2819 results about "Extraction methods" patented technology

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 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 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 provides an underground water environment automatic monitoring super station state supervision method and system. The method comprises the following steps: collecting a multi-source dynamic time sequence data set and carrying out time-space alignment; generating a dynamic characteristic index set by using a nonlinear dynamic characteristic extraction method; based on the index set, constructing a self-adaptive cooperative measurement network to perform anomaly monitoring, and generating an anomaly detection result and a cooperative control instruction; generating real-time monitoring and early warning information by using a dynamic threshold adjustment algorithm; and generating an adaptive control instruction and a dynamic resource allocation scheme by using a neural network adaptive control strategy and a resource scheduling optimization algorithm. According to the method, a dynamic characteristic index set is generated, a C-C method is adopted to reconstruct a high-dimensional phase space, a Wolf algorithm and a G-P algorithm are combined to calculate related indexes and dimensions, and a multi-scale fractal mode is analyzed through an R / S analysis method and wavelet transform. The methods capture complex dynamic behaviors of the underground water system, and solve the problem that the traditional method is insufficient in non-linear feature extraction capability.

The invention discloses a medical data structured extraction method based on machine learning, and the method comprises the following steps: carrying out the standardization processing of multi-source heterogeneous data in different medical scenes, constructing a time and condition two-dimensional filtering rule, and extracting preliminary data; and a modular index structure is formed according to medical process and technical attribute division. And generating analysis limiting conditions by fusing the medical knowledge graph and the knowledge base, guiding an analysis engine to perform semantic routing and reasoning, and outputting a structured result. Finally, disease identification and quality judgment are achieved, and structured information meeting or not meeting the standard is output. The method aims at efficiently extracting the structured information from various types of medical documents.

The invention discloses an online question and answer illusion detection method based on a knowledge graph and multi-stage fusion, and the method comprises the following steps: receiving a natural language question, recognizing a key entity set through a multi-temperature sampling entity extraction method, and carrying out the matching verification of the key entity set and a pre-constructed knowledge graph, and obtaining a successfully matched entity set; performing structured evidence mining from the knowledge graph according to the successfully matched entity set, calculating the final weight of the evidence by adopting a multi-dimensional weight evaluation mechanism, grading the evidence, and screening and retaining the evidence with different confidence levels; adopting a three-stage progressive reasoning strategy: conservative answer generation, extended answer enhancement and complete answer annotation, and generating a final answer; and performing declaration-level consistency judgment according to the evidence, and calling context information to perform error correction when a contradictory declaration is detected, thereby completing illusion detection. The problems that'illusion 'is easily generated in an existing online question-answering system and an effective illusion detection mechanism is lacked are solved.

The invention discloses a methane point source emission remote sensing recognition and plume extraction method and a methane point source emission remote sensing recognition and plume extraction system, belongs to the technical field of methane point source recognition, and solves the problems of incomplete methane plume extraction and lack of morphological characteristics of an extraction result in a traditional signal filtering method by introducing threshold segmentation, spatial continuity and geographic space constraints. And the method has good applicability to the multi-point dissipation condition in the after-mine activity process of a coal mine area.

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

The invention discloses a method and device for automatically generating a Java method unit test case based on AI, and the method comprises the steps: building a structured annotation specification, carrying out the automatic traversal and intelligent analysis of a Java project, extracting a method signature, a parameter type and a parameter constraint condition, constructing a test data generation model, and carrying out the automatic generation of the test case. The model is transmitted to an AI service to analyze business logic and boundary conditions, test case codes are generated, test cases are optimized and integrated to a project test directory, finally, code areas with insufficient coverage are identified through coverage rate analysis, a test completion strategy is generated, and iterative perfection of the test cases is completed. The technical problems that in the prior art, project-level automatic test case generation cannot be achieved, the generated test case cannot fully cover business logic and boundary conditions, and context relation understanding is lacked are solved, full-automatic project-level test generation is achieved, and the test efficiency and quality are improved.

The invention discloses an unmanned aerial vehicle communication data feature extraction method and an unknown type intrusion detection method. Physical layer information and network layer information of an unmanned aerial vehicle are acquired; wherein the physical layer information comprises the position, attitude, height and command type of the unmanned aerial vehicle, and the network layer information comprises an IP address, a port number, a protocol type and packet metadata; constructing an unmanned aerial vehicle state feature vector according to the physical layer information and the network layer information of the unmanned aerial vehicle; based on the trained CNN branch network, extracting communication data features used for intrusion detection in the unmanned aerial vehicle state feature vectors; a double-branch network structure of the convolutional neural network and the time sequence convolutional network is provided, a loss function is improved, an information entropy regularization item is introduced, efficient detection and distinguishing of unknown attack samples are achieved, the network structure has the advantages of being high in calculation efficiency and light in weight, and the method is suitable for large-scale popularization and application. The method is suitable for being deployed in resource-limited edge computing scenes such as unmanned aerial vehicles.

The invention relates to the technical field of deep learning, in particular to a building target intelligent extraction method and system based on multi-source surveying and mapping data fusion, and the method comprises the steps: obtaining multi-source surveying and mapping data of a target region; preprocessing the multi-source surveying and mapping data; based on a pre-constructed multi-source data fusion module, performing feature level fusion on the spectral feature map, the geometric feature map and the elevation feature map of the preprocessed high-resolution optical remote sensing image to generate a multi-dimensional fusion feature matrix; inputting the multi-dimensional fusion feature matrix into a trained model, and outputting an initial extraction result of the target region; and post-processing the initial extraction result to obtain a target extraction result. Compared with traditional single data extraction, high-resolution optical remote sensing images, laser point cloud and digital elevation model data are obtained synchronously, spectrum, three-dimensional geometry and elevation dimension information can be covered at the same time, the defect of single data is avoided from the source, and comprehensive data support is provided for subsequent accurate extraction.

The invention discloses a verifiable APT attack chain extraction method based on a cognitive distillation and gating technology. The method comprises the steps of input processing and extraction, dynamic gating weight generation, hierarchical prototype decision making, adversarial robustness guarantee and output. An attack chain time sequence modeling and dynamic gating mechanism is created for the first time, causal constraints are injected through phase perception position coding, semantic fragmentation is avoided through full-word masks, and splitting is conducted in the cracking stage; historical dependence and semantic similarity weight reduction are fused to block noise, and the cutting precision is improved. A cognitive distillation dynamic knowledge evolution system is constructed based on prompt engineering prototype matching and self-attention feature decoupling double engines, so that few-sample reasoning and decision determinacy quantification are realized, technical representation is stripped, a kernel is focused and attacked, and real-time evolution of a knowledge base is promoted; a defense closed loop is constructed by parameterized noise injection and KL divergence, a verifiable countermeasure immune system is established, an attack is simulated to optimize a defense benchmark, probability distribution stability is monitored, the misjudgment rate is extremely low, and a traceable audit log is output.

According to the movie and television play table book extraction method and device, the storage medium and the computer equipment provided by the invention, after an audio and video file of a movie and television play is split into a video file and an audio file, feature recognition is performed on the video file to obtain a subtitle text, speaker face information and a video understanding text; performing voice understanding on the audio file to obtain a voice transcription text and a voice understanding text; wherein the voice transcription text can be corrected into the standard transcription text with high accuracy through the subtitle text. Therefore, based on the face information of the speaker, the line segment of each speaker in the standard transcriptional text and the audio and video file is aligned, so that speaker information with accurate segmentation and semantic coherence can be obtained; and then, through combination with a character side-writing text generated by side-writing analysis on the speaker based on the video, the voice understanding text and the speaker information, table book information is constructed, and related feature description of the character can be covered on the basis of containing the line content, so that the content and depth of the table book are enriched.

The invention discloses an alloy field-oriented text and table double-track collaborative data extraction method and system. The method comprises the following steps of: obtaining an alloy related document DOI; taking the DOI as an index to obtain a full-text literature and storing the full-text literature in a JSON format; performing document screening based on an LDA topic distribution model and establishing a target topic document library; preprocessing the unstructured table, and obtaining structured table data by using a large language model; performing automatic text segment division on the JSON format text through rule matching analysis; and sorting the text segments by using a large language model to generate semi-structured text data, fusing and normalizing the text and table data, and outputting a component-process-organization-performance-strengthening mechanism alloy data chain. According to the method, through a double-track collaborative data extraction mechanism, the problems of multi-source heterogeneity, format fragments, information islands, semantic layering and the like of material subject data are effectively solved, structured treatment of literature data in the alloy field is achieved, and a unified technical path is provided for constructing a high-quality alloy database.

The invention belongs to the technical field of engineering data processing, and particularly relates to a digital twinning-based engineering completion delivery data information extraction method, which comprises the following steps of: acquiring completion site entity data, constructing a digital twinning body of building information model and live-action three-dimensional fusion, and fusing semantic tag information based on an improved ICP (Inductively Coupled Plasma) algorithm. Performing space coordinate alignment and attribute association on the digital twinborn body to generate a completion information dynamic twinborn mapping map; a multi-level rule inference engine is used for extracting information items conforming to delivery standards, a dynamic risk propagation model is used for simulating the influence of data deviation on a delivery result through multi-node consensus verification data of a construction party, a construction party and a supervisor, and information extraction weight parameters are corrected in real time; and dynamically generating a delivery data packet and a visual auditing report and synchronously updating the digital twin archive library in combination with engineering change records and acceptance specifications. Therefore, the problems of incapability of capturing engineering parameter fluctuation, poor transmission efficiency and the like in the prior art are solved.

The invention relates to the technical field of computers, and discloses a multi-modal fusion key frame extraction method and device, equipment and a medium, and the method comprises the steps: obtaining multi-modal input data, carrying out the modal feature coding, and obtaining a video modal feature, an audio modal feature and a text modal feature; carrying out attention fusion on the video modal features, the audio modal features and the text modal features to obtain fused cross-modal causal features; analyzing the cross-modal causal features through a causal reinforcement learning decision module in combination with a preset time sequence causal graph to obtain a fusion feature sequence and key frame probability distribution; and carrying out key frame selection operation on the time slice of the fusion feature sequence based on the key frame probability distribution to obtain a key frame set, and generating a space-time thermodynamic diagram and causal relationship visualization result corresponding to the key frame set. The multi-modal fusion key frame extraction method and device can be applied to financial science and technology or medical care service program systems, and the accuracy and interpretability of multi-modal fusion key frame extraction can be improved.

The invention provides an image region-of-interest extraction method and system based on a Mama architecture, and relates to the technical field of image processing, and the method comprises the steps: obtaining a to-be-extracted image; performing multi-scale feature extraction on the to-be-extracted image through a local enhancement module; multi-scale semantic enhancement features are generated through a cross-scale self-attention module, and dimension reduction processing is performed through a feature conversion module; local detail features are generated through an adaptive detail enhancement module; performing global context enhancement processing on the local detail features through a pyramid pooling module to generate context enhancement features; performing up-sampling processing on the context enhancement features; the context enhancement features after up-sampling processing are fused through a self-adaptive global-local fusion gating module; and carrying out image extraction based on the decoded fusion features. The image segmentation precision is improved, the model is light in weight, reasoning is fast, and the problems of image boundary blurring and scale variability are effectively solved.

The invention belongs to the technical field of remote sensing image analysis, and provides a multi-source remote sensing building extraction method based on hybrid experts, electronic equipment and a storage medium. The method comprises the steps of basic model construction, two-way structure model construction, optical feature extraction, earth surface feature extraction, two-way feature fusion, ViT coding, feature injection and extraction and semantic alignment step-by-step up-sampling. According to the invention, a dual-path structure is adopted to assist the multi-scale encoder and the bidirectional attention fusion module, and DSM data and optical images are used for feature fusion, so that the capability of distinguishing buildings from backgrounds is improved; the hybrid expert LoRA structure is introduced into the ViT encoder, parameters of the feedforward neural network are dynamically adjusted, the adaptability and flexibility of the model to input features are further enhanced, and the calculation complexity is reduced.

The invention discloses a low-altitude remote sensing ground feature element extraction method fused with a DSM side adapter. According to the method, visual features are extracted by taking a pre-training visual large model of frozen parameters as a trunk, and a trainable DSM side adapter is constructed in parallel to extract geometric features of elevation of a ground object target; a cross-modal attention fusion mechanism is utilized, visual features are used as queries, elevation geometric features are used as key values, height information injection is dynamically guided, and content-aware adaptive modal fusion is achieved. Besides, a parallel prompt decoding strategy is adopted, a virtual task batch is constructed by using a batch stacking technology, and multi-category semantic segmentation is reconstructed into a high-dimensional parallel prompt-driven binary segmentation task, so that the limitation of a large model native normal form is broken through on the premise of less structural modification, and the real-time performance of the system is improved. And end-to-end fine tuning is carried out by combining a channel mutual exclusion and competition mechanism. According to the method, the ground feature element extraction precision and the boundary integrity in a complex scene are remarkably improved, and the method has the advantages of low training cost, high generalization and flexible deployment.

The invention discloses a scene three-dimensional reconstruction and vector information extraction method and device based on vehicle return data, equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of a time sequence image returned by a vehicle and corresponding pose data, and obtaining a semantic mask and a camera pose of each frame of image in the time sequence image; based on the semantic mask and the camera pose, performing three-dimensional geometric reconstruction on the static scene area in the time sequence image to obtain a static three-dimensional scene model; a dynamic target is separated from the time sequence image according to the semantic mask, three-dimensional motion modeling and independent three-dimensional reconstruction are carried out on the dynamic target, and a dynamic target three-dimensional model is generated; and generating a dynamic three-dimensional scene model and vector labeling information based on the static three-dimensional scene model and the dynamic target three-dimensional model. According to the method, the limitation of automatic driving shadow mode data is overcome, the three-dimensional reconstruction of the scene and the automatic generation of the true value information are realized, the data acquisition cost is further reduced, and the efficiency of automatic driving research and development is improved.

The invention discloses a flotation froth image feature extraction method based on adaptive multi-scale weighted fusion, and belongs to the technical field of intelligent monitoring and image processing in the mineral flotation process, and the method comprises the steps: fusing a color histogram, texture and edge features, and depth features extracted by VGGNet, GoogLeNet and ResNet; and mapping and fusion of multi-modal features are realized by using a self-adaptive multi-scale weighted feature fusion network. And dynamic weighting and fusion are carried out on different characteristic modes through a nonlinear neural network model, and unified characteristic representation is generated to be used for accurate representation of the flotation froth state. The method has the advantages of high recognition precision, high robustness and flexible deployment, online monitoring, optimal control and index prediction of the flotation process can be effectively supported, and reliable technical support is provided for an industrial field.

The invention discloses a plant protection field literature information batch structured extraction method based on an AI large model, and belongs to the technical field of computer data processing and artificial intelligence application. According to the method, a PDF format literature is converted into a Markdown format text through a PDF literature self-adaptive preprocessing module and a Markdown conversion module; then, a double-strategy self-adaptive AI extraction module is adopted, different modes are adopted for processing according to a text length threshold value, and JSON data are extracted in combination with a JSON format data restoration strategy; then, the CPU intensive conversion task and the I / O intensive AI analysis task are processed in parallel through a two-stage parallel scheduling module, and a structured database file is generated through aggregation according to a predefined mapping rule through a multi-dimensional aggregation export module. According to the method, end-to-end automation from literature acquisition to structured data output is realized, and the problem of low extraction efficiency of manual literature reading information is solved.

The application belongs to the technical field of multimedia information processing, and discloses a kind of monitoring video key frame extraction method, system, medium, equipment and terminal, collect original video stream data, original video stream data is decomposed into image frame set;Image frame set obtained by decomposition is sampled, and the image frame result set obtained by sampling is filtered;The image frame set after filtering is adaptively clustered, and the result after clustering is collected to form a video abstract.In order to better utilize the memory space of storage medium and let the user better quickly browse the general content of original video stream, the application provides a key frame screening algorithm for original video stream by sampling, filtering and clustering.The key frame extraction method of the application screens out similar frames, redundant frames and fuzzy frames in original video stream through key frame screening algorithm for original video data, forms a video abstract storage of a section of original video, thereby greatly reduces the occupied storage space.

The invention relates to the field of drawing recognition, in particular to a PDF drawing data extraction method and system based on intelligent recognition. Comprising the following steps: reading an internal structure of a PDF engineering drawing to obtain a native text stream, a vector path and a grating image; identifying the native text flow through a shunt preprocessing framework to form structured text data; rendering the vector path and the grating image to obtain a background image; analyzing the structured text data by utilizing the intelligent recognition model through the character recognition and extraction sub-model, obtaining drawing metadata and recording the position, and obtaining a character recognition result; analyzing the background image through a graphic element recognition and classification sub-model, recognizing and classifying component elements, and obtaining a graphic recognition result; and performing fusion according to the visual space corresponding relation to form a drawing analysis result. According to the method, the adaptive capacity of engineering drawings with various sources and different qualities is improved through the shunting preprocessing framework and the intelligent identification model.

The invention discloses an electric arc detection method based on differential increase and structured attention, and the method comprises the steps: firstly carrying out the collection and preprocessing of a current signal, carrying out the differential enhancement according to a sample type, carrying out the strong enhancement of an electric arc sample, improving the generalization capability, and carrying out the weak enhancement of a normal sample, thereby avoiding the overfitting; the problem of class imbalance is relieved, and the model generalization ability is improved; secondly, obtaining six complementary feature representations of time domain waveform, frequency domain frequency spectrum, time frequency analysis, envelope features, statistical distribution and related features from the differentially enhanced current signal through a multi-modal feature extraction method, and fusing to generate a multi-modal image; then, designing a deep learning model integrated with structured attention, carrying out distinguished attention on different feature analysis areas of the multi-modal image, and directionally enhancing arc features; and finally, dynamically quantifying the trained model, reducing the size of the model and reasoning delay, and supporting efficient deployment of various edge computing devices.

The invention belongs to the technical field of image processing, and particularly relates to a remote sensing image road extraction method based on D-LinkNet improvement, which comprises the following steps: S1, inputting a remote sensing image; s2, extracting road feature information in the remote sensing image by using an encoder; s3, the feature map extracted by the encoder enters a D-block module, and the D-block module captures and fuses multi-scale information; s4, jumping connection exists between the encoder and the decoder, and a strip attention mechanism SCAM is added to the jumping connection; and S5, performing up-sampling on the feature map by using a multi-scale snakelike decoder, and finally generating a road segmentation image. According to the invention, a bar-shaped pooling module is added to an encoder part, remote context information is fused in real time and in multiple directions in a feature extraction stage, the problems of limited receptive field and isotropy of standard convolution operation are effectively solved, the phenomena of fuzzy details and road fracture caused by shielding and shadow are obviously improved, and the method is suitable for large-scale popularization and application. And the long and thin road extraction capability of the road extraction network is enhanced.

The invention discloses a geological mineralization causal knowledge extraction method, a storage medium, equipment and a product, and relates to the field of geological mineralization, and the method comprises the steps: constructing a causal hypothesis of a causal role from an unstructured geological text; constructing a causal data set based on causal hypothesis, and training the natural language inference model by using the causal data set; wherein the input vector of the causal data set and the vector of the causal role are fused to form the input vector of the model; a bias matrix of a causal role is introduced to calculate a self-attention weight; obtaining gating weights of the causal role and the global context based on [CLS] embedding, and performing weighted summation on embedding of the causal role and [CLS] embedding by using the gating weights to obtain a final fusion representation; and identifying a high-confidence causal chain from the geological text by using the trained model to obtain a typical causal mode related to the specific mineralization. According to the invention, the chain type identification of the complex mineralization mode can be realized.

The invention discloses a low-temperature CMOS carrier MFP and microscopic parameter extraction method and system, which are corresponding schemes, in the scheme, a carrier MFP extraction model for eliminating a microscopic unknown quantity is constructed by utilizing a geometric scaling rule of quasi-ballistic transport and an existing effective mobility extraction technology, and an MFP extraction model for eliminating the microscopic unknown quantity is constructed by utilizing the geometric scaling rule of quasi-ballistic transport and the existing effective mobility extraction technology; the MFP extracted by the carrier MFP extraction model is ensured to be a pure experiment observation value on the basis that effective quality, Fermi velocity and scattering time do not need to be predicted based on the self-consistency of macroscopic current and voltage data along with the change of channel length; meanwhile, on the basis, the microcosmic parameters are decoupled, so that the decoupled microcosmic parameters are extracted one by one; compared with an existing scheme, the method can be directly applied to a standard wafer-level test board in the industry, MFP distribution of hundreds of devices can be rapidly counted, and a feasible engineering scheme is provided for low-temperature process monitoring.

The invention relates to a pavement crack image skeleton extraction method based on an improved Zhang-Suen refinement algorithm. The method comprises the following steps: S1, carrying out binarization processing on a pavement crack image; s2, carrying out boundary expansion on the crack binary image; s3, carrying out run-length coding on the crack binary image in the horizontal direction, and carrying out compression storage on continuous non-zero pixel segments in each row; s4, using an improved Zhang-Suen refinement algorithm to process the crack binary image processed in the step S3; s5, removing boundary extension; s6, performing simplification processing on the preliminarily refined crack skeleton image by using four predefined 3 * 3 L-shaped structure templates; s7, removing burrs in the image; and S8, outputting a crack skeleton image. The technical problems that according to an existing skeleton extraction method, a topological structure is prone to being damaged, the calculation efficiency is low, the extraction result cannot be completely a skeleton with the single pixel width, and burrs exist in the skeleton extraction result are solved.

The invention discloses a tidal flat automatic extraction method fused with an irregular hexagonal grid. The method comprises the following steps: step 1, constructing a time sequence remote sensing data set; step 2, establishing a precision verification sample library; step 3, screening spectral indexes; 4, synthesizing an extreme tide level image; 5, generating an irregular hexagonal grid; step 6, carrying out adaptive threshold segmentation; step 7, secondary elimination of confused ground features; step 8, tidal flat range extraction and precision verification; according to the method, based on a time sequence remote sensing image set, pixels at all positions are screened through a specific spectral index maximum value, a new extreme tide level image is formed through combination, spatial heterogeneity of a complex coastal zone is adapted through an irregular hexagonal grid and a bottom-to-top hierarchical merging strategy, local self-adaptive threshold segmentation is achieved in cooperation with a local Otsu algorithm, and a new extreme tide level image is obtained. The optimal grid scale is automatically determined by means of an elbow method, and the confused ground features are secondarily eliminated through the time sequence standard deviation, so that the problem of miscarriage of the confused ground features is effectively solved, and high-precision full-automatic extraction of the tidal flat is realized.