231 results about "Conditional random field" patented technology

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 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 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 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 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 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 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 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 relates to the technical field of medical image processing, and particularly discloses an intelligent segmentation system for a kidney tumor in a CT image, and the system comprises an image preprocessing module, an ROI automatic detection module, a kidney tumor segmentation module, a segmentation result post-processing module, a visual interaction module, and a model updating module. The system improves image quality through normalization and filtering, uses a target detection network to position a kidney area, realizes precise tumor segmentation based on an integrated channel attention mechanism and a U-Net network guided by structure priori, optimizes a boundary in combination with a conditional random field, adopts a multi-plane fusion strategy to improve three-dimensional consistency, and calculates tumor volume. The system supports federal learning update, improves the cross-mechanism generalization ability, has the advantages of high segmentation precision, strong boundary reducibility, good structural rationality, strong clinical deployment and the like, and is suitable for kidney tumor auxiliary diagnosis and quantitative analysis.

The invention relates to a seismic liquefaction assessment method based on conditional random field simulation, which comprises the following steps: firstly, obtaining a logarithmic normal distribution random field of a target area under a corresponding SPT-N value, then resampling through a Bootstrap method, constructing a weighted prior probability density function of the target area in combination with a likelihood function, and finally calculating the seismic liquefaction of the target area according to a Bayesian theory. A Markov chain Monte Carlo sampling method is combined, through posterior probability density distribution, an optimal horizontal direction correlation distance is determined, a covariance matrix is constructed to generate a conditional random field, and then through multiple times of simulation, the conditional random field is converged; and finally, aiming at the target area, through calculation of a cyclic stress ratio and a cyclic resistance ratio, constructing a liquefaction probability distribution diagram corresponding to the target area. According to the method, a conditional random field simulation method is inferred and improved by combining Bootstrap and Bayesian theories, the precision and reliability of geological parameter simulation are remarkably improved, and reliable data support is provided for seismic liquefaction assessment of deep and uneven site engineering.

The invention provides an image video retrieval method based on a domain fine-tuning large language model, which comprises the following steps: performing fine-tuning on a pre-training model to obtain a fine-tuning pre-training model for intention classification and keyword extraction; performing dynamic iteration screening on an optimal prompt template through Monte Carlo tree search in combination with a hidden Markov model (HMM); performing noise filtering on the keyword list, and predicting category labels of the filtered keywords through a conditional random field model to obtain a keyword enhancement set; combining with the user intention to generate a query condition, and obtaining a candidate resource set; and according to the similarity between the user query text and the candidate resource set, and in combination with the optimal prompt template, obtaining the resource path with the highest matching score between the user query and the candidate resource, and obtaining the retrieved image or video, so that the identification deviation possibly occurring when a general model processes proper nouns and terminologies can be effectively solved, and the user experience is improved. And the retrieval accuracy and response speed are improved, so that the retrieval accuracy and professional adaptability are improved.

The invention relates to a colorectal cancer MRI image segmentation method and system based on multi-dimensional feature fusion, and relates to the technical field of medical image processing. The method comprises the following steps: firstly, collecting multi-source data of a colorectal cancer patient, preprocessing the data, and constructing a multi-scale feature extraction module to obtain feature data of an image; through a multi-dimensional feature fusion module, features of seven dimensions including texture, shape, gray scale, modal association, life habits, functional metabolism and tissue specificity are integrated, and feature screening and weight distribution are realized in combination with a space-channel attention mechanism; and finally, carrying out feature decoding through a U-Net network, and optimizing a segmentation boundary by adopting a full-connection conditional random field (CRF) of an adaptive potential function. According to the method, multi-modal data integration, multi-scale feature extraction, multi-dimensional feature fusion, attention mechanism enabling, U-Net network decoding and CRF post-processing are combined, so that the accuracy and robustness of colorectal cancer MRI image segmentation are improved.

The invention relates to the technical field of computer vision and image processing, in particular to a cross-day-and-night boundary target thermo-optic joint detection method, which comprises the following steps of: firstly, acquiring a visible light image and an infrared image and unifying the visible light image and the infrared image to an image plane reference system; extracting topological features and multi-scale energy features from the input, fusing the topological features and the multi-scale energy features to generate a feature map, and outputting a central heat map, target size regression and sub-pixel offset by adopting anchor-frame-free detection; implementing optimal transmission correction according to an evidence field obtained by normalization of a cross reconstruction residual field to obtain a correction heat map and an initial candidate; triggering a fixation area by using the shape correction heat map and the initial candidate, executing super-resolution and secondary detection in the area, and performing affine reprojection and primary detection fusion to form an updated heat map and a fusion candidate; and in combination with uncertainty and topological consistency, a final detection set is output by using a conditional random field and non-maximum suppression. The method is stable in low-contrast, small-target and strong-interference scenes.

The invention discloses an agent intention understanding method based on multi-modal information fusion, relates to the technical field of natural language processing, and solves the technical problem that multi-agent intention understanding cannot be realized in a dynamic environment. Voice, text and visual features are mapped to the same hidden space through maximum mean value difference minimization, distribution difference between modes is eliminated, joint features in the hidden space can capture cross-mode complementary information at the same time, and the richness and anti-noise capability of feature expression are enhanced. CRF (Conditional Random Field) is combined with a special dictionary in the scheduling field to carry out candidate set expansion on fuzzy keywords, and specific semantic variants in the field are captured. The optimal grammar of the scheduling instruction is analyzed and decomposed into a feature vector causal relationship pair, and components of an intention and logic dependence of the intention are clarified. A dynamic knowledge graph is constructed based on the causal relationship, probability dependence among parameters is quantized by using a Bayesian network, the causal relationship strength is dynamically adjusted, and environmental changes are adapted.

The invention relates to an artificial intelligence image segmentation processing method and device, equipment and a medium. The method comprises the following steps: acquiring pixel intensity distribution of an input image and extracting local features by using a convolutional neural network to generate a pixel intensity change feature map; on the basis of the feature map, boundary area probability distribution is calculated by using a conditional random field model, and a zigzag edge is smoothed by using a graph cut algorithm to generate a continuous segmentation boundary; splicing the continuous segmentation boundary and an input image, inputting the spliced continuous segmentation boundary and the input image into a Transform enhanced U-Net network, balancing edge alignment loss and region overlapping precision loss through a dynamic weighted loss function, and generating an optimized segmentation mask; and performing threshold binarization processing on the optimized segmentation mask, removing isolated noisy points in combination with morphological closed operation, and generating a segmentation image matched with the physical defect form. By adopting the method, the continuity of segmentation boundaries and the matching precision of defect forms can be improved, and the problems of edge sawteeth and noise interference in traditional segmentation are solved.

The invention relates to an access control strategy automatic generation system and method based on multi-task learning, and the method comprises the steps: carrying out the word segmentation, cleaning and embedded vector conversion of an original access control text through a data preprocessing module, and constructing a normative input format; the feature sharing layer module is used for extracting deep semantic features of a text through multi-layer bidirectional coding and an attention mechanism and providing unified representation for downstream tasks; the access control statement identification module is used for judging whether each sentence in the text is an access control statement or not and realizing automatic identification of strategy related contents; and the attribute extraction and annotation module is used for annotating words in the access control statements and extracting subject, object and operation access control attributes. A word coding layer and a sentence coding layer are shared, local and global attention mechanisms are combined, key information of a text is extracted, the semantic understanding ability is enhanced, and cooperative training of statement recognition and attribute extraction is achieved; a conditional random field CRF structure is used for sequence labeling, and the structural rationality of attribute labels is ensured.

The invention relates to the technical field of natural language processing, in particular to a foreign trade letter intelligent semantic error correction and business term optimization method and device, and the method comprises the steps: obtaining a target foreign trade letter, and constructing a target corpus; performing Chinese word segmentation and part-of-speech tagging on the target foreign trade letter; performing term optimization based on the word segmentation result and the knowledge graph; identifying the letter title by using a conditional random field model, and converting the letter title into structured data; a Bi-LSTM-CRF model is adopted to carry out risk point detection, including Bi-LSTM coding, feature engineering and Max-pooling technologies, a part-of-speech sequence is obtained through a softmax function and a Viterbi path, and sequence labeling is carried out to obtain a risk point detection result; and finally, performing Chinese error correction based on the word segmentation result after part-of-speech tagging and the knowledge graph. The recognition and correction accuracy of foreign trade terminologies is improved, and communication obstacles caused by nonstandard use of the terminologies are effectively reduced.

This invention discloses a method, system, and storage medium for constructing a knowledge graph in the civil aviation service field. The method includes: S1, using a BERT-BiLSTM-CRF algorithm model to extract entities and obtain interrelated entity vector sequences, feature vector sequences, and annotation sequences; S2, using a convolutional neural network model to extract sentence vectors and their contained entity vectors, and employing n filters to identify and extract a database of entity-relationship-entity triples; S3, using a conditional random field entity node integration model to integrate the annotation information and store it in the corresponding entities as entity attribute values; S4, using the triple database and the integrated entity attribute values ​​to link and fuse them to construct a civil aviation knowledge graph. This invention can obtain a comprehensive and accurate civil aviation knowledge graph of entity relationships based on a civil aviation knowledge text database. It can not only satisfy passenger knowledge question answering and querying needs but also serve as a training and educational resource, improving the overall service level.

The invention belongs to the field of tunnel engineering, and relates to a tunnel defect detection data spatial resolution enhancement method and device based on a Vine Copula dependent structure and a conditional random field, a computer readable medium and a computer program product. The method comprises the following steps: firstly, separating an overall trend and random fluctuation from original tunnel defect monitoring data; then establishing a Vine Copula multivariable dependence model to represent a statistical correlation relationship among the plurality of defect indexes; and then generating defect distribution data with high spatial resolution by using a conditional random field interpolation simulation technology in combination with a spatial autocorrelation analysis result. According to the invention, while the accuracy of the existing measuring point data is ensured, the spatial correlation structure and multivariable joint distribution characteristics of the defect data are maintained, and the precision of spatial interpolation prediction is improved.

The invention discloses a rock slope multi-mode instability probability analysis and risk evaluation method based on a drilling constraint condition random field, and belongs to the technical field of geotechnical engineering and engineering geology. According to the method, the conditional random field which is strictly consistent with the actually measured data is constructed under the condition of limited drilling exploration data, so that the reasonable expression of the spatial heterogeneity of the rock mass mechanical parameters is realized, and the authenticity of a numerical model and the reliability of an analysis result are greatly improved; the defect of insufficient result credibility caused by lack of actual measurement constraint of the unconditional random field is effectively overcome; through a large amount of multi-sample numerical simulation, multiple instability modes possibly occurring on the rock slope under different parameter space distribution conditions can be identified, the occurrence probability of each instability mode is quantitatively calculated, various risks possibly existing on the slope can be comprehensively grasped, only the most dangerous single condition is concerned, and the risk assessment accuracy is improved. And the accuracy and engineering applicability of slope stability analysis and risk evaluation are improved.