1164 results about "Template matching" patented technology

The present invention relates to an adaptive sensing-based lightweight monitoring method for a fine crack in a complex background region. The method comprises the following steps: step S1, on the basis of region division, performing automatic acquisition of crack information, wherein PTZ camera sensors are used to automatically perform block-wise acquisition on crack regions; step S2, performing an adaptive complex scale calibration process, using a multi-scale template matching algorithm to adaptively correct distortion information of all regions, and performing real-scale conversion from pixel precision; step S3, constructing a lightweight crack segmentation network to process data processed in step S2; and step S4, by means of a quantitative crack-tracking algorithm based on Euclidean distance similarity classification, performing real-time monitoring on each piece of crack dynamic information. Compared with the prior art, the present invention has advantages such as achieving efficient, accurate, and online monitoring and analysis of cracks.

The invention relates to an AI-based automatic adaptation method for a work approval process, and the method comprises the steps: collecting multi-source approval data, and carrying out the preprocessing of the multi-source approval data, and forming standardized approval data; analyzing the system rule text by using the pre-trained AI large model, and extracting a structured approval rule comprising a trigger condition, an approval role and a process node sequence; matching a basic examination and approval template according to the form field and the user permission information, and dynamically generating an adaptive process comprising multiple stages of examination and approval nodes, aging parameters and an additional examination and approval link; and through resource scheduling optimization and time domain correlation analysis, establishing an optimized mapping relation based on flow execution characteristics such as approval timeliness deviation and node skipping frequency, and outputting a visual flow chart and execution parameters. A business logic writing mode is replaced by automatic analysis of an AI large model on an unstructured rule; the dynamic template matching and continuous iterative optimization mechanism can quickly respond to business changes, and resource scheduling optimization and automatic process generation shorten the implementation period and reduce the operation and maintenance cost.

The invention discloses a bridge structure health monitoring data anomaly detection method based on deep learning, particularly relates to the technical field of structure health monitoring, and is used for solving the problems of high environmental interference sensitivity and insufficient cross-modal data fusion capability caused by image enhancement and feature extraction process splitting in the existing method. A cross-domain feature mapping relation is generated through combined training of dynamic image enhancement and a deep learning model, and collaborative optimization of enhancement parameters and feature space is achieved; time-frequency resonance parameters of visual images and acoustic emission signals are fused based on cross-modal convolution, and damage feature space distribution is corrected in combination with an attention mechanism; analyzing and quantifying the structural difference of the cross-domain features by using topology persistence coherence, and iteratively optimizing the feature mapping network through an optimal transmission theory; and finally, a multi-level feature template matching and self-adaptive threshold judgment mechanism is adopted to output an abnormal detection result, so that the robustness and generalization ability of bridge structure health detection in a complex environment are remarkably improved.

The invention discloses an industrial machine vision recognition and analysis system, relates to the technical field of machine vision recognition, and is used to solve the problem of poor industrial machine vision recognition. The invention combines total variation denoising, contrast-limited adaptive histogram equalization, random geometric transformation and other technologies to enhance image diversity, improve the system's generalization ability for images in different environments, extract image texture and edge features, provide high-quality data for subsequent analysis, use unsupervised learning to extract image potential features and patterns, and locate abnormal areas through template matching, so as to adaptively discover defects in the production process, dynamically update the model based on a small amount of labeled data, optimize feature representation in real time, and adapt to environmental changes. Finally, semi-supervised learning and pseudo-label generation technology are used to expand the training set, improve the generalization ability of the model, maintain efficient learning when labeled data is limited, reduce manual labeling costs, and improve the accuracy of automated quality inspection.

The invention belongs to the technical field of image processing and robots, and particularly relates to an image processing method and device and a robot, and the method comprises the steps: obtaining a structured light coding image, a polarization imaging image and a binocular depth image; constructing a multi-dimensional feature vector by fusing decoded contour phase, polarization analysis material characteristics and depth image data; dynamically calibrating the multi-dimensional feature vector according to the ambient light and the pose of the robot, correcting color deviation through an adaptive white balance algorithm, compensating a camera installation error, and obtaining a calibrated descriptor; based on the calibrated descriptor, utilizing an improved Hough transformation and template matching algorithm to identify the object category attitude, and calculating six-degree-of-freedom pose parameters through a triangulation method; and according to the six-degree-of-freedom pose parameters, a collision-free grabbing path is generated in combination with a robot kinematics model and obstacle information. Therefore, the problems of insufficient image analysis capability, single compensation mechanism, lack of path optimization capability and the like in the prior art are solved.

The invention discloses a multi-sensor fusion-based position and posture estimation method for a working device of a mining robot, which realizes accurate position and posture estimation by installing a body posture sensor and an environment sensing sensor and combining a kinematic model, visual inertia state estimation and a nonlinear least square optimization algorithm. The method comprises the following steps: firstly, carrying out preliminary prediction on a kinematic model through feedback data of an encoder and an inclinometer, and constructing a local visual inertia estimator by utilizing a camera and an MEMS to correct a prediction result; and then, a combined constraint modeling and optimization method is adopted to improve the precision of the pose of each working mechanism. Besides, geometric features of the bucket are extracted through a semantic segmentation network and a template matching technology, six-degree-of-freedom pose estimation of the bucket is completed, finally data fusion of a body pose sensor and an environment perception sensor is performed through a factor graph optimization algorithm, and comprehensive pose estimation of the whole working device is achieved. The working pose of the mining robot can be monitored in real time, the operation precision is improved, human errors are reduced, the working efficiency and safety can be optimized through data analysis, and more effective support is provided for path planning of a working device of the mining robot.

The invention discloses a three-dimensional point cloud registration method for image pre-registration and multi-criterion mismatching suppression, which relates to the technical field of point cloud registration, and comprises the following steps of: firstly, enhancing surface texture information of a to-be-measured object by using a projection speckle image to perform data acquisition, and then positioning an optimal registration area of the to-be-registered image by using an image partitioning and template matching algorithm, image pre-registration is realized by performing feature point detection and registration on the region, and adverse effects caused by the non-overlapping region of the to-be-registered image are inhibited; thirdly, according to the coordinate transformation relation, obtaining corresponding three-dimensional matching point pairs through the two-dimensional matching point pairs obtained through image pre-registration, and completing point cloud coarse registration; and finally, adopting a classic ICP algorithm to realize point cloud precise registration. In order to improve the precision and efficiency of registration, multiple error matching suppression strategies are integrated in the stages of image pre-registration and point cloud coarse registration. According to the method, high-efficiency and high-precision registration of the point clouds which are limited in overlapping rate and lack of features can be realized.

The invention relates to the technical field of machine vision, in particular to an intelligent machining positioning method and system based on machine vision, and the method comprises the steps: collecting and preprocessing original visual image data, and extracting geometric structure features and surface morphological features from the preprocessed visual image data, thereby comprehensively mining the characteristics of a workpiece; when the machining positioning offset vector is determined, the geometric structure features are matched with the preset machining reference template, and the workpiece machining position deviation can be preliminarily determined; on the basis of surface morphological characteristics, compensation adjustment is carried out on a machining positioning offset vector to generate an optimized positioning vector, the complex condition of the workpiece surface is considered, positioning deviation possibly generated according to a geometric structure purely is corrected, and positioning is more accurate; a machining path adjusting instruction is generated according to the optimized positioning vector and transmitted to a machining control unit, machining equipment can be effectively guided to adjust the machining path according to the actual state of the workpiece, it is ensured that the machining process is accurate and efficient, and the workpiece machining quality and production efficiency are improved.

The invention discloses a document element extraction method based on an AI large model technology. The method comprises the following steps that various receipts input by a user are received; carrying out analysis and layout analysis on the document through a visual language model or a text analysis technology to generate a unified intermediate representation; a dynamic grading module is adopted to evaluate document complexity from three dimensions of format, structure and variation, and the document complexity is divided into a simple regular type, a structure complex type or a height variation type; adaptively selecting a processing flow according to a rating result, wherein the processing flow comprises high-speed template matching, multi-modal cognitive fusion or intelligent agent driving processing; the processing result is converted into an EasyEX format which is easy to process; the method comprises the following steps of: converting a natural language demand into an execution rule by extracting an Agent and utilizing a Prompt dynamic compiling technology, and positioning and extracting a target field by LLM (Logical Language Model); and finally, outputting structured data after type verification, knowledge graph verification and compliance review. According to the invention, the universality, the accuracy and the automation degree of receipt processing are improved, and the labor cost is greatly reduced.

The invention discloses a textile production defect detection method and system based on image processing, and belongs to the field of textile defect detection. According to the method, the point cloud data is generated by collecting the surface deformation stripes, and the penetrability reflection signal containing the sound wave propagation time is obtained through the multi-frequency ultrasonic probe. Denoising the point cloud data and then positioning a surface deformation candidate area based on a curvature gradient and a normal vector deflection angle; and an internal abnormal region is determined by separating and extracting a reflection wave amplitude value and a time delay parameter through an ultrasonic frequency band. And matching the surface point cloud coordinates with the ultrasonic propagation time by adopting an iterative nearest point algorithm to establish feature association between the normal vector deflection angle and the reflected wave amplitude. And dynamically distributing fusion weights for the surface features and the internal features according to the curvature gradient change rate and the amplitude attenuation rate to generate a multi-modal defect distribution diagram. And geometric shape and reflection intensity parameters are extracted and matched with a preset defect template to realize type and position judgment, and the method can be suitable for production defect detection of the thick textile with a multi-layer composite material structure.

Disclosed herein are a method, an apparatus and a storage medium for image encoding / decoding. In typical image encoding / decoding methods, a decoder-side motion information derivation method may be limitedly used. Therefore, the improvement of encoding efficiency attributable to the decoder-side motion information derivation method may also be limited. In embodiments, a motion information search method used in an inter-prediction mode, an intra block copy mode and an intra template matching prediction mode is disclosed. With the use of various motion search methods, encoding efficiency may be improved.

The invention discloses a fixed dual-runway FOD detection method based on multi-source circumferential scanning, and belongs to the technical field of data fusion detection, and the method specifically comprises the steps: synchronously obtaining dual-runway multi-polarization echo data through a fixed circumferential scanning rada; performing time-varying clutter suppression by combining an adaptive clutter cancellation method matched with a dynamic background template and a runway material feature library, and generating a time-frequency domain three-dimensional feature matrix through time-frequency analysis; synchronously collecting and preprocessing image data, and marking a target area through a constant false alarm rate algorithm and image detection; a multi-source data geographic coordinate mapping model is established, data registration and cross validation are completed under a runway physical coordinate system, and real-time tracking of a real FOD target is realized based on radar and image fusion data through multi-dimensional confirmation of radar polarization / micro-motion features and image visual features; according to the method, the FOD detection accuracy and the position identification precision are improved through multi-source data fusion, the false alarm rate is effectively reduced, and a guarantee is provided for safe operation of an airport.

The invention discloses an automobile body welding seam detection method and system based on machine vision, and particularly relates to the technical field of welding seam detection. The method comprises the following steps: capturing a welding seam image in real time through a high-definition camera, positioning a welding seam by using a template matching algorithm, calculating the width of the welding seam by using a dynamic window scanning technology, identifying a complex form area, extracting curvature change and surface roughness characteristics of the welding seam, and evaluating the adaptability of the algorithm to the complex form; classification processing is carried out according to the adaptability level, and the system automatically judges that a high-adaptability-level area is qualified; generating early warning and storing data in the low-adaptation-level region; the accuracy of a general adaptation level area is improved through a dynamic optimization algorithm, the detection precision is effectively improved, reworking and missing detection are reduced, and the adaptability of the system to various welding seam forms is improved.

The invention relates to the technical field of intelligent monitoring, and discloses an intelligent archival repository monitoring system based on Internet of Things, comprising: a plurality of edge sensing units deployed in a repository, monitoring parameter drift in real time and triggering a local perturbation event by dynamically learning a personalized stable state baseline of environmental parameters; cooperative judgment is achieved among the units through perturbation wake-up signals, concerned events are reported to a central platform only when preset conditions are met, and full-spectrum perception of slowly changing and sudden risks is formed through baseline learning and a cooperative wake-up mechanism of the edge units in combination with instantaneous event template matching and targeted enhanced monitoring; based on the dynamic health fingerprint analysis of the perturbation resonance characteristic spectrum, the stability degradation trend of the archive microenvironment can be pre-warned in a prospective manner, and the intelligent level and the preventive protection capability of the monitoring system are remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an image generation method, device and equipment based on theme information and a medium. The method comprises the steps that input information is analyzed to generate theme information and copywriting information, a cue word set is generated, and a figure image set and a background image set are generated; fitting the segmented figure image with the background image to form a head image candidate set, and selecting a head image matching template frame to generate a basic image; decomposing the copywriting to generate a sub-module initial picture set, and adding a gradual change effect to form a sub-module picture set; the adjusted sub-module pictures are obtained based on size adjustment, and a pre-synthesized image is generated through splicing; and identifying the blank area to draw a title text to obtain a final image. Through theme analysis, template matching, image splicing, modular copywriting processing and blank drawing, poster generation efficiency is improved, layout flexibility is enhanced, and visual unification is realized.

The invention provides a nuclear power safety report data extraction method and system based on multi-modal feature fusion, and belongs to the technical field of data processing, and the method comprises the steps: obtaining a nuclear power safety report document; carrying out OCR (Optical Character Recognition) and paragraph segmentation on the report document to obtain first text data; performing text semantic understanding on the first text data by adopting a dynamic template matching and semantic driving extraction mechanism to obtain a text feature vector; segmenting pages of the report document and identifying fonts and fonts to generate first format data; adopting a cross-page table reconstruction algorithm to detect table cells of the first format data and splicing a cross-page table to obtain format feature vectors; performing feature alignment on the text feature vector and the format feature vector, and inputting the text feature vector and the format feature vector into a large pre-training language model to obtain a semantic vector; and fusing the text feature vector, the format feature vector and the semantic vector to form a composite expression unit, and intelligently extracting structured data based on the composite expression unit. The semantic entity and structural relationship in the nuclear power report can be effectively identified.

The invention discloses an unstructured file identification method based on primitive identification, and the method comprises the following steps: (1) preprocessing: cutting and zooming an electrical wiring drawing to a required size, and carrying out the gray processing; (2) primitive recognition: constructing a feature extraction network of an electrical primitive detection model by adopting a YOLO electrical primitive detection algorithm fused with an attention mechanism; (3) character recognition, which comprises the following steps: removing primitives, detecting an annotated text area, constructing a text candidate box, segmenting and adjusting an annotated text boundary, recognizing annotated text content, and filtering an electrical text recognition result; and (4) electrical element association relationship analysis, which comprises the following steps: frame element extraction, primitive group region division, template matching and electrical primitive-annotation text association. According to the method, the primitives and characters of the electrical wiring diagram can be obtained, the problem that different fonts and styles appear in marked characters can be solved, and semantic information can be understood.

The embodiment of the invention provides a batch image data processing method for an intelligent manufacturing production line. The batch image data processing method comprises the steps that a first to-be-recognized image in multiple to-be-recognized images contained in a current detection batch is acquired; performing template matching recognition based on a first sliding window and a first moving step length on the first to-be-recognized image to obtain a defect detection result of the first to-be-recognized image; under the condition that the defect detection result is that the defect exists, determining a position area of the defect, and performing corresponding area range calibration based on the position area; and for the remaining images to be recognized in the current detection batch, performing matching recognition of the first sliding window of the region where the region range calibration is located and a first template of the first moving step length, and performing matching recognition of a second sliding window of the region except the region range calibration and a second template of a second moving step length, and obtaining a corresponding defect detection result. According to the scheme, the image processing efficiency and effect are improved.

The invention discloses a concave-convex point detection and defect evaluation system based on multistage detection and feature change, and the system comprises an image collection unit which is used for capturing a product image in real time; the image preprocessing unit is used for carrying out noise reduction processing on the image and improving the brightness and the contrast ratio of the image; the primary product defect positioning module is used for selecting Gaussian difference extraction, dynamic threshold extraction, fixed threshold extraction, template matching and a deep learning algorithm module to extract and position suspicious defects according to the size of the product and the characteristic form of the defects; the secondary defect classification and judgment module is used for carrying out preliminary classification and type judgment on the defects in the region; the third-stage defect contour extraction unit is used for performing fine contour extraction on each detected defect area after the defect classification is clear; and the four-stage defect feature analysis and evaluation unit is used for deeply extracting key features of each defect and carrying out scientific and quantitative evaluation on the key features.

Disclosed herein are a method, an apparatus and a storage medium for image encoding / decoding. Methods for performing more accurate prediction are disclosed when performing prediction using a block vector. A block vector for a target block is derived by template matching, and a prediction block for the target block is determined based on the derived block vector. Various and detailed embodiments for a plurality of pieces of information used to derive a block vector, such as a search area, a block vector candidate list, a reference region, a buffer, and a tree type, are provided.

The invention relates to a packaging defect detection method and system, and the method comprises the steps: S1, obtaining the size information of packaging bags and the interval information of adjacent packaging bags through an area-array camera and a photoelectric sensor in combination with a multi-scale template matching method; s2, delay time of a conveying belt is calculated, and it is ensured that the distance between adjacent packaging bags is larger than a set value; s3, triggering a line scanning camera to shoot an image of the packaging bag, and collecting the vibration frequency; s31, performing motion compensation correction on the image by adopting an improved optical flow method; s32, inputting the corrected image into a forward propagation network based on an improved OfficientViT model, wherein the model comprises a multi-scale linear attention module and an SPP-ELan module; s33, the images are classified, if the classification result is that the bags are not packaged, the unpackaged bags are grabbed through a grabbing robot, and if it is determined that the bags are not packaged based on a classification confidence coefficient threshold value, the unpackaged bags are removed; and S34, inputting the defect image into an improved ACGAN network for data enhancement, wherein the network comprises a CMFNet denoising module based on a U-Net architecture. The method and the system can monitor and identify the packaging defects on the production line in real time, improve the accuracy and the efficiency of inspection, and reduce the labor cost.

The invention relates to the field of image recognition, and provides a positive and negative sample algorithm combined power transmission line high-altitude anomaly detection method. The objective of the invention is to improve the accuracy and robustness of detection. By comprehensively utilizing the advantages of the positive sample algorithm and the negative sample algorithm, the method effectively solves the problems of background complexity and abnormal data collection. Firstly, a detection picture and a base picture are cut and masked and focused on a power transmission line area; next, a positive sample algorithm is used to identify an abnormal region, while a negative sample algorithm is focused on detecting a specific type of anomaly. Through a multi-stage filtering mechanism including target detection filtering and template matching filtering, false alarms are effectively reduced, and the detection effect is improved. According to the method, a memseg algorithm and a YOLO model are adopted in model training, and an anomaly filtering model is introduced to detect common misinformation targets such as birds and vegetation. Finally, by combining positive and negative sample results, high-precision and high-recall-rate detection performance is realized.

The invention discloses an engineering drawing label automatic identification method and system based on template matching. According to the method, an existing drawing is utilized to make a drawing label template library, and a drawing label position detection model, a drawing label classification model and a drawing label text recognition model are trained respectively; for the target drawing, using the drawing label position detection model to position the coordinate position of the drawing label, and cutting to obtain a to-be-identified drawing label; determining a picture label template style to which a to-be-recognized picture label belongs by using the picture label classification model, and acquiring text content and position coordinates of a textbox in the to-be-recognized picture label by using the picture label text recognition model; and acquiring an attribute value of the corresponding textbox in the to-be-identified signature by utilizing the position information and the structural relationship between the template signature attribute and the attribute value textbox and the drop point position of the central point of the attribute value textbox in the to-be-identified signature, and realizing the correspondence between the attribute and the attribute value. According to the invention, accurate identification of the drawing label is realized in a more intelligent and efficient manner, and manpower and time costs are reduced while the precision is ensured.

The invention discloses an AI-based archive digitization full-process management and control method, which comprises the following steps: acquiring archive image data and text data, constructing an original archive data set, and generating an initial directory set; constructing an archive directory template set, executing template matching and structure adaptation, and generating an initial structure recognition result; setting a classification rule of directory deviation and structural contradiction, constructing a deviation rule base, executing rule scanning and node comparison, and generating a deviation contradiction label sequence; constructing an evolution state vector, inputting the evolution state vector into an improved GMN model, and generating a structure repair state vector through a structure guiding unit, a deviation sensing unit, a path fusion unit and a node evolution unit in sequence; and executing result updating and conflict rearrangement, completing standard template alignment and semantic coverage verification, and generating a final structure recognition result. According to the method, the structural integrity, the semantic consistency and the automatic repair capability of directory recognition are improved, and the method has relatively high adaptability and intelligent level.

The invention relates to the technical field of defect detection, and particularly discloses a railway fastener defect detection method and system, and the method comprises the steps: controlling a high-speed industrial camera to scan a track region so as to collect a track image, carrying out the image preprocessing of the track image, extracting an ROI image block containing a potential railway fastener based on template matching, and then carrying out the detection of the potential railway fastener. The method further introduces a target detection algorithm to perform fastener accurate positioning and segmentation on the ROI image blocks to obtain finer fastener ROI image blocks, further extracts visual features of the fastener ROI image blocks, and performs feature space distribution saliency enhancement on the visual features to enhance expression effects of the visual features such as surface color, texture and shape of the fastener, so as to improve the image quality of the fastener. Therefore, intelligent identification of railway fastener defects is realized on the basis. According to the method, the limitation of a traditional detection method can be effectively overcome, the reliability of railway fastener defect detection in a complex environment is improved, and the method has relatively high universality and adaptability.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. In the method, for a conversion between a current video block of a video and a bitstream of the video, a process is applied to the current video block based on template matching. At least one reference sample of a current template of the current video block is determined based on a block vector (BV) of the current video block during the process. The conversion is performed based on the applying.

The invention relates to the field of network live broadcast, and discloses a multi-person identity recognition method and device in a live broadcast stream, equipment and a medium, and the method comprises the steps: determining a data distance between each face image in the live broadcast stream of a live broadcast room and a plurality of face templates corresponding to a plurality of anchor persons in a preset live broadcast person library corresponding to the live broadcast room, obtaining a distance matrix; the optimal matching between the face images and the face templates is solved according to the distance matrix, and the identity label of the identity of the anchor person to which the uniquely matched face template of each face image belongs is determined through the optimal solution; and responding to an operation instruction acting on the identity of the target anchor person according to the position information of each face image in the video frame and the mapping relation data between the identity label of the face template matched with the face image. According to the method, the anchor person identity is determined by searching the optimal matching, so that the accuracy of multi-person identity recognition in the live broadcast stream and the user experience are remarkably improved.

The invention provides an automobile part visual inspection correction method and equipment. The method comprises the following steps: extracting a part contour through edge detection, calculating an inclination angle by adopting Hough transform, executing affine transform on an original image, enabling a rotation angle to be negative to the inclination angle, and ensuring that the parts are horizontally aligned; in the corrected horizontal image, calculating a transverse offset through a feature matching algorithm or template matching, and translating an image area to enable the center of the detection window to be aligned with the actual position of the part; if inclination and offset are detected at the same time, inclination correction is executed preferentially, the offset is calculated based on the image after inclination correction, synchronous correction is achieved through a composite transformation algorithm, and accumulative errors are reduced through iterative optimization; and extracting a size parameter from the corrected image, comparing the size parameter with a predetermined tolerance range, and if the measured size is outside the predetermined tolerance range, marking the image as a defective product, and removing the defective product. The method is used for upgrading the visual inspection system for automobile manufacturing.

A warehouse goods intelligent identification and positioning management system based on an image segmentation algorithm comprises an image acquisition and preprocessing module, an image segmentation algorithm module, a goods positioning and feedback module, an inventory management module, a data communication and integration module and a user interaction and management module. The image segmentation algorithm module is used for segmenting and extracting image information. The cargo identification and positioning module is used for identifying and positioning cargoes. The position feedback and alarm module is used for feeding back cargo positions and giving an alarm. The inventory management module is used for managing and maintaining cargo information. The data communication and integration module is used for transmitting information in real time. According to the warehouse goods intelligent identification and positioning management system based on the image segmentation algorithm, the warehouse image segmentation algorithm based on deep learning is put forward to classify goods, and the goods identification and positioning algorithm based on improved label template matching is put forward to intelligently position the goods.

The invention discloses a secondary circuit intelligent modeling method and system based on a visual language model, and belongs to the technical field of power system automation, and the method comprises the steps: recognizing and extracting primitives and symbols in a secondary circuit of a transformer substation, and constructing a primitive library; acquiring corresponding first visual language data in the primitive library through a primitive recognition and classification module; performing further deep learning identification on the first visual language data to obtain second visual language data; and carrying out secondary electric loop modeling through the second visual language data and carrying out visual display. The method breaks through a traditional method based on rule and template matching; unified management of models and effective integration of data are achieved, the problems that information is isolated and data are difficult to share are solved, a foundation is laid for development of a digital asset management system and establishment of an integrated application interface, the automation degree of engineering drawing recognition and modeling is improved, and the requirements of a modern electric power system for efficient and accurate automatic modeling are met.