326 results about "Equipment Defects" patented technology

The invention relates to the field of data processing, in particular to a method for defect detection of power equipment and a related product, and the method comprises the steps: obtaining defect image data and text data of the power equipment, preprocessing the image data and the text data, and constructing a power equipment association knowledge graph; the visual detection features and the knowledge graph features are fused through a cross attention mechanism, and multi-modal joint features are generated; performing fine adjustment on the LLaVA model based on the multi-modal joint features to obtain a defect detection model; acquiring a real-time image, and inputting the real-time image into the defect detection model to complete defect detection of the power equipment; through the multi-modal data fusion technology, the visual data, the text data and the associated knowledge graph of the power equipment are fully utilized, and the defects of a traditional single data source detection method in a complex scene are overcome.

The invention discloses a substation equipment defect detection method based on multi-modal normal sample learning, which is suitable for unmanned aerial vehicle data acquisition. The method comprises the following steps: step 1, acquiring multi-modal data of substation equipment and preprocessing the multi-modal data; 2, extracting and fusing features from the multi-modal data preprocessed in the step 1 to obtain a feature set; dividing the feature set into a training set and a verification set; 3, adopting a machine learning model, and performing normal sample learning by utilizing the training set and the verification set to obtain a normal classification model; 4, features are extracted from the to-be-detected substation equipment multi-modal data and then compared with the normal classification model, and whether defect features exist in the to-be-detected substation equipment multi-modal data or not is judged through difference analysis. The detection accuracy can be improved, manual intervention is reduced, dependence on defect samples is reduced, and the method has the advantage of being high in adaptability.

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 the field of intelligent monitoring, and discloses an infrared identification-based equipment defect detection method, which comprises the steps of infrared thermal imaging data acquisition, noise reduction processing, nonlinear heat conduction modeling, high-order partial differential optimization and variational regularization inversion solution. An infrared thermal imager collects equipment surface temperature distribution data, after multi-scale wavelet noise reduction and boundary heat flow balance optimization, a non-linear heat conduction model is established by utilizing a temperature-related heat conduction coefficient, a heat diffusion boundary is optimized by combining a high-order Laplace operator, defect heat source distribution is inverted through a variational regularization method, and the defect heat source distribution is calculated. And geometric characteristics, thermal parameters and position coordinates of the defects are accurately extracted. The method can adapt to complex working conditions, realizes high-precision detection and characteristic analysis of equipment defects, overcomes the problems of noise interference, insufficient boundary processing, weak defect inversion capability and the like in the prior art, and is widely applied to fault diagnosis and health monitoring of industrial equipment.

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 provides an electric power system main equipment defect diagnosis method and system based on a large language model, and relates to the technical field of electric power system operation and maintenance and intelligent diagnosis, and the method comprises the steps: collecting defect description information of electric power system main equipment; preliminarily understanding the defect description information by using a pre-constructed natural language representation model, obtaining corresponding embedded representation, executing a defect level classification task and a defect basis classification task, and outputting a defect level classification result and a defect basis classification result; and summarizing by utilizing a large language model, fusing the summarized classification information into a prompt text, exciting the reasoning ability of the large language model through a prompt project, and finally obtaining a diagnosis basis and a diagnosis result corresponding to the defect description information. According to the method, excellent results are obtained in defect level classification tasks and question and answer tasks, and high-quality classification evidences and guidance information can be generated.

The invention provides an image generation method and system based on substation equipment defects, electronic equipment and a computer readable storage medium. The method comprises the steps of obtaining an original substation equipment defect text; generating a transformer substation equipment defect basic image based on the original transformer substation equipment defect text by using a diffusion model; utilizing a diffusion model to generate a transformer substation equipment defect image according to the transformer substation equipment defect basic image and the fine-grained transformer substation equipment defect text; wherein the fine-grained substation equipment defect text is obtained by performing text enhancement on the original substation equipment defect text through a text enhancement model based on a Decoder-only architecture. According to the method, the diffusion model framework including two-stage image generation is arranged, so that the technical effect of improving the synthesis accuracy and quality of the text-driven substation equipment defect image is achieved.

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 provides a robustness evaluation method based on a power equipment defect detection model, and relates to the technical field of power equipment defect detection, and the method comprises the steps: obtaining an original sample of a power defect detection image to build a training set and a test set; applying an adversarial attack algorithm to the test set to generate an adversarial sample; respectively inputting the training set and the confrontation sample into a to-be-tested power equipment defect detection model to obtain a classification result of the original sample and a classification result of the confrontation sample; calculating robustness evaluation indexes of the to-be-tested model according to classification results of the original samples and the adversarial samples; calculating weights of different evaluation indexes through an entropy weight method, and calculating a comprehensive score according to each weight to obtain an evaluation result; according to the method, the robustness difference of different models under adversarial attack is deeply analyzed, so that the accuracy and the reliability of defect detection of the power equipment are improved, the performance and the practicability of the deep learning model in a power defect detection scene are improved, and the method has wide application prospect and practical value.

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 substation power equipment defect detection method, medium and system, and the method comprises the steps: S1, obtaining an image of substation power equipment, and marking defects in the image to obtain a defect picture data set; s2, constructing an improved YOLOv8s network model: constructing an improved YOLOv8s backbone network, a YOLOv8s neck network and a YOLOv8s decoupling detection head, and constructing the improved YOLOv8s network model; s3, training and testing the improved YOLOv8s network model constructed in the step S2 through the electrical equipment defect picture data set in the step S1 to obtain an electrical equipment defect detection model; and S4, inputting the substation equipment picture into the electrical equipment defect detection model to obtain a substation electrical equipment defect detection result. The method has the advantages of high detection precision and the like.

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.

The invention discloses a construction method and system of a power transformer equipment defect knowledge graph and a storage medium. The method comprises the following steps: acquiring structured data and unstructured data of transformer equipment operation defects; constructing an ontology layer of the transformer equipment defects, and carrying out structured expression on the relationship among all ontologies in the ontology layer; taking the constructed ontology layer as a reference, extracting the structured data to obtain a first target triple, and extracting the unstructured data to obtain a second target triple; and performing knowledge fusion on the first target triad and the second target triad to obtain transformer equipment defect map data. According to the method, the accuracy of entity recognition and relation recognition can be improved, fusion of multi-dimensional data such as equipment basic information, operation state information and defect information is better realized, so that operation and maintenance personnel are more effectively helped to quickly retrieve, position and process transformer defects, and the workload of the operation and maintenance personnel is reduced.

The invention relates to the field of power system equipment monitoring and diagnosis, in particular to an intelligent substation secondary equipment defect diagnosis method. The method comprises the steps that multi-source signals of the operation state of substation equipment are collected in real time and filtered, real-time monitoring signals of an intelligent substation are obtained, reverse rearrangement and segmentation are carried out, and then a reverse dynamic relation matrix is constructed; mapping the reverse dynamic relation matrix to a high-dimensional nonlinear feature space, performing dynamic optimization, dynamically fusing final feature values of obtained high-dimensional nonlinear feature vectors to obtain a final fused feature vector, and further obtaining a defect classification result; and performing trend prediction based on the final fusion feature vector and a defect classification result. The problems that a traditional equipment monitoring method is insufficient in dynamic feature extraction capacity, insufficient in adaptive capacity to a nonlinear complex mode, lack of effective connection between a defect classification result and running state trend prediction and influence the overall effect 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 discloses a multi-modal power grid equipment defect detection method and system. The method comprises the following steps: performing defect detection on power grid equipment by applying a random forest algorithm after training optimization; wherein the training process of the random forest algorithm is as follows: acquiring laser point cloud data, visual camera images and ultrasonic data with power grid equipment defects; preprocessing the laser point cloud data and the visual camera image, and converting the ultrasonic data into an ultrasonic image; performing data fusion on the preprocessed laser point cloud data, the visual camera image and the ultrasonic image, and constructing a multi-modal three-dimensional data information model; a reference defect-free model is set, the multi-modal three-dimensional data information model and the reference defect-free model are compared in the aspects of surface defects and internal defects, and difference results are obtained; and performing power grid equipment defect detection training on a random forest algorithm by using a difference result. According to the invention, the precision and integrity of defect detection are improved.

The invention belongs to the technical field of image processing, and provides a small-target lightweight substation equipment defect detection method and system, and the method comprises the steps: obtaining a substation equipment image; according to the acquired image and a preset small target lightweight defect detection model, completing defect detection of the substation equipment; wherein the preset small target lightweight defect detection model adopts a YOLOv8 target detection network which adopts an Officient Former V2 and a separation convolution attention mechanism as a backbone network, adopts scale time sequence feature fusion as a neck network and adopts a prediction head network of a shared convolution lightweight detection head, and image features are extracted based on the backbone network; obtaining feature maps of different scales; the neck network performs feature fusion on the feature maps with different scales to obtain a multi-scale feature map; and detecting the multi-scale feature map through the shared convolutional lightweight detection head, and performing defect positioning of the equipment image to complete defect detection of the substation equipment.

The invention relates to the technical field of information, and provides a power grid equipment information visualization method and system, equipment and a medium, and the method comprises the steps: carrying out the region division of main network equipment through employing a hierarchical clustering algorithm according to the attribute of the main network equipment; when it is monitored that the line loss rate of a certain line exceeds a preset line loss rate threshold value, automatically obtaining region division information of equipment to which the corresponding line belongs, and extracting historical operation data of the equipment from a cloud database of the equipment in the corresponding region; an Apriori association rule mining algorithm is adopted to analyze the relevance between the historical operation data of the equipment and the line loss rate abnormity, and an equipment defect and line loss rate relevance analysis result is obtained; and acquiring real-time monitoring data of equipment corresponding to the line with the abnormal line loss rate, and verifying and correcting an equipment defect reason judgment result by comparing and analyzing the real-time monitoring data with historical operation data of the equipment and adopting an isolation forest anomaly detection algorithm. According to the invention, rapid positioning of line loss rate abnormity and accurate prediction and visual management of equipment defects are realized.

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 discloses an electrical equipment defect detection method and system based on a multi-modal large model, and the method comprises the steps: analyzing a collected electrical equipment image, obtaining an equipment type and an equipment defect in the electrical equipment image, and marking the equipment type and the equipment defect; based on the equipment types and the equipment defects, respectively constructing corresponding tables of the equipment types and the equipment defects with attributes; constructing a self-attention equipment fault detection model; and inputting the marked electrical equipment image into the self-attention equipment fault detection model for training, and setting a basic task until the precision of the basic task reaches a specified threshold, thereby completing defect detection of the electrical equipment. According to the method, the problems of uncertain data preparation standard, indefinite training process, low professional adaptability and the like of the current large model in training and application in the power field can be solved, the accuracy of the large model in power equipment defect detection is effectively improved, and the operation and inspection quality efficiency is improved.

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.