485 results about "Boundary detection" patented technology

The invention belongs to the technical field of artificial intelligence, and relates to an artificial intelligence-based electric energy quality disturbance identification and positioning method, which comprises the steps of constructing an electric energy quality disturbance signal data set, performing segmented preprocessing on electric energy quality disturbance voltage data, enhancing time-frequency joint features and encoding disturbance sensitive areas. And constructing a deep learning model for power quality disturbance identification and positioning, and identifying and positioning the power quality disturbance. According to the invention, through adaptive denoising processing, boundary detection and multi-resolution time-frequency feature extraction, the identification precision and positioning precision of power quality disturbance are significantly improved; self-adaptive wavelet denoising and dynamic segmentation are combined, noise interference is effectively suppressed, and the edge characteristics of voltage sudden change points are kept; according to the dual-task sharing network, disturbance identification and positioning tasks are cooperatively optimized, so that the network can consider disturbance classification and time positioning at the same time; and through Bayesian reasoning, the system can output confidence estimation, provides credibility quantification of identification and positioning results, and effectively improves the reliability of the system.

The invention belongs to the technical field of document content extraction, and particularly discloses a document content extraction method and system based on multimodal model collaboration, a terminal and a medium. Comprising the following steps: identifying the type of an input to-be-processed document, and judging the document type; on the basis of the type identification result, calling a multi-modal model to analyze the document content, and outputting space coordinates, visual features and semantic features of document elements; generating a content sequence according with a reading habit through a semantic sequence reconstruction algorithm; paragraph boundary detection, paragraph recombination and semantic association modeling of charts and texts are completed based on the multilayer attention network and the graph neural network; grammar error correction, format optimization and title hierarchy generation are carried out by using a large language model and a hierarchical classification network; and converting the identification result into a structured output file. According to the method, the processing requirements of different types of documents can be considered, and high-precision analysis and efficient output are realized under the scenes of complex layouts, multiple languages and formula tables.

The invention relates to the technical field of industrial production intelligent process control, in particular to an AI-based electrolytic manganese process production quality intelligent prediction method and system, and the system comprises a drift calibration module, a structure matching module, a process parameter inversion module, a path correction module and a deviation verification module. According to the method, dynamic compensation of deviation between input parameter drift and output parameters is achieved through the response relation between input and output of all links, the actual influence degree of fluctuation of all the parameters on the target control parameters and product quality is effectively recognized according to correlation matching of the periodic change of the production process and the fluctuation frequency of the target control parameters, and the accuracy of the product quality is improved. Potential interference of key process parameters on stability of each process is locked in time, diffusion starting time and space of impurities are clearly traced through path judgment of residual liquid concentration trend and raw material adding position, and migration path parameters are further adjusted based on ion retention behaviors of boundary detection points.

The invention provides a rail turnout foreign matter detection and type analysis method and system, and the method comprises the steps: arranging a first camera group at the front end of a train, recognizing the position of a turnout through a preset target detection model, and calibrating foreign matters in the turnout through a preset foreign matter detection model; shooting an image calibrated to the foreign matter in the turnout through a second camera group as a to-be-analyzed image; extracting a second boundary region rolled by a wheel on the foreign matter from the to-be-analyzed image through a preset wheel rolling track detection model, performing coarse classification on the foreign matter in the to-be-analyzed image to obtain a coarse classification result, and selecting a boundary detection strategy to identify the first boundary region according to the coarse classification result in the foreign matter image; and respectively extracting corresponding texture features in the first boundary region and the second boundary region through a deep learning network, performing one-to-one mapping on the extracted texture features to obtain a mapping relation group, performing analysis through the mapping relation group to obtain a feature change rate, and performing analysis through a multi-feature analysis strategy to obtain a specific foreign matter type.

The invention relates to a distributed trajectory planning method and system for an unmanned aerial vehicle cluster in an obstacle environment. The method comprises the steps of initializing a system, constructing a local Euclidean symbol distance site map, and realizing real-time interaction of state information of the unmanned aerial vehicle. An initial collision-free path is generated using jump point search. And solving an optimal control point through an L-BFGS algorithm through multi-constraint trajectory optimization in combination with a load swing dynamics model, four-rotor dynamics limitation, cluster collision avoidance and environment obstacle avoidance requirements. And a dynamic time redistribution strategy is adopted, and the track time interval is adjusted according to speed and acceleration overrun conditions. The method further comprises an adaptive re-planning mechanism, and local target points are updated in real time and adjacent aircraft collaborative optimization is triggered based on local map boundary detection and quadrotor track safety detection. According to the method, efficient, safe and stable trajectory planning of the hanging load quad-rotor unmanned aerial vehicle cluster in a complex environment is realized, the requirement of autonomously and efficiently completing tasks is met, and the task execution efficiency and safety are improved.

The invention provides a farmland plot extraction method based on a multi-type double-branch convolutional neural network, and the method comprises the steps: creating a double-branch multi-task network DBMNet based on a multi-type convolution module, and designing a double-branch feature extraction module DBFE to replace a convolution layer of an original network, a dynamic linear constraint convolution module LCDC and a multi-scale deformable convolution feature extraction module MDFE are used to comprehensively extract edge features of an image and field block region features of different sizes and shapes, the perception ability of a network to information of different scales is improved, and thus the segmentation precision of the network is improved. The network uses boundary detection and distance regression tasks to improve the precision extraction of the farmland plot masks, so that the network has a better generalization effect. According to the method, the multi-type convolutional network is applied to the farmland plot extraction task, the experimental result shows that compared with other most advanced algorithms, the method has the best segmentation performance, discriminative features of the farmland plot can be effectively extracted, the segmentation precision is remarkably improved, the farmland plot is accurately extracted, and the method has great application potential and prospect.

The invention belongs to the technical field of remote sensing image intelligent processing and agricultural information extraction, and particularly relates to a remote sensing large model pre-training and multi-granularity boundary supervision natural field type extraction method, which comprises the following steps: firstly, pre-training a model on a large-scale space-time spectrum remote sensing data set, and combining anchor point sensing mask and geographic information coding; secondly, inputting the multi-scale features into a multi-branch structure sensing network, and outputting a semantic segmentation prediction map through high and low resolution double input and dynamic attention fusion; generating a multi-scale field boundary label through morphological operation, and outputting a boundary prediction map through multi-task supervision after domain enhancement of features by a frequency space double-domain enhancement module; and finally, aligning the two images and performing pixel-level operation to obtain a high-precision extraction result. Through the method, the global classification error during cross-region migration is greatly reduced, the method is adaptive to a low-pixel wide ridge, the boundary detection value and the closure rate of a small-scale field are improved, and the extraction precision of a large field and a small field is considered.

Systems and methods for imaging, such as with an intravascular ultrasound (IVUS) imaging device are employed with components and accessories, such as for example, catheters, a catheter interface module, mounts, a workstation, a tablet, a display, and cart. The components may be controlled or interacted with via hands-free commands, voice control, gestures, and may be enhanced with artificial intelligence and / or machine learning. Real time measurements and border detection with enhanced imaging are provided.

The invention discloses an image edge cutting method and device based on boundary detection, equipment and a storage medium, and relates to the technical field of image boundary detection, and the method comprises the steps: carrying out the preprocessing of an original image, and obtaining a target image; determining a scene type of the target image; based on an edge detection strategy corresponding to the scene type, performing edge detection on the target image to obtain an edge image; performing straight line detection on the edge image to obtain a plurality of candidate straight line segments; and based on a boundary positioning rule, determining a target boundary line segment from the plurality of candidate straight line segments, so as to carry out edge cutting on the original image according to the target boundary line segment. According to the invention, a more reliable image edge cutting effect can be generated.

The invention discloses a sign language translation model, a sign language translation system and a sign language translation method based on full-modal alignment. The sign language translation method comprises the steps of extracting multi-modal features of hand, face and body postures from an input video and performing preliminary fusion; deep secondary fusion and alignment are carried out through multi-scale time sequence coding and a cross-modal collaborative attention mechanism, and a spatial-temporal feature sequence of full-modal alignment is generated; performing boundary detection and dynamic segmentation on the feature sequence by using a CTC-based sequence prediction model, and outputting a discrete sign language word sequence with a timestamp; and finally, capturing a sign language grammar structure of the sequence through a graph structure enhanced Transform encoder, inputting the sign language grammar structure into a Transform decoder integrating grammar consistency loss, and generating a target text conforming to target natural language grammar and semantic rules. According to the method, the problems of continuous sign language action adhesion and grammar structure difference are effectively solved, and the sign language translation accuracy and the natural language generation fluency are greatly improved.

The invention discloses a complex environment lane line accurate detection method based on multi-scale cooperative enhancement and semantic compensation. The method comprises the following steps: obtaining a lane line data set, and inputting the lane line data set into a trained lane line accurate detection network; adopting a backbone network to obtain multi-level features; utilizing a multi-scale collaborative enhancement module to obtain features of spatial and semantic information interaction enhancement; a mixed scene semantic compensation module is utilized to obtain supplementary semantic features so as to enhance feature robustness in different scenes; a boundary correction guiding module is adopted, features are refined from space and channel dimensions, and the lane boundary detection effect is improved; and optimizing and adjusting the lane line anchor layer by layer through the enhanced features to obtain a final lane line coordinate prediction sequence. According to the method, multi-scale cooperative enhancement and semantic compensation are combined, the lane line detection precision is improved, and the detection robustness in a complex scene is enhanced.

The invention discloses a pulmonary nodule boundary detection method based on semantic segmentation. The method comprises the following steps: generating an edge prior image set; obtaining a multi-scale boundary alignment feature map set and a deformation offset parameter set; a post-gating deformation offset parameter set is obtained, the multi-scale boundary alignment feature image set is updated, cross-scale feature fusion is executed, and a fusion feature image set is generated; establishing a node set and an edge set by utilizing node features in the fusion feature graph set and a spatial adjacency relationship, and initializing a node feature vector set; forming a fusion feature map set enhanced by multi-hop reasoning; and outputting a pulmonary nodule region mask prediction image set and a pulmonary nodule boundary probability prediction image set. According to the method, fracture between pseudo boundaries and slices is effectively inhibited, so that a three-dimensional structure prediction result is more coherent, and high-readability and high-consistency boundary segmentation output is provided for a clinical CAD (Computer Aided Design) system.

The invention relates to the technical field of voice signal processing, and discloses a voice segmentation intelligent editing system based on deep learning. The system comprises a voice feature extraction module, a segmentation boundary detection module, a semantic content analysis module, an editing strategy generation module and a real-time quality evaluation module. The voice feature extraction module collects multi-dimensional voice features and timestamp information, and verifies feature integrity and timeliness; the segmentation boundary detection module identifies voice pause intervals and semantic turning nodes and divides segmentation units and boundary types; a semantic content analysis module extracts text content and emotion features of each segment, and analyzes semantic topic relevance and information density; an editing strategy generation module formulates a segmentation retention rule and a sequence adjustment scheme, and matches user preferences and scene demands; the real-time quality evaluation module monitors voice fluency and information integrity in the editing process and analyzes splicing errors and user feedback. According to the system, intelligent processing of the whole voice editing process is realized.

In various examples, object boundary detection for autonomous and semi-autonomous systems and applications is described. Systems and methods are disclosed that may use one or more machine learning models to process sensor data generated using a machine to generate one or more outputs indicating boundaries of objects surrounding the machine. In some examples, the output(s) may include confidence values (e.g., scores) associated with locations of an environment at least partially surrounding the machine, where the confidence values indicate whether an object boundary is located at the locations. In some examples, the output(s) (and / or an output from post-processing of the confidence values) may include an obstacle map indicating at least the boundaries of the objects. Additionally, in order for the machine learning model(s) to generate such outputs, systems and methods are further disclosed that train the machine learning model(s) using various types of ground truth data.

The invention provides an anti-collision method, device and equipment based on obstacle classification in a medical environment, a medium and a program product, and relates to the field of intelligent anti-collision. The anti-collision method comprises the following steps: acquiring point cloud data of a target body in a medical environment; carrying out clustering reconstruction processing on the point cloud data of the first dynamic obstacle in the target body to obtain a reconstructed cluster, and carrying out space division on the cluster to generate a high-dynamic convex boundary; performing recursive space division on the point cloud data of the second dynamic obstacle and the static obstacle in the target body to generate a low dynamic convex boundary, and performing recursive space division on the point cloud data of the to-be-controlled anti-collision body to generate a target convex boundary; and expanding the boundary of the target convex boundary based on the safety distance, detecting the position relationship between the expanded boundary and the low-dynamic convex boundary and the position relationship between the expanded boundary and the high-dynamic convex boundary, generating a control instruction according to the position relationship, and controlling the movement of the to-be-controlled anti-collision body.

The invention discloses a remote sensing image semantic segmentation method and equipment based on an enhanced MambaVision architecture. The method comprises the following steps: performing initial feature extraction on a to-be-segmented high-resolution remote sensing image through a patch embedding module; the initial features are input into a MambaVision backbone network to extract multi-scale features, the backbone network comprises four layering stages, the stage 1 and the stage 2 adopt adaptive expansion convolution blocks to balance high and low frequency features, and the stage 3 and the stage 4 adopt two-dimensional selective scanning and a multi-head attention mechanism to capture global dependence; performing fusion and up-sampling on the multi-scale features through an MFMS decoder, and optimizing texture and boundary detection; the decoder comprises an MFMS attention module and a channel mixer; and performing multi-scale convolution fusion on the output of the MFMS attention module based on the channel mixer to generate a segmentation result. According to the method, through two-dimensional selective scanning, adaptive expansion convolution and a multi-frequency multi-scale MFMS decoder, the segmentation precision and efficiency are improved, and the method is suitable for high-resolution and large-size remote sensing images.

The invention belongs to the technical field of three-dimensional point cloud processing, and particularly discloses an automatic tooth segmentation method and system for oral cavity scanning point clouds, and the method comprises the following steps: collecting and preprocessing dentition three-dimensional point clouds, extracting point cloud features containing coordinates and normal directions, and constructing a neighborhood structure; setting a first-stage multi-branch network, and performing tooth and gingiva coarse segmentation and instance initial clustering on the point cloud; setting a second-stage semantic refinement network, and carrying out local cutting on the original point cloud; setting a boundary detection and boundary offset network to enhance a tooth contact area, and refining a boundary instance by clustering to obtain a boundary enhanced instance tag; and performing label fusion, and propagating the fused label to the original high-resolution point cloud through a nearest neighbor algorithm to obtain a final semantic label and a final instance label. By adopting the technical scheme, high-precision instance-level segmentation of the complete dentition point cloud is realized through semantic segmentation, boundary detection and clustering cooperative work based on a biased field.

The invention discloses an ultrasonic image denoising method based on variational mode decomposition and local space sparse fusion, which comprises the following steps of: firstly, adaptively optimizing key parameters of variational mode decomposition by using a grey wolf optimization algorithm to realize stable and efficient decomposition of an ultrasonic image; then, classifying the modal components according to the structural features of the modal components, and implementing differentiated denoising strategies for different types of modals to separate noise and reserve useful information; after modal reconstruction, a sparse expression method based on local space information is further introduced, according to the method, accurate boundary detection is carried out through gradient vector flow and gray scale proportion analysis, self-adaptive partitioning is carried out on an image according to boundary information, and finally sparse reconstruction is carried out through double dictionaries trained for different areas. According to the method, speckle noise in the ultrasonic image can be effectively suppressed, and meanwhile, the capability of keeping the edge and detail information of a tissue structure is remarkably improved, so that the ultrasonic image with higher quality is obtained.

The invention relates to the technical field of pupil boundary detection, in particular to a multi-algorithm fusion pupil boundary detection method and system, and the method comprises the following steps: obtaining an eye image through an infrared camera, converting the eye image into a grey-scale map, optimizing the grey-scale map, carrying out the median filtering noise reduction, maintaining the edge information, and determining the pupil boundary through a Sobel operator and Hough transform. And the pupil center displacement is analyzed, the motion consistency is evaluated, the pupil position is corrected, and the pupil positioning accuracy is optimized. According to the method, the image preprocessing process is optimized by applying infrared camera shooting and image processing algorithms, accurate detection, median filtering and local contrast enhancement of pupil boundaries are achieved through multi-algorithm fusion, and particularly noise and edge information in the images are optimized; according to the method, the edge detection precision and the image definition are effectively improved, the pupil boundary is accurately mapped through combined use of the Sobel operator and Hough transform, and the misjudgment rate is greatly reduced.

Coarse resolution satellite data has limited information content of land captured in the images and small area field boundary detection is technically challenging. Embodiments of the present disclosure provide a method and system for detection of field boundaries using coarse resolution satellite data and GIS. Multi-layer crop segmentation approach is used over RoI, wherein agricultural land related segments are derived from a soil layer, a temporal stack for a current season and a temporal change layer from the coarse resolution satellite data of the RoI. Segmentation based on different aspects captures various details at each segmentation layer. The segments are then combined, which aggregates all information related to the land to accurately detect the field boundaries in the RoI. The field boundaries are further refined using GIS topology operations. Baseline cadastral maps are updated using information from maps generated using refined field boundaries identified in real time for each season.

The invention discloses a relation enhancement type named entity recognition method and device, a medium and a program product, and relates to the field of data processing. The method comprises the following steps: performing feature extraction on text data through a BERT model to obtain a plurality of tokens and semantic feature vector sequences; performing different-scale convolution operations by using a convolutional neural network to obtain local character combination features, and fusing the local character combination features with the semantic feature vector sequence to form a feature enhanced semantic feature vector sequence; the Transform model determines a long-distance dependency relationship of semantic feature vectors in the sequence by using an attention mechanism; generating a long entity structure model based on the long-distance dependency relationship, and then determining an enhanced semantic feature vector sequence; and performing tag sequence optimization processing on the named entity through a conditional random field layer, determining sequence labeling loss, boundary detection loss and relation prediction loss, determining total loss, and adjusting a tag sequence so as to determine the named entity. According to the method, the accuracy and integrity of named entity recognition can be effectively improved.

The invention discloses an autonomous exploration method and system for a mobile robot in an unknown environment, and the method comprises the steps: firstly scanning the environment in real time to obtain the positioning and point location information of the robot, and updating a 3D grid map represented by a three-dimensional space; secondly, boundary detection and candidate viewpoint sampling are carried out on a map, and viewpoint weights are calculated to screen extension nodes; carrying out path planning by adopting a Hybrid A * algorithm, and maintaining a path into a topological map; path tracking is carried out by adopting a pure path tracking method based on a ray distance, the speed is adjusted in combination with a PID algorithm, and the accuracy and real-time performance of path tracking are improved. According to the method, the completeness of expanded viewpoint selection is improved, repeated exploration of the same area is reduced, the path tracking precision is improved, and the technical problem of autonomous exploration of the robot in a large-scale unknown environment is effectively solved.

The invention discloses a field generalization semantic segmentation enhancement method based on an efficient multi-scale and simple attention module, and belongs to the field of computer vision. According to the invention, the model is based on a TQDM (Text Query-Driven Mask Transfer) framework, and an EMA (Efficient Multi-Scale Attention Module) and a SimAM (Simple Parameter-free Attention Module) are fused, so that the adaptability and the robustness of a semantic segmentation task on a plurality of domains are improved. According to the invention, by introducing the EMA module, multi-scale feature aggregation and cross-space information interaction are realized, so that the understanding ability of the model to a complex scene is enhanced; meanwhile, in combination with a SimAM module, feature expression is optimized under the condition of not increasing extra parameters, and the precision of small target segmentation and target boundary detection is improved. The method can be widely applied to computer vision tasks related to cross-domain semantic segmentation, such as automatic driving, intelligent monitoring and medical image analysis.

The invention generally relates to medical imaging systems that instantly and / or automatically detect borders. Embodiments of the invention provide an imaging system that automatically detects a border at a location within a vessel in response only to navigational input moving the image to that location. In some embodiments, systems and methods of the invention operate such that when a doctor moves an imaging catheter to a new location with in tissue, the system essentially instantly finds, and optionally displays, the border(s), calculates an occlusion, or both.

The invention discloses a safety fence protection method and device based on the Internet of Things technology, and belongs to the technical field of safety alarm and Internet of Things application, and the method comprises the following steps: respectively collecting the distance, heat source distribution and movement track information of a target protection area through an ultrasonic sensor, an infrared sensor and a millimeter wave radar; generating a virtual fence model with an initial alarm boundary by adopting a confidence weighted fusion algorithm based on environmental condition dynamic adjustment; environmental change parameters such as obstacle distribution and meteorological conditions are monitored in real time, when the change exceeds a preset threshold value, a dynamic boundary adjustment strategy is triggered, and the fence alarm boundary shape is optimized through a boundary node position redistribution mechanism; a distributed computing framework is adopted to decompose boundary detection tasks, and the tasks are dynamically allocated according to the real-time resource utilization rate of edge computing nodes. According to the invention, the problems of insufficient real-time performance, limited dynamic adjustment capability and low system integration degree in a complex environment in the prior art can be effectively solved.

The invention provides a road boundary detection method and a false target identification method and system, and the method comprises the steps: detecting the operation information of a vehicle in real time, and updating the historically accumulated point cloud information based on the motion information of the vehicle; performing screening processing on the point cloud information based on a preset screening rule to obtain final point cloud information; performing rasterized indexing on the final point cloud information based on a preset rasterized indexing rule to obtain a preliminary road boundary point; and based on the self-vehicle motion information and the final point cloud information, carrying out smoothing processing on the initial road boundary point to obtain a final road boundary. According to the method provided by the invention, the technical problem that the road edge identification technology cannot accurately identify the road boundary in special-shaped road boundaries or irregular road scenes, such as forks, roundabout, small roads in villages and the like, is effectively solved.

The invention discloses a hybrid PDF (Portable Document Format) document analysis and knowledge fragment construction method and device, and belongs to the technical field of digital document intelligent processing. The method comprises the following steps: firstly, carrying out layout element identification and semantic classification on a PDF page through a deep learning model, and calculating the confidence of the PDF page; when the confidence coefficient is lower than or equal to a threshold value, a correction module based on vertical and horizontal projection analysis is automatically switched to carry out boundary detection and result error correction; the fused layout result is converted into structured JSON data, and the structured JSON data is further mapped into a Markdown format with a reserved title level; and finally, knowledge fragment segmentation is carried out based on a Markdown hierarchical relationship, so that each fragment carries a complete title context path. According to the method, the problems of semantic deficiency, insufficient stability and context splitting in the prior art are effectively solved, and the robustness and accuracy of complex format PDF and low-quality scanning copy analysis and the interpretability of downstream knowledge retrieval are remarkably improved.

The invention relates to the technical field of material pile boundary detection, and particularly discloses an automatic material pile boundary detection system and method for an unattended bucket-wheel stacker-reclaimer. Radar equipment is used for scanning a target material pile to obtain point cloud data of the target material pile, and after coordinate system transformation and filtering processing are carried out on the point cloud data, the target material pile boundary is detected; carrying out local window-based geometric feature extraction on the preprocessed point cloud data by adopting a deep learning algorithm so as to mine local shape information of the target material pile, and carrying out core information-based aggregation analysis on geometric features of the target material pile under each local window; the global shape features and boundary information of the target material pile are captured, and automatic detection of the boundary of the target material pile is achieved through the semantic segmentation technology. By means of the mode, the defects of a traditional radar data processing mode can be overcome, accurate detection of material pile boundary information is achieved, and powerful support is provided for automatic operation of the bucket-wheel stacker reclaimer.

The invention discloses an industrial product surface defect detection system and method based on a segmentation cutting model, and belongs to the technical field of detection. According to the method, all segmentation models are successfully applied to an industrial defect detection task through a double-adapter strategy, a scale perception feature attention sensor is designed, and the sensitivity of defect detection is enhanced by fusing a Transform-based multi-scale feature and a mixed attention mechanism; meanwhile, an efficient decoder comprising an adaptive feature enhancement module is constructed, the boundary detection capability is optimized through local convolution and cross-layer compensation, the defect detection method is remarkably superior to a traditional defect detection method, and the robustness of the model to small defects and complex backgrounds is improved. The invention provides an efficient and accurate solution for industrial defect detection, and has important engineering application value.

The invention provides a multi-task learning optimized corpus annotation automation system, and aims to solve the problems of low efficiency and poor accuracy of a traditional corpus annotation method. The system comprises a shared encoder, a multi-task decoder, a dynamic adaptive module and an online incremental learning module. The shared encoder adopts a Transform architecture to carry out word segmentation, sliding window segmentation and time embedding processing on an input corpus to generate global semantic representation; the multi-task decoder completes tasks such as event boundary detection, time sequence reconstruction and causal relationship extraction in parallel; the dynamic adaptive module adjusts the loss weight in real time according to the consistency score output by each task, and enhances the adaptive ability of the model; and the online incremental learning module realizes continuous optimization of the model through expert auditing and model parameter updating. According to the system, through multi-task learning and self-adaptive optimization, the efficiency and accuracy of corpus tagging are remarkably improved, and the system is suitable for multiple fields such as news reports, social media analysis and legal document processing.