5340 results about "Extraction methods" patented technology

The invention discloses a remote sensing image building extraction method fusing double-space attention features, and belongs to the technical field of image processing. The method comprises the following steps: firstly, inputting a training set of an Inria aviation data set into a ConvNeXt backbone network to extract shallow feature information; then, carrying out feature information enhancement processing on the feature information through a double-space attention feature fusion module (DAFF); and finally, a multi-modal dynamic enhancement module (FAM) is used in the last time of feature information up-sampling operation to carry out channel alignment operation of different levels of features, and feature information fusion errors are reduced. According to the method, the accuracy of building extraction in the remote sensing image is remarkably improved.

The invention discloses a multi-modal entity and relation extraction method and system based on cross-modal alignment and fusion, and the method comprises the steps: carrying out the processing and coding of an input text and an image, and obtaining a plurality of types of image and text features; performing feature alignment on the fine-grained and coarse-grained text features and the pixel-level image representation by taking the semantic representation of the image as an anchor point, and mapping the image and the text features to the same semantic space; performing multi-granularity feature fusion through text-guided dynamic gating aggregation, visual prefix cross-modal fusion and cross-modal image-text matching, modeling association between noun phrases and image objects in a text while increasing feature complementarity, and obtaining multi-granularity multi-modal feature representation; fusing multi-granularity multi-modal features through entity guidance attention gating, and gathering visual information related to a text entity to obtain final multi-modal fusion representation; according to the multi-modal fusion representation, task prediction of multi-modal named entity recognition and multi-modal relation extraction is carried out.

The invention discloses an interface parameter extraction method and device based on slot filling and a medium, and relates to large model application. The method comprises the steps that semantic analysis is conducted on natural language query through a pre-trained large language model, an intermediate parameter set containing intention types and slot filling parameters is generated, and accurate conversion from natural language parameters to structured interface specifications is achieved through multi-stage standardization processing; based on a dynamic matching mechanism of parameters and interface metadata, an interface calling chain including parallel calling, transaction compensation and data conversion nodes is constructed, and a multi-source aggregation data set is generated through cross-interface data verification and space-time alignment processing; and finally, in combination with dynamic template selection and semantic generation rules, outputting a multi-modal interaction report fusing the structured visual component and natural language interpretation.

The invention provides a remote sensing coastline automatic extraction method and system based on residual space pyramid segmentation and two-dimensional attention, and relates to the technical field of space analysis. The method comprises the steps of high-resolution remote sensing image acquisition and preprocessing, sea-land segmentation network reasoning, probability graph thresholding and edge extraction and vectorization processing. According to the method, the segmentation precision is improved through multi-scale feature aggregation and attention enhancement, coastline vector data with geographic coordinates are generated in combination with edge detection and topological repair, and the method is suitable for spatial analysis and coastline monitoring.

The invention discloses a coal rock fracture intelligent extraction method based on improved U-Net. The method comprises the following steps: S1, constructing a coal rock fracture CT image data set; s2, constructing an improved U-Net segmentation model, specifically comprising the following steps: S2.1, taking VGG16 as a backbone network, and introducing a depth separable convolution module; s2.2, a PPA attention module is added after each layer of depth separable convolution of the decoder, the PPA attention module is introduced after each up-sampling stage of the decoder, and the output of the PPA attention module is subjected to batch normalization and Dropout layer processing; s2.3, defining a composite loss function; s3, training and optimizing a segmentation model, wherein the specific steps comprise: S3.1, setting hyper-parameters; and S3.2, training the model by using the training set, adjusting hyper-parameters by using the verification set, and evaluating the performance by using the test set, wherein the evaluation indexes comprise MIoU, MAcc and FWIoU. According to the method, the problems of difficult identification of small fractures, large model calculation amount, poor multi-scale information fusion and class imbalance in the coal rock fracture image can be solved, and the robustness, segmentation precision and practicability of the model are improved.

The invention provides a literature structured extraction method and system based on artificial intelligence, and aims to solve the problem that information extraction is incomplete and inaccurate in an existing literature processing technology. Firstly, an original literature data set comprising a plurality of literature units is obtained and preprocessed to obtain a standardized literature data set; calling a pre-trained literature feature extraction model to process the standardized literature data, extracting content features and structural features of the literature units, based on the content features and the structural features of the literature units, executing structured analysis processing to generate a structured element set containing theme elements, logic relation elements and key information elements, and finally, storing the structured element set. And integrating and optimizing the structured element set to generate a final structured literature result containing the element association relationship, thereby realizing automatic and structured extraction of the literature information, improving the literature processing efficiency and accuracy, and providing a new way for intelligent management and application of the literature.

The invention relates to the technical field of image recognition, and further relates to a phenotypic character image recognition and extraction method for efficient corn breeding. The method comprises the following steps: step 1, acquiring an image of the same plant canopy in a test field by using an unmanned aerial vehicle carrying a multispectral sensor in a plurality of different growth periods from a corn jointing period to a mature period, so as to obtain a normalized grayscale image; 2, performing gradient operation on a target wave band based on the normalized grayscale image to obtain significant edge intensity, and generating a character sensitive probability field of a corresponding time phase; and 3, presetting an optimal observation period for each target character, selecting a pixel set which has a probability value not less than 0.5 and is judged to be the character type from an image of a time phase corresponding to the optimal observation period, and sequentially measuring the total projection area, the average connected domain area and the average leaf angle to obtain a comprehensive breeding sequence value of the character. According to the method, high-precision probability identification of the effective phenotype area of the grain-leaf is realized.

The invention discloses a coal mining subsidence area surface crack intelligent extraction method based on an improved Transform model, and belongs to the technical field of remote sensing image processing. Firstly, an unmanned aerial vehicle carrying a high-resolution optical camera is used for collecting images, the image overlapping rate of 70%-80% is guaranteed, and a training data set is constructed through professional labeling, cutting screening and data enhancement. The encoder of the innovative model is very distinctive, and the adaptive multi-scale patch mapping layer can dynamically adjust the patch size according to the local complexity of the image and efficiently extract features; double-attention fusion is combined with optimization position coding, and long-distance dependency capture is enhanced; and the calculation amount and the overfitting are reduced by the dynamic sparse connection full-connection layer. Residual attention enhancement pyramid pooling and a space-channel attention bottleneck mechanism are adopted, key features are highlighted, and noise is suppressed; and a breakpoint detection and connection rule determination module is utilized to realize complete restoration of the ground fracture. After the data set is used for training a model, deployment is carried out, and through preprocessing, encoding and decoding and post-processing, ground fracture information can be accurately obtained, and a data foundation is built for mining area safety management and geological disaster prevention and control.

The invention discloses a welding seam track extraction method and system based on RANSAC parameter fitting, and the method comprises the steps: S1, obtaining three-dimensional point cloud data, and obtaining the normal vector and local curvature of each point cloud data point; selecting an initial seed point from the three-dimensional point cloud data according to the local curvature of each point cloud data point; s2, establishing a feature similarity evaluation system, and adding the initial seed points and the corresponding similar adjacent points into the same plane; according to different planes where the point cloud data points are located, plane area coarse segmentation is carried out; and S3, a plurality of geometric fitting models are constructed for the welding track based on the RANSAC algorithm, the geometric fitting model with the highest matching degree is selected from the geometric fitting models, and the welding track in the welding area is extracted. According to the technical scheme, the welding seam track can be rapidly and effectively extracted, the universality of welding seam track recognition is improved, and the extraction error of the welding seam track is remarkably reduced.

The embodiment of the invention relates to the technical field of artificial intelligence, in particular to a waste incineration large model domain knowledge self-adaptive extraction method and system based on dynamic confidence evaluation, and the method comprises the steps: carrying out the dynamic word segmentation of preprocessed text data, and obtaining a dynamic word segmentation result; performing domain term confidence analysis according to a dynamic word segmentation result to obtain a dynamic term confidence score; generating a candidate domain term set based on the dynamic term confidence score; screening the candidate domain term set according to a confidence score threshold, and constructing a waste incineration domain knowledge dictionary according to a screening result; the waste incineration domain knowledge dictionary is merged into a preset large model bottom layer dictionary, and a merged knowledge dictionary is obtained; analyzing the target working condition document based on an AI algorithm model corresponding to the merged knowledge dictionary, and generating an operation instruction question and answer pair; and performing parameter fine adjustment on the AI algorithm model by adopting the operation instruction question and answer to obtain a target algorithm model.

The invention discloses a perceptual fusion-based remote sensing image road extraction method, which comprises the following steps of: carrying out error correction and enhancement expansion on data in a remote sensing public data set, and dividing the data into a training set and a test set; a remote sensing image road extraction model based on channel graph convolution and pixel edge perception is established, a Swin Transform is adopted as an encoder, a channel graph convolution module and a pixel edge perception module are respectively designed at a bridging part and a jump connection part, and a decoding structure of a LinkNet network is taken as a decoder main body part of the model. And designing a topological consistency loss function fused with boundary penalty, and training and optimizing the network model in batches. And testing the images in the test set by using the model stored in the training process. According to the method, the global topological structure and the local detail features of the road can be optimized at the same time, and the accuracy and robustness of road extraction in the remote sensing image are remarkably improved. Compared with an existing general semantic segmentation method DLinkNet, the accuracy rate, the recall rate, the F1 score and the cross-union rate are improved by 1.41%, 2.66%, 3.44% and 3.46% respectively.

The invention relates to an internet big data extraction method and device, equipment and a storage medium, and the method comprises the following steps: carrying out distributed crawler collection on an internet data source, obtaining original network data, and converting the original network data into a structured data matrix; and performing multi-level semantic analysis on the matrix, constructing a semantic feature map, and performing topic segmentation and classification to form a topic domain knowledge tree. Association rules in the knowledge tree are further mined, and an implicit knowledge network is constructed. Performing semantic decomposition and expansion on query conditions based on the network to generate expanded query data, and performing similarity matching with the knowledge network to obtain a candidate data set; and finally, performing multi-factor sorting and extraction on the candidate data, and outputting target data, thereby solving the technical problem that in the second-hand car market, due to wide data sources and fuzzy semantics, an existing system has relatively large deviation when performing price prediction and maintenance cost analysis.

The invention discloses a relation extraction method and system based on a graph neural network, and belongs to the technical field of natural language processing. A target text is obtained, word segmentation, part-of-speech tagging and named entity recognition are carried out, and an entity set is extracted; constructing a text graph structure containing multiple edge types based on the entity set; performing feature coding on nodes in the graph to generate an initial feature vector fusing semantic, part-of-speech and position information; inputting the graph into the graph neural network model, and obtaining high-order node representation through multi-layer message passing and aggregation; modeling the entity pair in combination with the structure path and the context information, and inputting a multi-channel classification network to predict the relationship type of the multi-channel classification network; and finally, outputting an entity relationship triple according to a prediction result. The method has stronger semantic modeling ability and structure expression ability in a relation extraction task, and is suitable for scenes such as knowledge graph construction and information extraction systems.

The invention discloses a multi-modal ship target individual identification method and system, and relates to the technical field of ship target identification, and the method comprises the steps: obtaining multi-modal data of a target region, and constructing a training set and a test set; each training sample in the training set is input into a target individual prediction model, a multi-granularity adaptive loss function is adopted as a loss function to carry out model training, and the target individual prediction model adopts an image feature extraction method based on cross-modal guide enhancement to carry out feature extraction and splicing fusion on each training sample; and a target individual identification result of each training sample is obtained by adopting a visual angle self-adaptive fusion method of embedded type label information. And after training is completed, a trained target individual prediction model is obtained, each test sample in the test set is input into the trained target individual prediction model, and a final target individual identification result is obtained. According to the invention, the generalization ability of model learning individual features can be improved, and the accuracy of ship target individual identification is improved.

The invention relates to an audio feature extraction method and device based on a neural network. The method comprises the following steps: performing framing processing and fast Fourier transform on an original audio signal acquired by a vehicle-mounted microphone to obtain a frequency domain feature sequence; extracting short-time energy, a zero-crossing rate, a frequency spectrum centroid and a Mel-frequency cepstrum coefficient from the frequency domain feature sequence, and combining first-order difference and second-order difference features of each feature to obtain a multi-dimensional acoustic feature point sequence; performing time sequence arrangement on the multi-dimensional acoustic feature point sequence, constructing an original feature matrix, and performing principal component analysis on the original feature matrix to obtain a feature description matrix; inputting the feature description matrix into a dynamic time feature extraction network for deep time-frequency feature analysis to obtain vehicle-mounted voice deep time-frequency features; and performing voice detection classification based on the vehicle-mounted voice deep time-frequency features, and outputting a vehicle-mounted voice detection result. According to the invention, various noise interferences in a vehicle-mounted environment are effectively suppressed, and the anti-noise capability of the system is enhanced.

The invention relates to a vertical domain relation extraction method and device, electronic equipment and a storage medium, and the method comprises the steps: constructing a knowledge base of a target vertical domain, carrying out the entity relation extraction of a target text through combining with the knowledge base of the target vertical domain, and obtaining a first entity relation set; performing entity relationship extraction on the target text by utilizing a trained entity relationship extraction model to obtain a second entity relationship set, and performing correction processing on the second entity relationship set by utilizing a knowledge base to obtain a fourth entity relationship set, and screening out a target entity relationship set from the first entity relationship set and the fourth entity relationship set in combination with the knowledge base of the target vertical field. By introducing the vertical field knowledge base, external data support is provided for a large model, the defects of the prior art in the aspects of long-tail relation processing and vertical field reasoning capability are overcome, and meanwhile, the filtering capability of noise samples is enhanced and the extraction quality and efficiency are improved by combining a combined comparison screening mechanism, a knowledge base automatic updating mechanism and a health management mechanism.

The invention discloses a word embedding vector extraction method and system based on a large language model, and belongs to the field of natural language processing, and the word embedding vector extraction method based on the large language model comprises the following steps: S1, embedding a global context attention module, generating a Q / K / V vector through linear transformation, and calculating a global attention weight in a cross-position manner; s2, splicing coding features of adjacent layers, and performing gating fusion to generate a global context dynamic semantic state; s3, multi-head attention joint coding is carried out on the dynamic semantic state of the current layer and the global state; s4, performing average pooling to generate a global semantic vector, and calculating a dynamic attention weight; s5, performing weighted fusion on the dynamic weight and the global vector to generate an enhanced code; s6, outputting a final word vector by a GELU nonlinear transformation full-connection layer; the method has the beneficial effects that the global context semantic capture capability is enhanced, and the expression effect of the word embedding vector is improved.

The invention relates to the field of document processing and data extraction, and particularly discloses a PDF text extraction method and system based on a large language model.The method includes the steps that content of all pages of a target PDF document is positioned and marked to obtain a first to-be-recognized area and a second to-be-recognized area, and noise interference features of the to-be-recognized areas are removed; formulating a multi-level text logic reconstruction strategy to complete reconstruction of a logic sequence of the target PDF document, preliminarily outputting a first-level PDF document, and performing primary image-text association degree analysis to output first association strength; performing intelligent anomaly recognition and correction on the content of the first-level PDF document on a semantic structure through a large language model to obtain a second-level PDF document, and outputting second association strength; judging whether the secondary PDF document is qualified or not based on the first association strength and the second association strength; according to the method, the logic sequence and the semantic integrity of the document can be recovered, and the text purity and the structural integrity are improved.

The invention provides a torreya grandis forest extraction method based on a deep learning network and a multi-temporal remote sensing image, and the method comprises the steps: carrying out the data preprocessing of a remote sensing image, constructing and obtaining a comprehensive feature image of each month, extracting the pixel samples of torreya grandis and non-torreya grandis types, calculating and obtaining a comprehensive class spacing distinguishing capability index of each month, and obtaining a torreya grandis forest extraction result. Obtaining an original wave band feature set of the similar hyperspectral structure; performing feature optimization by using a maximum correlation minimum redundancy algorithm to obtain an optimized waveband feature set; marking torreya grandis and non-torreya grandis areas according to the torreya grandis sample points and the high-resolution remote sensing image, and making classification labels for deep learning; and constructing a space-spectrum multi-scale feature fusion network model, inputting the optimal waveband feature set into a deep learning network for training, and outputting classification results of torreya grandis and non-torreya grandis. According to the method, the multi-temporal remote sensing image can be fully utilized, and the spatial and spectral features are effectively extracted and fused, so that the recognition precision and efficiency of the torreya grandis are improved.

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

The invention relates to the technical field of data processing, and discloses a multi-modal relation extraction method based on large model generation and knowledge graph integration, which comprises the following steps of: firstly, generating description for an image by using a large language model, constructing an image graph, analyzing a text, constructing a text graph, linking a knowledge graph based on a tagged entity, and extracting a multi-modal relation between the tagged entity and the knowledge graph; and constructing a knowledge graph sub-graph. And then, taking the labeled entity as an anchor point, splicing the three through similarity calculation to form a unified combined graph, and extracting and fusing multi-modal features by utilizing a graph information bottleneck technology and a GCN optimization graph structure, so that the accuracy and the processing efficiency of relation extraction are improved. And through multi-modal data fusion, the accuracy and the processing speed of relation extraction are remarkably improved. A graph structure is optimized by using a graph information bottleneck technology and a graph convolutional network, and redundant information is eliminated, so that semantic features in multi-modal data are effectively extracted and fused.

The invention discloses a method and a system for intelligently acquiring and analyzing webpage merchant information. Automatic acquisition of the merchant information is realized through five main steps of intelligent webpage capture, intelligent field identification, accurate address information analysis, intelligent merchant grouping and automatic page discovery. According to the method, a traditional rule-based extraction method is abandoned, information such as merchant names, phone numbers and addresses in webpages is automatically recognized by using context semantic analysis and mode recognition technologies, and accurate association grouping of fields is realized through a dynamic weight multi-dimensional similarity calculation method; and the merchant information can be continuously discovered and collected by using an automatic page discovery mechanism without manually specifying a collection path. The technical bottlenecks that specific rules need to be written for different websites, webpage structure changes are difficult to deal with, multi-merchant information grouping cannot be processed and the like in a traditional method are broken through, the method adapts to various webpage structure changes, and the automation degree, accuracy and efficiency of merchant information collection are greatly improved.

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 discloses a video portrait consistency editing method based on optical flow guidance and text driving, and the method comprises the steps: firstly, employing a face key point extraction method and a depth estimation method to extract a face key point and a depth map of each frame of an input video, and guaranteeing the consistency of the facial expression of an edited portrait and a source image; a diffusion control network is used to edit and generate the face depth map, the key points and the user input text description as conditions, and the consistency of multi-view editing is realized through the potential code alignment of an attention mechanism and the guidance of optical flow change; thirdly, constructing a diffusion model with consistent time by utilizing the inter-frame optical flow change and the depth map change; through the above steps, the problems of multi-view and time consistency in video editing are solved, and high-quality video portrait editing is realized.

The invention relates to the technical field of artificial intelligence, in particular to a spatio-temporal context extraction method and system for real world model training, and the method comprises the steps: extracting all image information and text semantic information through content feature vectors; structural information such as spatial layout of a scene and relative positions of people and objects in the collected image is extracted through the spatial structure feature vector, and feature information such as appearance, posture and action of a target object is extracted through the entity feature vector; obtaining a time sequence feature vector according to the content feature vectors, the spatial structure feature vectors and the entity feature vectors in a plurality of preset time periods, capturing multi-dimensional dynamic changes of the target physical place in a period of time, and analyzing the extracted vectors to obtain scene analysis feature data; all feature information is integrated to obtain the spatio-temporal context, the representation richness and comprehensiveness of the target physical place are improved, and the representation degree and adaptability of the real world model obtained through subsequent training to the target physical place are achieved.

The invention relates to the technical field of intelligent file processing, and discloses an element extraction method and device of a test report, computer equipment and a storage medium, the method comprises the following steps: obtaining scanning document documents of a plurality of paper test reports, and screening out a multi-modal format document from the scanning document documents; identifying and extracting text features, picture features and table features in the multi-modal format document based on an optical character identification technology model; performing paragraph combination on the extracted text features; and extracting structured data from the text features, the picture features and the table features after paragraph combination by adopting a multi-modal element extraction model to obtain a complete electronic document corresponding to the multi-modal format document. Based on an optical character recognition technology, character features, image features and table features in various paper test reports are accurately recognized and extracted by using a number, image and character recognition and extraction technology, and the recognition and extraction accuracy and extraction efficiency are improved.

The invention provides a sea ice area extraction method and device based on remote sensing, which are applied to the field of seawater detection, and the method comprises the steps: obtaining a remote sensing image of a target area; pixels of the remote sensing image are partitioned based on the normalized vegetation index, the pixels with the normalized vegetation index smaller than a preset vegetation index threshold value are divided into first type regions, and the first type regions comprise at least one of a water body region and a sea ice region; determining a normalized deicing index of the first type region; performing normalized deicing index threshold segmentation on the first type region through a maximum between-class variance method to obtain a sea ice classification threshold; and filtering and post-processing the remote sensing image based on the sea ice classification threshold, and taking the first type region of which the normalized deicing index is greater than the sea ice classification threshold as a sea ice region. The method is suitable for sea ice monitoring in a large area, and sea ice information can be efficiently and accurately extracted.

The invention provides a complex question and answer method and system based on adaptive task deconstruction and multi-modal evidence aggregation, belongs to the technical field of natural language processing, and designs a dynamic Few-shot prompt construction method based on dependency syntax fingerprints to ensure that a prompt template is matched with a question structure; the invention discloses a dynamic problem deconstruction method based on confidence evaluation and auto-reflection. The method comprises the following steps: recursively decomposing a problem tree by using a large language model; converting the problem tree into a standardized linear task execution sequence by a problem tree context dependence specification and task sequence generation method; obtaining a high-correlation evidence set of each task based on an evidence generation method of two-way recall and cross encoder rearrangement; and the task sequence is reasoned and dynamically optimized by a question answer extraction method based on double-strategy aggregation reasoning. According to the method, the accurate complex question and answer result can be provided on the premise of ensuring the question disassembling quality, restraining error propagation and comprehensively recalling evidences.

The invention discloses a tunnel disease target intelligent identification and extraction method based on machine vision, relates to the technical field of tunnel engineering detection and intelligent analysis, and is used for solving the problems of low identification precision and poor adaptability to complex scenes. Comprising the steps of collecting a high-resolution image and carrying out adaptive illumination compensation preprocessing, carrying out multi-scale image segmentation to extract texture and edge features, constructing a disease discrimination model to screen a suspected area, and accurately positioning and classifying a disease target in combination with three-dimensional space coordinates. The complex scene adaptability and the irregular target recognition capability are remarkably improved, meanwhile, efficient and accurate positioning and classification of disease targets are achieved, and reliable technical support is provided for tunnel operation and maintenance.

The invention discloses an X-ray chest radiography report automatic generation method, system and device based on factual description enhancement and a medium. The method comprises the following steps: firstly, constructing a medical entity extraction method based on a RadGraph model, and carrying out identification and structured extraction on clinical keywords to obtain factual description consisting of key medical entities; secondly, establishing a comparative learning method guided by factual description, enhancing semantic consistency between the image and the text from global and local levels, and extracting visual features with diagnostic value; establishing a historical similar case retrieval strategy independent of disease tags, and calculating visual semantic similarity to realize automatic retrieval of historical cases; and finally, proposing an evidence-driven chest radiography report generation method, constructing a cross-modal fusion network, and generating a chest radiography report with clinical accuracy and consistency. The system, the equipment and the medium automatically generate an X-ray chest radiography report based on factual description enhancement based on the method; according to the method, efficient and stable automatic retrieval is realized, the clinical accuracy of the generated chest radiograph report and the reliability of evaluation are improved, and the universality and robustness of the model are remarkably improved.