320 results about "Conditional random field" patented technology

The invention provides a virtual DPU power plant simulation fault restoration method and system based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: carrying out the preprocessing of collected DPU power plant operation data, including noise reduction, time sequence alignment and abnormal point elimination, carrying out the data quality evaluation, training a fault feature mapping model based on the processed data, and carrying out the fault restoration of the DPU power plant. The model is used for recognizing abnormal clusters in real time, a fault evolution path is searched and determined in combination with a conditional random field and a Monte Carlo tree, an optimal path is determined through a particle filtering algorithm, fault root causes are determined in combination with causal analysis, spectral clustering and a Bayesian network, and a fault diagnosis report is generated.

The invention relates to a multi-modal named entity recognition method based on cross-modal guide interactive fusion, which comprises the following steps: constructing a data set, designing a cross-modal contrast aggregation mechanism, respectively extracting image features and text features, and constructing a contrast learning mechanism to screen out image features with high association degree with text semantics for dynamic aggregation; a DINO model is introduced to extract image features, a dynamic similarity matching method is constructed, a dynamic similarity matching weight is generated based on text features and an image feature correlation matrix, a dynamic gating mechanism is utilized to adaptively select image features related to text feature context, and a cross-modal fusion and guide interaction strategy is constructed. Outputting an enhanced semantic representation vector, and mapping the semantic representation vector after multi-modal fusion into a final entity tag sequence by adopting a conditional random field decoder to complete entity recognition; the method has the advantage of remarkably improving the robustness and accuracy of multi-modal entity recognition.

The invention relates to the technical field of text data analysis, in particular to an electronic medical record free text analysis method, system and device, which improve the efficiency of information extraction and reduce the demand of manual intervention. The method comprises the steps of receiving free text data of the electronic medical record, performing cleaning, word segmentation and medical term standardization processing, and converting an unstructured text into structured data; based on deep learning and a medical knowledge base, text features are extracted through a pre-training language model, entity boundaries are captured on an output layer in combination with a conditional random field, medical entities in a text are recognized, and the recognized entities are classified and labeled; extracting a causal relationship, a treatment relationship and an examination relationship among entities through a dependency syntactic analysis and semantic role labeling technology, and constructing an entity association network; performing dynamic correction and supplementation on entity classification and relationships in combination with medical record context information; and outputting an analysis result in a structured JSON format to generate a computable semantic map.

The invention provides a tunnel crack identification method and system based on multi-source image processing, and relates to the technical field of tunnel engineering, and the method comprises the steps: obtaining multi-source data of a horizontal rock stratum tunnel; performing spatial registration on the multi-source data to generate a multi-modal image under the same reference system; extracting multi-modal features according to the multi-modal image, and constructing a multi-source feature map under the same space grid; performing crack initial detection on the multi-source feature map in combination with multi-scale filtering and structure tensor analysis to obtain a crack candidate region mask; accurate crack identification is carried out through the crack candidate area mask, a crack identification result is obtained by combining bedding direction constraint and a microconditional random field, and the crack identification result comprises a crack segmentation map and a crack category. The method solves the problem that existing crack identification does not consider the bedding characteristics of the horizontal rock stratum tunnel.

The invention relates to the technical field of remote sensing data processing and glacier area calculation, in particular to an unmanned aerial vehicle and satellite remote sensing data fused glacier area calculation method, which comprises the following steps of: cooperatively acquiring a satellite multispectral image and unmanned aerial vehicle high-resolution optical and LiDAR data, performing time synchronization, high-precision space registration and data enhancement processing, and calculating the glacier area through the unmanned aerial vehicle and satellite remote sensing data fusion. A satellite image glacier macroscopic feature and an initial mask are extracted by using a convolutional neural network and an NDSI / NDWI algorithm, and unmanned aerial vehicle image microscopic texture, edge and topographic features are acquired through a local binary pattern, edge detection and LiDAR point cloud; based on pyramid layering and a conditional random field, adopting a variance weighting algorithm to realize multi-scale feature level fusion; and after segmentation through an Otsu algorithm, calculating the area through a pixel counting method and introducing gradient correction, and evaluating the reliability through three types of precision. The method breaks through the limitation of a single data source, fuses macroscopic and microscopic features, improves the boundary positioning precision and calculation efficiency, and is suitable for glacier dynamic monitoring in a complex environment.

The invention discloses a mobile terminal streetscape image real-time segmentation method based on a lightweight neural network, and relates to the technical field of image segmentation. The method comprises the following steps: firstly, carrying out 320 * 320 adjustment, Z-score standardization, adaptive histogram equalization and 3 * 3 Gaussian filtering preprocessing on an input streetscape image; then, an improved MobileNetV3 backbone network is used, and a five-scale feature map is output in combination with DropBlock regularization through eight feature extraction stages including depth separable convolution and an SE attention module; multi-scale features are fused through a U-shaped structure, and a fusion feature map is generated through up-sampling, element-by-element addition of dimension reduction low-layer features and an attention gating module; and during reasoning, outputting a segmentation mask by using a convolutional layer, Softmax and a conditional random field, and finally performing knowledge distillation, weight pruning, 8-bit quantization and TensorRT optimization. According to the invention, high-precision real-time street view segmentation is realized, the robustness is high, and the method is suitable for different devices and scenes.

The invention discloses a self-adaptive dynamic SLAM method based on Gaussian distribution, and relates to the technical field of dynamic environments. According to the method, the dynamic environment positioning precision is improved, the system can accurately recognize and eliminate interference of dynamic objects through a dynamic target elimination strategy based on CRF in combination with YOLO detection and conditional random field optimization, and a traditional SLAM system is likely to be affected by moving objects in the dynamic environment to generate pose offset, so that the system is not easy to operate. Reprojection error evaluation and feature point weighting processing are introduced, differential modeling is carried out on static points and dynamic points, the dependence of pose estimation on static features is strengthened, estimation errors caused by dynamic interference are effectively reduced, the positioning precision and stability of the system in a complex dynamic scene are improved, and the positioning accuracy of the system in the complex dynamic scene is improved. Moreover, the consistency and integrity of the map are enhanced, and the mapping efficiency and scene adaptability are improved.

The invention relates to the technical field of artificial intelligence, in particular to a business contract intelligent review and risk quantification method and device, and the method comprises the steps: building and maintaining a business contract key term information base; obtaining and preprocessing a to-be-rechecked contract text; processing the text based on a bidirectional long-short term memory network and a conditional random field model, and extracting semantic representation; identifying key information through a multi-level attention mechanism; executing multi-label learning to classify and identify clause types and attributes; utilizing a dependency syntactic analysis technology to extract logical association and a responsibility chain among terms, and constructing a knowledge graph; identifying risk terms and generating risk prompts; business indexes are extracted, and risk open values are calculated; generating a rechecking report; the corresponding device comprises nine functional modules such as an information base management module, a text preprocessing module and a semantic representation extraction module, risk terms in a contract can be automatically recognized, a quantitative risk assessment result is provided, and contract auditing efficiency and accuracy are effectively improved.

The invention discloses a knowledge graph proposition error correction method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the feature extraction of knowledge graph data, the knowledge graph data comprises text data and structure data, and obtaining multi-modal feature data; constructing a local semantic verification model based on a graph attention network, performing local semantic consistency detection on the multi-modal feature data, and outputting a local semantic verification result; constructing a global semantic verification model based on a knowledge graph embedding technology, performing global semantic consistency detection on the multi-modal feature data, and outputting a global semantic verification result; and inputting the local semantic verification result and the global semantic verification result into a conditional random field model to generate a semantic error correction result of the knowledge graph. According to the method, the accuracy and availability of the knowledge graph are remarkably improved, reliable technical support is provided for maintenance and optimization of the knowledge graph, and the method has important practical value.

The invention relates to an ancient character image recognition and semantic analysis method, which comprises the following steps of: firstly, acquiring an original image containing irregular deformation and material diversity characteristics from the surface of a cultural relic, and eliminating noise and illumination interference through adaptive filtering; calculating a main inclination angle based on stroke feature distribution, and executing inclination angle correction to obtain a processed image; thirdly, separating independent characters by adopting a region growing algorithm based on stroke features, and extracting feature vectors by utilizing a deep convolutional network for identification; and finally, matching context information in combination with an ancient text corpus, and optimizing sequence labeling by adopting a conditional random field algorithm to obtain semantic output. And if the result is not ideal, the inclination angle correction parameter is adjusted through backtracking and iterative optimization is carried out, so that the overall identification accuracy is improved. According to the method, the problems of irregular deformation and material diversity in the ancient character image can be effectively solved, and the recognition precision and the semantic analysis reliability are improved.

The invention relates to the technical field of remote sensing image processing, and discloses a remote sensing image adaptive identification method and system for territorial space planning, and the method comprises the steps: obtaining a multi-source remote sensing image data set of a research region, feature extraction, cloud detection, quality evaluation and adaptive preprocessing are carried out; carrying out prototype network coding, calculating a category prototype and probability, and supporting fine tuning of a set; carrying out multi-scale cavity convolution and category scale attention fusion; evaluating the adaptability score of the comprehensive fusion feature map set, and carrying out weighted fusion, classification and normalization; change detection is carried out, stable and change regions are segmented, and time sequence context features are extracted and constrained optimization is carried out; entropy is fused, a boundary is decided, uncertainty is estimated, and weighted fusion is carried out according to a change area; conditional random field optimization, confidence level grading and connected domain identification are carried out; the automation level, the adaptive capacity and the recognition reliability of remote sensing monitoring of territorial space planning are improved.

The invention discloses a legal text information extraction and enhancement method and system based on artificial intelligence. The method comprises the steps of data acquisition and processing, semantic segmentation and reconstruction, text logic perception, dynamic knowledge fusion and information extraction and enhancement. The invention relates to the technical field of text information extraction, in particular to a legal text information extraction enhancement method and system based on artificial intelligence, which adopts a joint frame of semantic segmentation reconstruction, text logic perception and dynamic knowledge fusion to realize more accurate recognition of the structural level of a legal text. Understanding of logic association of legal clauses is enhanced, and meanwhile, knowledge among different legal documents is dynamically fused, so that cross reference and intelligent application among different laws and regulations are facilitated; semantic segmentation reconstruction is carried out by adopting a conditional random field bidirectional long-short-term memory model improved by combining hierarchical label reasoning; and carrying out text logic perception by adopting a causal graph convolutional reasoning neural network enhanced by combining a dual-channel logic unit.

The invention relates to a tunnel lining leakage infrared-millimeter wave fusion detection method and system, and belongs to the technical field of civil engineering detection, and the method comprises the steps: obtaining high-density millimeter wave radar data and thermal infrared imager data; performing data preprocessing to realize space alignment and time synchronization; topological features are extracted, manifold learning mapping is carried out on the preprocessed millimeter wave radar data, and a topological signature matrix is constructed; multi-phase spectrum analysis is carried out, nonlinear phase space reconstruction and multi-scale entropy feature analysis are carried out on the preprocessed infrared thermal imager data, and an entropy feature map is obtained; probability fusion and progressive reasoning are executed, and a leakage area probability graph is generated through a conditional random field model and a progressive belief propagation algorithm; the method overcomes the limitation of a single sensor detection method, achieves the high-precision detection and quantitative evaluation of the leakage of the tunnel lining, and has the technical effects of high detection accuracy, high environmental adaptability, high automation degree and the like.

The invention relates to the technical field of agricultural information, in particular to a tobacco agriculture standard named entity recognition method and system based on a hybrid neural network, and the method comprises the steps: carrying out the embedded representation of an input tobacco agriculture standard text through a BERT pre-training language model, and generating a word vector sequence containing global semantic information; performing local feature extraction on the word vector sequence by using an iterative expansion convolutional neural network to obtain a local feature vector; splicing the global semantic information and the local feature vectors, and inputting the spliced global semantic information and local feature vectors into a bidirectional long-short-term memory network for context feature extraction to generate context enhancement features; weight optimization is carried out on the context enhancement features through a multi-head attention mechanism, and key semantic features are highlighted; and performing label prediction on the optimized feature sequence by adopting a conditional random field decoder, and outputting a standard article element entity identification result. According to the method, high-precision and high-robustness named entity recognition is realized, and the method is particularly suitable for complex semantic and low-resource field scenes.

The invention provides a method and a system for detecting surface defects of a flange sealing element. The method comprises the following steps: expanding an annular surface image into a rectangular image; constructing a defect segmentation network to process the rectangular image to obtain a preliminary defect mask; the defect segmentation network adopts an encoder-decoder structure, a parallel multi-scale feature extraction module is arranged at the tail end of an encoder, and the parallel multi-scale feature extraction module comprises a plurality of parallel convolution branches with different receptive fields; training the defect segmentation network by using a composite loss function, and performing post-processing on the initial defect mask by using a full-connection conditional random field to obtain an optimized defect mask; and inversely transforming the optimized defect mask to a Cartesian coordinate system, and identifying and marking the position and contour of the defect on the original annular surface image.

The invention discloses a Chinese named entity recognition system, method and equipment based on multi-scale features and a medium, and the method comprises the steps: decomposing an original text into a character sequence through a preprocessing module, and preprocessing characters, including removing punctuation marks and uniformly converting the characters into lower letters; a RoBERTa-WWM sub-module of a feature extraction module is responsible for converting an input text into a high-dimensional feature vector, a CNN sub-module effectively extracts local features through a sliding window mechanism, and a BiLSTM sub-module finally utilizes the advantage of bidirectional processing to obtain complete context information; decoding the hidden state representation by using a dynamic conditional random field (CRF) through a sequence tagging module to determine an optimal tag sequence; according to the method, pre-training, multi-scale feature fusion and dynamic decoding technologies are integrated, and efficient and accurate Chinese entity recognition is realized; through a unique preprocessing rule, a modular architecture design and optimized hyper-parameter configuration, the performance and robustness of the system in a complex scene are ensured.

The invention discloses a field large model geological survey report generation method based on a knowledge graph, and the method comprises the following steps: S1, building an engineering survey field data set through multi-source heterogeneous data collection and structured preprocessing, and the engineering survey field data set comprises five text dimension tags divided according to engineering survey specifications; s2, constructing an engineering investigation knowledge graph; and S2A, knowledge extraction, wherein a bidirectional encoder presentation layer-bidirectional long short-term memory network-conditional random field joint extraction model is adopted. According to the method, a bidirectional encoder presentation layer-bidirectional long short-term memory network-conditional random field joint extraction model is improved on knowledge modeling to perform high-precision entity-relation joint extraction, a geological knowledge map with consistent semantics and clear structure is constructed based on RDF, rule reasoning and graph neural network reasoning mechanisms are fused, and the method has the advantages of high-precision entity-relation joint extraction and high-precision entity-relation joint extraction. Deep mining and complementation of explicit and implicit knowledge are realized, and the ability of the prior art in knowledge expression granularity and reasoning breadth is improved.

The invention provides a method and equipment for constructing a text entity recognition model based on large model data enhancement. The method comprises the following steps: firstly, constructing an initial model comprising a preprocessing unit, a syntax dependency analysis unit, a data correction enhancement unit, a context coding unit, a syntax enhancement unit, an expression fusion unit and a sequence decoding processing unit; preprocessing the training sample to obtain a preprocessed text, and establishing a preliminary dependency graph through syntactic analysis; correcting and enhancing the text and the dependency graph by the large language model to obtain a text sequence and a dependency graph for subsequent use; encoding the text sequence to obtain an initial representation vector, and enhancing the vector in combination with the dependency graph; after fusion, decoding and outputting a prediction label containing a lexical entity label; and calculating loss by using a function containing conditional random field structure loss, judging convergence, and if not, updating parameters and continuing training until a target model is obtained. According to the method, data are optimized and expanded by means of a large language model, syntactic dependency enhancement features are combined, and the recognition capability of the complex text named entities is improved.

The invention provides a financial account distribution and anomaly detection method and system based on machine learning, and relates to the technical field of machine learning, and the method comprises the steps: building a causal graph model, generating a time sequence attention weight matrix through a conditional random field, carrying out the weighted fusion, and carrying out the attribution of a difference reason; and processing the reconciliation difference feature vector by adopting a reinforcement learning method, generating and optimizing a processing strategy, and finally outputting a difference processing scheme. The account checking accuracy can be improved, abnormal transactions can be automatically found, the manual processing cost is reduced, and financial risks are effectively prevented.

The invention relates to the technical field of machine vision, in particular to an automobile detection system and method based on machine vision, and the system comprises an image processing module, an automobile body feature extraction module, a defect positioning module, a defect analysis module, a decision support module and a detection evaluation module. According to the method, the U-Net model is combined with the conditional random field, various vehicle body defects such as scratches and recesses can be finely recognized and accurately positioned, the recognition capability of different defect types is enhanced by extracting complex geometric and textural features from the image, the defect classification is more accurate, and the accuracy of vehicle body defect classification is improved. The defect boundary is continuously optimized, the detection parameters are automatically adjusted, the reliability of decision support is improved, the repair process is more targeted and efficient, human errors are reduced through automatic detection and parameter adjustment, the operation cost is reduced, and potential maintenance cost and risks caused by defect omission are reduced.

The invention relates to a heterogeneous remote sensing image change detection system and method based on a Mama model. The system comprises a feature extractor, a codec network and a change detector. The feature extractor is used for extracting first feature maps of an optical mode and an SAR mode respectively; the codec network is used for mapping and reconstructing the first feature map based on a Mama model to generate a second feature map of an optical mode and an SAR mode; the change detector subtracts the second feature maps of the same mode and calculates an L2 norm along the channel dimension, and weighted fusion is carried out on calculation results of the L2 norms of different modes, so that a difference map is obtained; the difference map processor optimizes the difference map based on a full-connection conditional random field method, and segments the optimized difference map into a varying region and a non-varying region based on an optimal threshold selected by an adaptive threshold segmentation algorithm. According to the method, a feature alignment process and difference chart generation are coupled into an end-to-end optimization task, and the problem of error accumulation caused by two-stage decoupling in a traditional unsupervised method is avoided.

The invention discloses a multi-level cross-modal alignment method based on comparative learning, which is used for improving the accuracy and efficiency of multi-modal sentiment analysis. According to the method, a RoBERTa model and a Vision Transform model are used for coding a text and an image respectively, and text representation and image representation are obtained. The global cross-modal alignment module aligns the representation of the text and the representation of the image by adopting a comparative learning technology to enhance the consistency between the text and the representation of the image. Furthermore, through a local cross-modal alignment module, a cross-attention mechanism is used to perform fine-grained alignment on the text and image representations to identify smaller, more specific semantic units in the associated image and text. According to the method, a multi-task learning framework is adopted to integrate cross-modal information from texts and images, sequence tag prediction is carried out through a conditional random field, and terms and emotions in the aspects are recognized and classified. Experimental results show that the performance of the method on a Twitter-2015 data set and a Twitter-2017 data set is superior to that of an existing single-mode model and an existing multi-mode model, and the performance of multi-mode sentiment analysis is effectively improved.

The invention provides a large-scale three-dimensional tunnel surrounding rock parameter random field model construction method and system, and relates to the technical field of tunnel surrounding rock modeling, and the method comprises the steps: carrying out the fitting construction of a probability distribution model reflecting surrounding rock parameters; selecting a plurality of surrounding rock parameters as clustering features, quantitatively classifying global geologic structures in tunnel surrounding rocks, and performing global decomposition on a tunnel model into continuous sub-domains containing overlapped buffer areas; for a first sub-domain from any direction of the whole domain of the tunnel model, performing covariance matrix spectral decomposition by adopting a KL decomposition method to generate a first sub-domain random field, and extracting random field values of overlapped regions among the sub-domains as boundary condition data of a recursive generation process; on the basis of the boundary condition data, conditional random fields of subsequent sub-domains are generated layer by layer; and seamlessly splicing the conditional random fields generated by recursion of the sub-fields into a global parameter random field, converting the global parameter random field into probability distribution by adopting a probability mapping method, and performing instruction encapsulation by developing a cross-platform data interface engine to realize a construction process of a parameter random field model.

The invention relates to the technical field of intelligent breeding, and discloses a beef cattle identification method based on multi-scale segmentation optimization and multi-modal data fusion, which comprises the following steps: acquiring beef cattle multi-modal image data, carrying out time-space synchronization and fusion to construct a fusion image, carrying out preprocessing of illumination invariant transformation and multi-scale pyramid construction, and carrying out multi-scale segmentation optimization and multi-modal data fusion on the fusion image; generating an enhanced image, inputting the enhanced image into a depth-guided attention segmentation network to extract double-branch foreground features, generating an initial segmentation probability graph, optimizing a segmentation mask in combination with a conditional random field model and a motion consistency constraint, finally extracting multi-dimensional features from the mask, inputting the multi-dimensional features into a multi-classification support vector machine to perform individual classification reasoning, and obtaining a final segmentation result. And obtaining a beef cattle individual identification result. Therefore, the foreground and background segmentation method applied to the intelligent breeding scene for beef cattle individual recognition is provided, the beef cattle individual recognition precision is improved, and meanwhile the management requirements of commercial breeding for high precision, high stability and multi-scene adaptability are met.

The invention relates to the technical field of intelligent questioning and answering workers, in particular to a power grid equipment intelligent questioning and answering optimization method and system based on a large language model. The method comprises the following steps: acquiring problem data of power grid equipment, and creating a dynamically updated power grid equipment vocabulary to store the problem data; a part-of-speech tagging model of a conditional random field is used for tagging part-of-speech of question data, power grid equipment knowledge retrieval and large language model building are performed based on the question data of tagging and semantic roles, parameter updating is performed on the built large language model, and answer optimization is performed according to a processing result of the large language model. According to the method, a position coding formula combined with semantic information is introduced, so that a large language model can better understand the relationship between a word order and semantics in a text in the field of power grid equipment, semantic logic in the text can be more deeply mastered, the ability of understanding the operation process and the equipment relationship is improved, and answers more conforming to professional logic are output.

The invention belongs to the technical field of image processing, and particularly relates to a crack image segmentation method and system. Gaussian noise is introduced into image data for enhancement processing, so that the robustness of the model to noise is improved; an encoder composed of three layers of progressively stacked residual units is adopted to extract crack features, and a DropBlock regularization module layer is matched to reduce feature redundancy and enhance feature diversity; the image spatial resolution of the crack features is recovered through a decoder symmetrical to the encoder, and the fine structure of the crack boundary is restored; and finally, optimizing the crack probability graph by adopting a dense conditional random field, and outputting a final crack segmentation graph. According to the method, the problem of foreground-background category imbalance is effectively relieved, the phenomena of false detection and missing detection are reduced, the segmentation performance of the model is improved, the method is particularly suitable for deployment of edge equipment such as unmanned aerial vehicles and mobile phones, and a crack recognition scheme with high precision, low complexity and high robustness is provided for infrastructure health monitoring.

The invention discloses a video behavior recognition method and system based on a neural network, and relates to the technical field of computer vision. According to the method, video, audio and acceleration data are efficiently integrated through the multi-modal fusion network, and the fusion weight is dynamically adjusted, so that the problems of heterogeneity and interference of the multi-modal data are solved, and the robustness and accuracy of a behavior recognition system are improved; meanwhile, a lightweight convolutional network and a dynamic time warping algorithm are adopted to optimize calculation efficiency and real-time performance, and industrial real-time monitoring requirements are met; besides, starting and ending time points of abnormal behaviors are accurately positioned by using time sequence analysis and a conditional random field model, and model parameters are dynamically adjusted, so that self-adaptive optimization is realized, and the operation safety of workers and the production efficiency are guaranteed.

The invention relates to the cross technical field of artificial intelligence and medical image processing, in particular to a medical image segmentation system and method based on a wavelet bridge diffusion model and an efficient conditional random field. A WBDM-ECRF framework is constructed and comprises a discrete wavelet transform module, a BDM-T module, a BDM-S module and an ECRF module; decomposing the image through discrete wavelet transform, extracting a low-frequency sub-band, and enhancing the contrast ratio of a focus and normal tissues; the BDM-T takes U-Net as a backbone, integrates a FlashAttention mechanism, and optimizes a variance formula to realize efficient training; the BDM-S adopts a leapfrog sampling strategy, so that the reasoning time is greatly shortened; the ECRF introduces a multivariate potential function of a structural similarity index and smooth operation through edge expansion, and accurately optimizes edge segmentation. According to the method, the dependence of marked data is reduced, the training and reasoning efficiency is improved, the problem of fuzzy edge segmentation is solved, the Dice coefficient and intersection-union ratio performance on the ISIC data set is excellent, and reliable quantitative support is provided for disease diagnosis and treatment.

The invention discloses a time-of-flight magnetic resonance blood vessel image cerebral vessel segmentation method, system and device and a medium, and relates to the field of medical image processing, and the method comprises the steps: obtaining a to-be-processed time-of-flight magnetic resonance blood vessel image; reinforcing blood vessel features in the to-be-processed time-of-flight magnetic resonance blood vessel image to obtain a preprocessed image; according to the preprocessed image, performing cerebrovascular segmentation by adopting a few-sample segmentation model to obtain a blood vessel probability graph; the few-sample segmentation model is obtained by migrating knowledge of a pre-training video word segmentation device to train 3D U-Net; and performing post-processing on the blood vessel probability graph and the to-be-processed flight time magnetic resonance blood vessel image based on a human-computer interaction interface and a conditional random field to obtain a final segmented image. According to the method, a high-precision and high-robustness segmentation effect can be realized only by a small number of samples, and result optimization can be carried out through an efficient man-machine interaction mode.

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.