieved.

The invention discloses a method and a device for identifying defects of power transformation equipment, which are used for solving the technical problem that the existing method for identifying the defects of the power transformation equipment limits the environment adaptability of a model, so that the identification effect is poor. The method comprises the following steps: acquiring a substation equipment image and a plurality of predefined substation equipment state text descriptions; and adopting a preset lightweight image-text matching model to carry out substation equipment defect identification according to the substation equipment image and the plurality of predefined substation equipment state text descriptions, and outputting a substation equipment defect identification result.

The invention relates to the technical field of power system fault protection, in particular to a power grid secondary equipment defect identification method based on knowledge graph and Bayesian network fusion, and the method comprises the steps: carrying out the semantic extraction of multi-source heterogeneous data to construct a defect knowledge graph, breaking through the limitation of a data island, and achieving the full-dimensional correlation analysis of equipment defect features; mapping the power grid secondary equipment defect knowledge graph to a Bayesian network framework, and quantifying the prior probability and conditional probability of defect occurrence based on historical data prior knowledge; and finally, providing accurate fault mode positioning by means of defect sub-graph search of the knowledge graph, and completing defect propagation path prediction by means of probability reasoning of the Bayesian network, so that the method can comprehensively utilize multi-source heterogeneous data of the secondary equipment of the power grid, and the fault propagation path prediction accuracy is improved. The complex relation and probability influence between the defect phenomenon and multiple layers of reasons are accurately described, the defect recognition accuracy and response timeliness are remarkably improved, and support is provided for safe and stable operation of a power grid.

The invention provides edge equipment, center equipment, a defect detection method, equipment and a medium. According to one example of the application, the edge device may include an edge detection unit and an edge management unit; wherein the edge detection unit is connected with the edge management unit and is used for acquiring detection data of a to-be-detected product based on configuration data of the to-be-detected product and generating a detection result of the detection data by utilizing a defect detection model; and the edge management unit is used for sending the detection result to the central equipment, so that the central equipment judges that the to-be-detected product has the defect problem.

The present application belongs to the technical field of glass product production management and data processing, and relates to a glass product production management method and system. The method comprises: collecting glass surface ripple signals and process parameters of a production line in real time; calculating a local curvature sequence and a local curvature entropy reflecting the sharpness of the micro-waveform of the signals, and adaptively determining a dynamic bandwidth adjustment factor of a variational mode decomposition algorithm based on the local curvature entropy; using the dynamic bandwidth adjustment factor to perform mode decomposition on the glass surface ripple signals, and extracting a characteristic component matching the frequency of a roller fault; calculating a physical real wear index after eliminating the influence of the speed according to the characteristic component and the glass drawing speed, and making a device maintenance decision based on the index. The present application can accurately identify device defects under complex working conditions by adaptively adjusting algorithm parameters and decoupling the influence of speed, thereby improving the accuracy of maintenance decisions.

The invention relates to the technical field of safety protection, in particular to a valve cooling external cold water cooling tower abnormal sound online monitoring device and safety monitoring method based on a sound sensor, and the device comprises a microphone collection module which is arranged on a target valve cooling external cold water cooling tower and is used for obtaining acoustic signals generated in the operation process of the cooling tower in real time, the acoustic signal is converted into digital audio data; the MCU mainboard module is connected with the microphone acquisition module and is used for receiving the audio data of the microphone acquisition module and realizing feature extraction, model reasoning and logic control; the communication module is used for transmitting processing information of the MCU mainboard module to a mobile terminal to realize online early warning of abnormal sound of the valve cooling external cold water cooling tower, and the device realizes real-time sound monitoring of the cooling tower, judges equipment of which the sound exceeds a normal range, and sends an alarm signal at the terminal to ensure that equipment defects are found as early as possible and treated in advance; defect deterioration is avoided.

The invention provides an infrared defect discrimination method based on an improved neural network and a related device, and the method comprises the steps: obtaining an infrared image of power inspection, and carrying out the processing through employing a constructed infrared equipment image recognition model and a temperature difference recognition model, and obtaining a target detection and temperature difference result. The temperature difference result reflects the difference between the target current temperature and the reference temperature. And based on the temperature difference judgment basis of the equipment defect, carrying out logic analysis on the detection result to determine the equipment defect type. An infrared equipment image recognition model is based on an improved YOLOv5 network, and the target recognition effect is improved by introducing an Octave Conv module and a CARAFE operator to optimize feature extraction and up-sampling stages. The improved recognition model is used for automatically processing the infrared image, manual interpretation is reduced, misjudgment caused by visual fatigue is avoided, the method is not limited by professional ability of personnel, the judgment efficiency is improved, the processing time is shortened, and the infrared heating defect problem can be solved in time.

The present invention discloses a data-driven intelligent defect identification method and system for power equipment, which relates to the technical field of power equipment defect identification, including obtaining historical defect records of power equipment from a power grid platform, analyzing the state parameters in the defect identification data to determine the abnormality of the power equipment defects; analyzing the operating parameters in the equipment operation records to determine the parameter influence on the abnormal state parameters in the parameter abnormality data; predicting the change trend of the state parameters of the power equipment in the current cycle, obtaining parameter prediction data, and combining the defect identification data to identify the equipment defects of the power equipment; obtaining characteristic defect root cause data of the defective power equipment, obtaining parameter prediction data, analyzing the defect root cause of the defective power equipment, sending the defect root cause data of the defective power equipment to staff through the power grid platform, and prompting the staff to repair the defective power equipment.