345 results about "Domain identification" patented technology

A method for directing a domain-specific request to a machine learning (ML) agent of a plurality of ML agents configured to generate domain specific language (DSL) scripts includes, at a computing system: processing a request received from a user to determine a particular domain associated with the request; identifying an ML agent of the plurality of ML agents that is associated with the particular domain; generating, using the ML agent, a DSL script for the particular domain based on the request; executing, using the ML agent, at least one of an API call and a database query based on the DSL script; processing, using the ML agent, a response to the at least one of the API call and the database query; generating, using the ML agent, an output based on the response; and processing the output using at least one other ML agent of the plurality of ML agents.

The invention discloses a CAD drawing segmentation parameter adjustment method and system combined with region recognition, and relates to the technical field of image processing, and the method comprises the steps: obtaining a target CAD drawing file, carrying out the region feature extraction, generating a region feature vector set, carrying out the region type recognition, and determining a plurality of pieces of region information; constructing a segmentation rule base, matching the segmentation rule base according to the information of the plurality of regions, and dynamically adjusting the boundary information according to a matching result to obtain a segmentation parameter set; based on the segmentation parameter set, performing adaptive segmentation processing on the target CAD drawing file to generate a structured drawing segmentation result; and based on the structured drawing segmentation result, carrying out topological relation verification, and generating a segmentation optimization result. According to the method, the technical problem of poor segmentation effect caused by inaccurate region identification and fixed segmentation parameters in the CAD drawing segmentation process in the prior art is solved, and the technical effects of improving the accuracy and adaptability of CAD drawing segmentation and optimizing the segmentation quality and the structural effect are achieved.

The invention discloses a graph convolution spatial domain identification method of a fusion gene interaction network, and relates to the technical field of biological information, and the method comprises the steps: preprocessing spatial transcriptome data; constructing a spatial diagram according to the spatial position information of the spot and the gene expression matrix; constructing a conditional gene interaction network based on at least a part of key genes in the spatial transcriptome data, constructing a conditional gene interaction network matrix, combining the conditional gene interaction network matrix and the gene expression matrix to obtain an expression degree-interaction matrix, and constructing a feature map; performing graph neural network convolution on the space graph and the feature graph, and introducing an attention mechanism to obtain final embedding; and clustering and finally embedding, and identifying a spatial domain with a similar gene expression mode. According to the method, the spatial domain is identified through the fusion gene interaction network, the problem that the spatial domain is discontinuous is effectively solved, the robustness and accuracy of the spatial domain identification method are improved, and high performance is shown in complex and highly heterogeneous tissue samples.

The invention discloses an automatic data compliance check tracking system based on a smart contract, and relates to the technical field of data check, and the system comprises the steps: building a hierarchical mapping database of domain classification samples and compliance check depth; deploying a field identification engine, and analyzing the data packet information according to the field identification engine to determine a field type; identifying a compliance check depth corresponding to the domain type based on the hierarchical mapping database, and loading a corresponding check rule cluster from an intelligent contract strategy library according to the compliance check depth; and performing compliance check according to the check rule cluster, and recording a compliance check state to perform blockchain upchain storage. The technical problems that in the prior art, the execution efficiency of a data compliance check process is low, and traceability cannot be achieved are solved, and the technical effects that the compliance check efficiency and accuracy are improved, and the check process is credible and traceable are achieved.

The invention discloses an intelligent question answering system and method for cross-domain knowledge reasoning, and relates to the technical field of intelligent question answering. In order to solve the problem that an existing intelligent question answering system is limited by technical shortages and cannot give satisfactory reply to a user in cross-domain question processing, the adopted scheme comprises the steps that a domain knowledge base construction module collects multi-domain knowledge data, and the multi-domain knowledge data is stored in a corresponding domain vector knowledge base after being preprocessed and vectorized; the user interaction module provides an interface, receives input, interacts and returns a result; the field identification module identifies the field and extracts key information by using a large language model; the knowledge retrieval module retrieves related information accordingly; the cross-domain knowledge reasoning module is used for reasoning a knowledge logic relationship of each domain and guiding a large language model to integrate fused knowledge; and the intelligent question-answering module generates prompt words, inputs the prompt words into the large language model to obtain a final response, and returns each result to the user interaction module for display. According to the method, knowledge from multiple fields can be processed, and the method is suitable for various different types of problems and scenes.

The invention discloses a transcriptomics spatial domain identification method, and belongs to the technical field of transcriptomics. The objective of the invention is to solve the problems of low data noise reduction precision and poor recognition effect of an existing spatial transcriptional spatial domain recognition method. The method comprises the following steps: firstly, obtaining an undirected neighborhood graph according to a gene expression matrix, obtaining embedded representation of the gene expression matrix by utilizing an encoder, obtaining a corresponding reconstruction matrix by utilizing a decoder, and further determining reconstruction loss; meanwhile, a ZINB model is used for fitting a reconstruction matrix, and a ZINB loss function is obtained; then, an augmented graph is constructed based on the undirected neighborhood graph, respective embedded matrixes are obtained through an encoder, the comparison loss of the undirected neighborhood graph and the comparison loss of the augmented graph are obtained through a comparison representation learning mechanism, and then the neighbor comparison loss is obtained; total target loss is obtained based on all losses, a joint optimization strategy is adopted for training, and after training of the whole model is completed, dimensionality reduction and spatial domain recognition are carried out on a generated reconstruction matrix.

The invention relates to the technical field of construction safety management, and discloses a construction engineering site construction monitoring management method and system, and the method comprises the steps: obtaining an original data set, carrying out the data preprocessing according to the original data set, obtaining a preprocessed feature data set, carrying out the sequential optimization of the preprocessed feature data set, and obtaining a feature data set; the method comprises the steps of optimizing a data uploading sequence to obtain an optimized data uploading sequence, performing time synchronization on the optimized data uploading sequence to obtain a unified time reference data set, performing spatial calibration on the unified time reference data set to obtain a spatially calibrated data set, and performing data clustering analysis on the spatially calibrated data set to obtain a data clustering result. And obtaining a region-level data deviation analysis result, and performing abnormal region identification according to the region-level data deviation analysis result to obtain an abnormal region report. According to the method, the abnormal region can be dynamically identified.

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

The invention discloses a computer sensitive data intelligent identification method based on artificial intelligence, and relates to the technical field of sensitive data identification, and the identification method comprises the steps: collecting different source data, and based on a multi-modal data fusion technology, integrating the different source data; extracting features based on a self-supervised learning technology; based on a deep learning model, model training is carried out, and sensitive data detection is carried out; a dynamic model updating mechanism is introduced, and online adjustment and optimization are carried out on the model; and identifying and classifying new data by using the trained model, and implementing a multi-level classification system. Multi-modal data fusion is provided, the multi-modal data fusion technology is utilized, different types of data are integrated into a unified structure, complementarity of information is ensured, meanwhile, a dynamic model updating mechanism is introduced, online adjustment and optimization are conducted on the model according to real-time data feedback, the method can adapt to data changes and emerging modes, and the method is high in practicability and easy to popularize. And the data detection is more accurate.

The invention relates to an abnormal transaction behavior recognition method and system based on artificial intelligence, and belongs to the technical field of financial transaction risk control, and the recognition method comprises the steps: collecting real-time transaction flow data, user behavior time sequence data and association graph data of a user, carrying out the space-time alignment processing, and generating a space-time aligned structured data set; dynamically calculating a transaction time attenuation factor; extracting a composite feature vector, inputting the composite feature vector into a pre-trained dual-channel decision model, and performing confidence weighted fusion on a dual-channel decision result based on a time decay factor to obtain a final risk score; dynamically adjusting a risk judgment threshold, and generating a dynamic decision boundary; judging whether the final risk score exceeds a dynamic decision boundary, if so, outputting an abnormal transaction behavior recognition result, and judging a transaction risk level; and triggering a risk disposal action corresponding to the transaction risk level. The method can improve the understanding depth of complex transaction behaviors, and reduces the false alarm rate of abnormal transaction recognition.

The invention relates to the technical field of big data analysis, and discloses a prospecting target area positioning method and system based on big data, and the method comprises the steps: collecting multi-source exploration data in real time through distributed nodes, completing coordinate normalization, semantic alignment and time synchronization through a spatial heterogeneous data flow engine, and generating a standardized incremental data block; performing local feature sensitivity analysis based on the historical model library, identifying a newly added feature dimension, and performing parameter increment updating by adopting a sliding window gradient descent method; inputting the updated model into a target evolution model driven by a Bayesian space-time probability field, and dynamically calculating the metallogenic probability of each space grid in combination with a stress field, an element migration path and historical verification data; and generating high, medium and low three-level target area maps according to probability sorting, and pushing the high, medium and low three-level target area maps to a three-dimensional visual decision terminal. According to the method, minute-level dynamic response of the target region under triggering of newly-added data is realized, computing resource consumption is reduced to be less than 5% of that of an original system, and prospecting efficiency and abnormal region identification timeliness are improved.

The invention discloses a medical image abnormal region identification method based on large model self-supervised learning, and belongs to the technical field of image data processing, and the method comprises the steps: obtaining an abnormal sample data set D1 and a health sample data set D2 of a medical image; constructing and training a discrete mask auto-encoder; generating a pseudo-anomaly graph for the samples in the D2 to form a data set D3; automatically generating labels for the D2 and the D3; and constructing a medical image abnormal region identification network and total loss, and training the network by using D2 and D3 to obtain an abnormal region identification model for identifying the abnormal region in the sample in D1. The discrete mask auto-encoder can enhance the perception ability of the model for structure distortion and semantic mutation, alleviates the problem of abnormal region overfitting reconstruction, the pseudo-abnormal graph can improve the diversity and effectiveness of training samples, the abnormal region recognition model has the pixel-level anomaly positioning ability, and the recognition accuracy of the abnormal region is improved. And the structural design is adaptive to complex medical image features, the training mechanism is flexible, and end-to-end optimization can be realized.

The embodiment of the invention provides a document abstract extraction method and system, electronic equipment and a storage medium, and belongs to the technical field of navigation. The method comprises the following steps: analyzing a document of which the abstract is to be extracted into fragments, and generating a fragment set; performing optical character recognition to form a preliminary semantic text, and determining a first token sequence; performing image type identification on the illustrations in the fragment set to form a structure description text, and determining a second token sequence; identifying an industry or field label of the document of which the abstract is to be extracted, and determining a corresponding code; and inputting the first token sequence, the second token sequence and the code corresponding to the tag into a pre-constructed abstract extraction model to obtain an abstract text. According to the image-text document abstract generation scheme based on the recurrent neural network, key semantics and key illustrations are extracted from enterprise documents through key semantics extraction, document structure extraction and document industry or field recognition, image-text document abstracts are formed, and the image-text document abstract generation scheme has pertinence and is high in generalization capacity.

The invention discloses an information extraction method and device based on a large model and electronic equipment, and relates to the field of data processing. The method comprises the steps of obtaining a to-be-recognized text; performing entity domain identification on the to-be-identified text to obtain a target entity domain, and performing relation domain identification on the to-be-identified text to obtain a target relation domain; based on the target entity domain and the target relation domain, the to-be-recognized text is input into a preset large model for matching, a target prompt template is obtained, and the preset large model comprises a plurality of prompt templates; and generating a corresponding target triple based on the target entity domain, the target relation domain and the target prompt template. By implementing the technical scheme provided by the invention, the accuracy of information extraction is improved through staged processing and dynamic prompt adjustment.

Systems and methods of processing domain-specific content in a generative AI system including receiving a prompt, tokenizing the prompt, identifying an identified domain of the tokenized prompt identifying domain-specific functions within the identified domain, generating domain-specific sub-functions from the domain-specific functions according to a hierarchical mapping, generating H-Tokens, each encapsulating one of a domain-specific function or a domain-specific sub-function and relationships domain-specific functions and the domain-specific sub-functions, implementing the of H-Tokens, assembling a response using the implemented of H-Tokens, and transmitting the response to the user.

The invention discloses a spatial domain identification method and device based on multi-modal topological consistency, and belongs to the field of transcriptome spatial domain identification, and the method comprises the steps: constructing a multi-layer network which is in one-to-one correspondence with modal information contained in a biological tissue based on spatial transcriptomics data of the biological tissue; extracting a consensus structure feature shared by the multi-layer network and a specific structure feature specific to each layer of network; constructing a cell consistency network of the biological tissue according to the consensus structural features, the specific structural features and the multilayer network; and performing clustering processing on the cell consistency network to obtain recognition results of different spatial domains in the biological tissue. According to the method, the heterogeneity problem among different modal data can be overcome, and the spatial domain recognition effect is better.

The invention relates to a customer service scene-oriented collaboration system and method, and belongs to the technical field of artificial intelligence. In the system, a shared memory storage center stores knowledge entries from a plurality of customer service agents in a predefined data structure; the shared memory storage center is provided with an access control mechanism associated with a visible domain identifier, and an access range is limited during retrieval through a logic partition; the memory management module receives candidate knowledge entries from each customer service agent based on a trigger event, determines visible domain identifiers for the candidate knowledge entries, associates the candidate knowledge entries with the visible domain identifiers, and stores the associated candidate knowledge entries and the visible domain identifiers in the shared memory storage center; the problem processing and decision-making module responds to an access request from any customer service agent and determines a corresponding visible domain identifier according to the identifier of the customer service agent; and executing access based on the visible domain identification and the input question, and retrieving knowledge entries with access from a shared memory storage center to realize cross-agent multiplexing.

The invention discloses a violation person identity recognition method for gas turbine power plant on-site safety supervision, relates to the technical field of person identity recognition, and aims at recognizing specific violation behaviors, positioning the occurrence time and space of the violation behaviors and marking corresponding behavior subjects. The method comprises the following steps: acquiring operation image data, extracting behavior subject image features and equipment acquisition data, generating fusion identity information, matching violation identities, outputting structured records, evaluating violation risk levels, verifying the identities of persons in charge and filing violation records, and automatically identifying dangerous behaviors in a gas turbine power plant through the operation image data acquisition and violation behavior identification model. And in combination with the dynamic attitude vector sequence and equipment acquisition data, generating identity feature fusion data, performing map matching, identifying the identity of a violation person, labeling through a risk level label, pushing to a responsible person terminal, completing identity confirmation and violation behavior recording and archiving, and realizing whole-process supervision and closed-loop management.

The invention provides a bad domain name identification method and system, and the method comprises the steps: obtaining domain name data, extracting a time sequence feature from the domain name data, generating a node feature matrix based on the time sequence feature, obtaining an attribute value of a domain name node in the domain name data, the multi-modal hypergraph is constructed, a multi-modal hypergraph association matrix is obtained based on the multi-modal hypergraph, and the multi-modal hypergraph is an association mode of the domain name node in different modals; and inputting the node feature matrix and the multi-modal hypergraph incidence matrix into a constructed classification model so as to identify the bad domain name based on the classification model. According to the method, by constructing the multi-modal hypergraph, enhancing explanation of the graph structure and aggregating the node feature matrix of the time information of the domain name nodes, the domain name information is used more comprehensively, and the recognition accuracy of bad domain names can be improved.

The invention discloses a product production process scheduling method and system based on intelligent manufacturing, and belongs to the technical field of intelligent manufacturing. According to the method, rapid local rescheduling is realized through fault influence domain identification, a three-level response strategy and real-time equipment health assessment. When an equipment fault signal is detected, a to-be-executed task on the fault equipment is identified, a subsequent task chain is traced through the technological process dependency relationship, the boundary of a fault influence domain is determined, and the influence domain relates to 10%-20% of tasks in the whole production plan. And selecting a corresponding response strategy according to the predicted repair time, adopting a task delay strategy when the predicted repair time is less than 30 minutes, performing local redistribution on tasks in the influence domain when the predicted repair time is less than 30 minutes to 2 hours, and expanding the influence domain to the whole process for comprehensive optimization when the predicted repair time is more than 2 hours. Meanwhile, monitoring data such as equipment vibration, temperature, current and rotating speed are collected to calculate a health degree score, and 5%-10% of productivity redundancy is reserved for high-risk equipment.

The invention discloses a deep learning-based rock hardness intelligent identification model and method, a DF (data fusion)-improved CNN (Convolutional Neural Network) model provided by the invention still keeps 97.92% stable accuracy under continuous cutting and different working conditions, and the problems of tedious artificial feature extraction engineering and weak model generalization in traditional rock hardness identification are solved. And the problems of insufficient characterization capability and weak model generalization performance of a traditional method are solved. Different from a feature screening process dominated by expert experience in a traditional mode, the method provided by the invention realizes feature adaptive extraction through a multi-channel convolutional neural network, carries out overlapped sampling data enhancement on an original vibration signal, constructs a time-frequency entropy multi-domain fusion graph through short-time Fourier transform, and obtains a time-frequency entropy multi-domain fusion graph; experimental verification shows that the classification accuracy of the multi-channel structure is greatly improved compared with that of a single-channel model, and time-frequency entropy multi-domain fusion is more accurate than that of a single-domain recognition model.

The invention discloses a typical bridge disease characterization method, and relates to the technical field of bridge detection, and the method comprises the steps: firstly constructing a degraded disease sample library, and building a surrounding environment model and a bridge BIM model; secondly, constructing a virtual bridge scene by using a UE5 engine, and obtaining a virtual disease sample; performing data enhancement by adopting an improved double-branch generative adversarial network in combination with real and virtual samples, generating a similar real degradation sample, and expanding real sample data; carrying out disease area identification and extraction on the detection image by using an improved Unet network trained by using real sample data; and finally, mapping an identification result to a BIM model to realize three-dimensional visual representation. According to the method, the problem of insufficient disease samples is solved through the generative adversarial network, the fine-grained disease recognition precision is improved by utilizing the improved Unet, full-process digital representation of bridge diseases from data acquisition, intelligent recognition to three-dimensional dynamic display is realized, and the accuracy and visualization effect of disease detection are remarkably improved.

The invention discloses a multi-modal tampering information detection method based on cooperation of a large model and a small model, and the method comprises the following steps: designing a prompt learning template for a large language model, and carrying out the field recognition and emotion analysis of an input image and text through the large language model according to the prompt learning template, generating tampering field guide information and an emotion analysis result; extracting semantic features and emotional features of the image and the text by using a small model, generating cross-modal semantic interaction features and emotional interaction features through a contrast learning loss and cross attention mechanism of manipulation perception, and mining inconsistent clues of cross-modal semantics and emotions; inputting the generated cross-modal semantic interaction features and emotional interaction features into a decoder, and generating a tampering region mask of the image or the text; and inputting the tampering field guide information, the tampering region mask and the multi-modal interaction characteristics into a large language model to generate a tampering category, tampering region identification and a reasoning basis of a tampering identification process.

The invention relates to a method for operating a speech dialogue system. First, a request (1) from a user (6) is detected via an interface of the speech dialogue system. Then, a domain recognition unit (8) is activated by a control logic (7) of the speech dialogue system, which domain recognition unit is configured to assign the detected request (1) to at least one domain (24). The request (1) is assigned to at least one domain (24) by means of domain recognition. Subsequently, the at least one domain (24) is associated with at least one language model (5) within the speech dialogue system using a decision matrix (3) of the control logic (7), each domain (24) being associated with at least one language model (5).The request (1) is then forwarded to the at least one corresponding language model (5), wherein a feedback (9) regarding the request (1) is generated based on information stored in the at least one selected language model (5). This feedback (9) is then output to the user (6).

The invention discloses a robust radio frequency fingerprint identification method based on Barlow Twins domain adaptation. Aiming at the problems of training and deployment environment distribution offset and cross-domain identification performance reduction caused by wireless channel multipath and time-varying characteristics in the prior art, the invention provides a domain adaptation framework combined with feature decoupling. The method comprises the following steps: firstly, converting a received time domain signal into a short-time Fourier transform spectrogram to reserve a time-frequency structure; then, a double-end convolutional encoder is constructed, bottom layer features are extracted through a shared backbone network, and device fingerprints and channel features are obtained in an identity branch and a channel branch respectively; in order to realize thorough decoupling of the two types of features, Barlow Twins-based independence regularization is introduced, and the identity features and the channel features are statistically orthogonal by minimizing a cross-correlation matrix of the identity features and the channel features, so that purer fingerprint features are obtained, channel interference is effectively eliminated, and generalization and recognition precision of the model under an unknown channel are remarkably improved.

The invention discloses a flood discharge gate structure dynamic load time domain rapid identification and dynamic response field inversion method comprising the following steps: 1) carrying out a vibration test of a flood discharge gate structure, and obtaining a multi-source vibration response signal of the flood discharge gate structure; 2) constructing a unit impulse response matrix H and a load shape function matrix N; 3) establishing an inverse analysis model X = HNA for dynamic load time domain identification of the flood discharge gate structure; 4) inversely solving the load shape function coefficient matrix A to obtain a dynamic identification load of the flood discharge gate structure; and 5) inputting the identification load as an external load of the sluice structure to obtain an overall dynamic response field of the sluice structure.The method is based on the multi-source actual measurement vibration response of the sluice structure, the improved dynamic load time domain identification method based on the shape function is adopted, the time domain load of the sluice structure can be rapidly and effectively identified, and the identification accuracy is high.

The invention provides a multi-field text-oriented hierarchical semantic understanding and intelligent question and answer generation method, which relates to the technical field of text understanding, and comprises the following steps of: preprocessing an input text, performing field identification through a multi-field text classification pre-training model, and calling a corresponding knowledge graph; a hierarchical semantic comprehension model based on a bidirectional long and short term memory neural network is adopted to obtain hierarchical semantic representation of a text, entity and relation attention calculation is performed on a knowledge graph in combination with a multi-hop reasoning method of a graph attention network to extract related knowledge paths, and finally question and answer pairs are generated based on a bidirectional decoding strategy. Through multi-domain knowledge integration and hierarchical semantic understanding, the accuracy and generalization ability of the intelligent question and answer system are improved, and question and answer requirements of different domains can be met.

The invention provides a wild animal image cross-domain identification method based on a fusion perception adversarial network, and belongs to the technical field of image identification. The method comprises the following steps that ResNet50 is adopted as a backbone network to extract image bottom layer features, and semantic information of the image features is obtained by a model; an adversarial learning strategy is adopted to realize extraction of domain invariant features by the model; introducing a maximum mean value difference constraint between the fully connected layers, and expanding the domain invariant feature extraction capability of the adversarial network under a complex background; domain invariant features are extracted through a two-layer domain adaptation strategy, positive migration of the model is enhanced, and cross-domain recognition of wild animal images is achieved; compared with the prior art, the method effectively improves the processing efficiency of large-scale image data of wild animals and the recognition accuracy of wild animal images in different environments.

The invention provides a training data generation method, electronic equipment, a storage medium and a program product, and the method comprises the steps: obtaining domain identification information of a target domain, and generating system-level prompt information related to the target domain; inputting the system-level prompt information into the large language model after alignment training is completed, and driving the model to generate an input instruction set related to the target domain; based on the input instruction set, generating a response set semantically related to the input instruction set, thereby forming a first training data set; constructing multi-round dialogue training data with semantic coherence and rich context for each instruction-response pair in the first training data set through a multi-round dialogue extension generation mode; and summarizing the multi-round dialogue training data to construct a second training data set. The training data generated by the method does not depend on manual prompting engineering, expert writing or seed instruction presetting, can quickly adapt to business requirements in different fields, and has excellent universality, mobility and cross-field expansibility.

The invention discloses a spatial transcriptome data spatial domain identification method based on multi-space self-supervised contrast learning, and the method comprises the steps: carrying out the modeling to generate a spatial neighborhood graph, keeping the graph structure unchanged, disorganizing node features, and carrying out the data enhancement, thereby obtaining an enhanced graph; constructing an encoder based on a graph neural network, extracting spatial transcriptome data fused with spatial information and gene information to obtain potential embedding, and sending the potential embedding into a multi-space generator to generate multiple groups of rich graph feature representations; fusing graph feature representation and potential embedding to obtain refined representation, reconstructing a gene expression matrix through a decoder, and adding contrast learning loss and reconstruction loss as a total objective function; and updating network parameters by adopting an Adam optimizer according to the obtained total objective function to complete spatial transcriptome spatial domain identification. Spatial transcriptome data are fully mined from global and local angles, and accurate spatial domain identification is realized.