1306 results about "Informatics" patented technology

The invention provides a technical condition prediction method for a tunnel civil engineering structure, and relates to the technical field of traffic control monitoring systems.A biological acoustic composite sensing system comprising a distributed optical fiber acoustic sensing system and microorganism sample collection and analysis is constructed, and acoustic depth mode characteristics are mined by applying a deep learning model; the microbial dynamic biomarker is analyzed and identified by using bioinformatics; multi-modal information, a domain knowledge graph and a causal inference algorithm are fused, and a dynamic causal network model for revealing an internal driving relation in the degradation process is constructed; further, training and applying an adaptive neural network prediction model based on causal driving and fusing physical and biochemical mechanism constraints, and carrying out probabilistic prediction on future technical conditions of the tunnel; and executing anti-fact reasoning by using the causal network and the prediction model, and generating and optimizing an intervention strategy of cooperation of physical measures and biochemical measures. According to the method, the accuracy and advance of tunnel structure state prediction can be remarkably improved.

The invention discloses a knowledge graph-based learning path design system, and belongs to the technical field of learning path design, and the knowledge graph-based learning path design system comprises a knowledge graph construction module which is used for collecting, sorting and representing knowledge in different fields from various sources, and constructing a knowledge graph model; the system comprises a learning module which is used for integrating knowledge levels, interests, preferences and learning style information of users into a coherent knowledge system, a user modeling module which is used for understanding and representing the knowledge levels, interests, preferences and learning style information of the users, and a learning path planning module which is used for generating personalized learning paths according to the information provided by the user modeling module. According to the method, multiple aspects of knowledge graph construction, user modeling, learning path planning, learning resource management, learning monitoring and the like are covered, and through cooperative work of the modules, the system can provide efficient, personalized and comprehensive learning path design, so that the experience and learning effect of learners are remarkably improved.

The invention relates to the technical field of chemical engineering and machine learning, and provides a method for predicting the solubility of carbon dioxide in a deep-eutectic solvent based on a machine learning model, and the method comprises the steps: collecting CO2 solubility data, carrying out the coding and feature descriptor conversion of a deep-eutectic solvent molecular structure through an open-source chemoinformatics software package RDKit, and constructing a data set. Four algorithms of XGBoost, random forest, a deep neural network and a convolutional neural network are adopted to construct a model, and hyper-parameters are optimized through grid search and k-fold cross validation. After the model is trained, the prediction performance is evaluated, and the optimal model is selected for CO2 solubility prediction. The method improves prediction efficiency and accuracy, has strong adaptability and generalization ability, and provides an effective tool for formula optimization of the eutectic solvent and improvement of CO2 capture efficiency.

The invention relates to a polyp image segmentation method based on pyramid vision Transform, and belongs to the technical field of digital medical treatment. The method comprises the following steps: capturing global context information through a pyramid vision Transform module, and obtaining feature maps with different resolutions from a plurality of extraction stages; detail information is extracted from the shallow feature map of the first stage through a cyclic cross attention module, and shallow features are obtained; a multi-scale feature fusion sensing module is used for carrying out fusion processing on the feature maps extracted in multiple stages, deep features are extracted, and the deep features and shallow features are fused and then used for image segmentation; and designing weighted binary cross entropy loss and weighted IoU loss to train the above process, and segmenting the polyp image after training is completed. According to the invention, through the pyramid vision Transform module, the cyclic cross attention module and the multi-scale feature fusion perception module, the global information learning ability, the detail information capturing ability and the segmentation performance are significantly improved.

The invention relates to the field of medical informatics, in particular to an anesthetic dosage optimization method based on artificial intelligence. Comprising the following steps: collecting real-time physiological data and static data of a patient, converting the real-time physiological data and static data of the patient into standardized feature vectors through standardization processing, and predicting the initial infusion rate of an intraoperative anesthetic by using a long-short-term memory network; collecting and standardizing the intraoperative data of the patient to obtain the standardized intraoperative data; a personalized anesthetic infusion rate is generated through a dynamic metabolism risk and nerve sensitivity joint optimization algorithm; the maximum allowable infusion rate and the minimum effective infusion rate are determined based on a clinical guide, the remaining time of an operation is obtained, safety verification is carried out, and the final infusion rate is generated in combination with the remaining time of the operation. The technical problem that the prediction of the initial infusion rate of the preoperative anesthetic is inaccurate due to the fact that the initial infusion rate is determined by depending on the experience of an anesthetist in the prior art is solved.

The invention discloses a digital intelligent teaching recording and broadcasting system and method, and relates to the technical field of data encryption, and the method comprises the steps: collecting teaching videos and audios of a teacher through a high-definition camera and an audio device, carrying out the preprocessing, obtaining the course content, recognizing the key information of the course content through a natural language processing NLP and computer vision technology, and carrying out the recording and broadcasting. The method comprises the following steps: segmenting a video into single knowledge point fragments, performing classified storage, forming a learning resource library, collecting basic information and learning habit data of students, constructing a user portrait, matching learning resources, extracting the learning habit data in the user portrait, performing feature extraction, and obtaining a comprehensive feature vector; constructing a learning state evaluation model by using a convolutional neural network to comprehensively analyze the comprehensive feature vector to obtain a learning state comprehensive score, formulating a playing strategy according to the learning state comprehensive score and matched learning resources, regularly testing knowledge points of students, evaluating the mastering degree of the students, and updating a user portrait according to a result.

The invention relates to a biological information MCP service calling method, system and device and a medium, and relates to the technical field of bioinformatics and artificial intelligence. The method comprises the steps of obtaining user request information; querying MCP service information matched with the user request information based on the user request information; building enhanced prompt information corresponding to the MCP service information which contains the user request information and is matched with the corresponding user request information, and sending the enhanced prompt information to the large language model to generate calling parameters; and calling the target biological information MCP service based on the calling parameter. The automatic discovery of the MCP service is realized, and the expandability and the flexibility of the system are improved; the user intention can be more accurately understood, and the tool discovery accuracy is improved; an MCP specification is used for assisting LLM to accurately generate calling parameters, and the use threshold of a user is lowered; interoperability and expandability are ensured based on an MCP protocol; and a large-scale biological information MCP service scene is effectively supported.

The invention belongs to the field of bioinformatics, and relates to a molecular property prediction method based on multi-modal gating and comparative learning, which comprises the technologies of comparative learning, graph neural network, cross-modal alignment, gating attention and the like. Firstly, data standardization and graph construction are carried out, and molecular fingerprint embedding is extracted; secondly, a heterogeneous dual-channel graph coding architecture is adopted, one path captures atom short-range interaction through an attention mechanism, the other path integrates a molecular global structure and long-range dependence, and complementary molecular representation is generated; then, a cross-modal attention mechanism is introduced, bidirectional association of graph and fingerprint features is achieved, and modal weights are adaptively and dynamically distributed through a gating fusion module; and finally, a comparison pre-training strategy is adopted, a molecular graph and fingerprints are utilized to construct a sample pair, and discriminative molecular representation is learned on unlabeled data. According to the method, the accuracy of molecular property prediction is remarkably improved, and an efficient and reliable calculation tool is provided for virtual drug screening and lead compound optimization.

The invention relates to the field of medical care informatics, in particular to an ophthalmic disease prediction model training method based on artificial intelligence. Comprising the following steps which are executed in sequence: S1, acquiring a data set of ophthalmic diseases, and performing disease labeling on each image in the data set; s2, performing data expansion on the labeled data set to obtain a training data set of ophthalmic diseases; s3, performing feature extraction on the training data set of the ophthalmic disease by using a neural network algorithm based on dynamic weight redistribution as a feature extraction model to obtain extraction features of the ophthalmic disease; s4, using a self-encoding neural network based on feature refinement as a feature dimensionality reduction model to perform dimensionality reduction on the extracted features, and obtaining initial low-dimensional features of the ophthalmic disease after dimensionality reduction; s5, inputting the initial low-dimensional features of the ophthalmic diseases after dimension reduction into a classifier model for training, and obtaining a classification result; s6, classifying the data set by using the trained model; features of ophthalmic disease data are effectively captured.

The invention provides a medical diagnosis system based on chain reasoning and multi-modal fusion and application thereof, and belongs to the technical field of medical care informatics. The system comprises a data preprocessing module, a data fusion module, a model reasoning module and an output module, and can perform deep processing and intelligent analysis on multi-modal medical data (including text data, medical image data and structured data) of a patient. And the output module provides a diagnosis result and a detailed reasoning process, so that the interpretability and the credibility of the diagnosis result are improved. Through multi-modal data fusion, a chained reasoning mechanism and optimized loss function design, the accuracy, generalization ability and interpretability of disease diagnosis are remarkably improved, the limitation of the traditional technology in the aspects of single-modal analysis and black box reasoning is broken through, transparent, efficient and intelligent diagnosis decision support is provided for clinicians, and the clinical application prospect is wide. And intelligent and precise development of medical diagnosis is promoted.

The invention relates to the technical field of medical informatics, and discloses an intestinal flora associated disease risk prediction system based on multi-data set difference mutual identification, and the system comprises the steps: capturing the dynamic evolution characteristics of a network through constructing a species-genus-phylum three-level microbial interaction network, and employing a time sequence diagram convolution network; and designing a network disturbance experiment, introducing a network recovery force index to quantify the network recovery capability, constructing a network vulnerability map to identify key nodes and analyze a disturbance propagation path, and integrating stable and consistent network toughness characteristics in multiple data sets to establish an early warning mechanism. By means of the method, early and accurate early warning of disease risks is achieved, and a key time window is provided for clinical intervention.

The invention relates to the field of bioinformatics and drug development, and discloses an efficient siRNA effectiveness prediction method and system based on an XGBOOST algorithm. The method comprises the following steps: acquiring a training data set containing siRNA sequence information, chemical modification information and corresponding validity data; carrying out cleaning, feature extraction and data balance processing on the data; training the prediction model by using an XGBOOST algorithm, and optimizing hyper-parameters; performing model evaluation and optimization through cross validation; a new siRNA sequence is input for effectiveness prediction; explaining a prediction result by using an SHAP value analysis technology; the actual interference efficiency of the efficient siRNA sequence is verified through an in-vitro cell experiment, and the model is further optimized based on an experiment result. According to the method, the accuracy and efficiency of siRNA effectiveness prediction are improved, the influence of chemical modification is comprehensively considered, the problem of data imbalance is solved, the model interpretation is enhanced, and the calculation efficiency is remarkably improved.

The invention discloses a solar wind speed prediction method based on a fusion prediction mode and a collaborative attention mechanism. The method comprises the following specific steps: collecting and processing solar wind speed and related data within a certain time; dividing the data into a plurality of patches through a patterning segmentation method, learning a plurality of modes in prediction information, and guiding mode fusion by using historical information to obtain fused prediction mode information; learning historical information and fused prediction mode information by using an attention mechanism to obtain time sequence feature representation, aggregating global information through down-sampling and the attention mechanism, and learning inter-variable relation feature representation of variable dimensions for the global information by using a collaborative attention mechanism; mapping the relationship feature representation among the variables through a flat layer to obtain a solar wind speed prediction result; and training and optimizing the model. According to the method, prediction mode information can be fully mined so as to realize interaction between historical information and prediction information, meanwhile, the relation strength between variables can be captured, and the prediction precision is improved.

The invention relates to a method for dividing cell types based on single-cell multi-omics data, and belongs to the technical field of bioinformatics. The problems of high noise and high sparsity formed by a common technology are solved. Comprising the following steps: step 1, constructing a deep neural network model, step 2, obtaining a single-cell multi-omics comprehensive data set, step 3, preprocessing the single-cell multi-omics comprehensive data set, step 4, training the deep neural network model based on the preprocessed single-cell multi-omics comprehensive data set, and step 5, training the deep neural network model based on the preprocessed single-cell multi-omics comprehensive data set. And performing cell type division on to-be-detected data based on the trained deep neural network model. According to the method, an scDRMAE model is provided, dependency relationships of different omics are captured through two parallel MAE modules, factors which are usually ignored are considered in cell clustering, interpolation of different omics data is realized by means of a mask prediction mechanism in the cell clustering, and finally, the omics data are effectively fused through an attention mechanism, so that the accuracy of cell clustering is improved. And the accuracy of model cell clustering is improved.

The invention provides a mass spectrum structure analysis method and device, a storage medium and a program product, and relates to the technical field of bioinformatics, and the method comprises the steps: calculating the similarity between each reference mass spectrum in a reference database and a to-be-analyzed target mass spectrum, screening at least one similar reference mass spectrum from the reference mass spectrums according to the calculated similarity; the reference database comprises a plurality of reference mass spectra and corresponding reference molecular structures; according to a preset mass spectrum structure analysis task instruction, the target mass spectrum, the similar reference mass spectrums and the corresponding reference molecular structures, generating prompt information; and inputting the prompt information into the large language model to determine a target molecular structure corresponding to the target mass spectrum. Therefore, a relatively good mass spectrum structure analysis effect is realized by combining the advantages of database retrieval and deep learning by utilizing the few-sample learning capability of the large language model.

The present disclosure relates to a 5G communication system or a 6G communication system for supporting higher data rates beyond a 4G communication system such as long term evolution (LTE). A method of transmitting or receiving a signal by a user equipment (UE) in a mobile communication system is provided. The method may include: identifying a neural network model for transmitting first information to a base station (BS); learning a connection weight of the neural network model using the first information; transmitting, to the base station, second information for updating a weight of a second partial neural network corresponding to the base station based on a result of the learning; and updating a weight of a first partial neural network corresponding to the UE based on the result of the learning.

The invention relates to the technical field of medical care informatics, and discloses a laryngeal mask ventilation control method suitable for outpatient anesthesia, which comprises the following steps: before core analysis, injecting and monitoring a high-frequency signal to confirm the integrity of an original breathing waveform data stream; constructing a real-time ventilation waveform trajectory in a differential phase space only on the premise that a data stream is complete, and performing morphological comparison with an individualized baseline template; meanwhile, when a parameter adjustment event of the respirator is monitored, the baseline template is automatically re-calibrated, a collaborative analysis framework with front information source quality inspection and dynamic reference self-adaption capabilities is constructed, ventilation monitoring is converted from passive response to an isolated peak point into active insight to a system dynamics evolution trajectory, and the system dynamics evolution trajectory is optimized. Therefore, the risk of gradual deterioration of the ventilation state caused by tiny air leakage of the laryngeal mask or secretion accumulation can be clinically recognized in an early stage, and intervention time is won for anesthetists.

The invention discloses a metabonomics data batch correction method based on multi-kernel learning, and belongs to the cross technical field of bioinformatics and analytical chemistry. According to the method, the multi-kernel learning technology is utilized, the advantages of different kernel functions are fused in a self-adaptive mode, a model conforming to data reality is constructed, complex drift characteristics of metabolite signals are accurately captured, and efficient and accurate normalization processing of metabonomics data is achieved. Compared with traditional data standardization methods such as SVR and LOESS, the method has the advantages that the performance is excellent in the aspect of reducing the metabolite peak intensity variability, and the data stability is remarkably improved. In the subsequent multivariate statistical analysis, the classification accuracy is greatly improved, the comparability among different batches of data is also remarkably enhanced, reliable data support can be provided for discovery of disease biomarkers, and the method plays a key role in large-scale metabonomics research.

To increase the amount of information to be utilized.SOLUTION: An information processing system includes an information processing apparatus, a first terminal device that holds first user characteristic information, and a second terminal device that holds second user characteristic information. The information processing apparatus includes: an information collection unit which collects, from the first terminal device, first anonymized information generated by anonymizing the first user characteristic information, and collects, from the second terminal device, second anonymized information generated by anonymizing the second user characteristic information; a learning unit which generates a model configured to learn, by machine learning, a relationship between the first user characteristic information and the second user characteristic information, using the first anonymized information and the second anonymized information, as learning data, and output, on receipt of the first user characteristic information, estimated user characteristic information estimated from the relationship between the first user characteristic information and the second user characteristic information; and a model output unit which outputs the model to the first terminal device.SELECTED DRAWING: Figure 3

The invention relates to the technical field of medical care informatics, and discloses an intelligent post-stroke cognitive impairment assessment method based on brain network dynamic reconstruction, which comprises the following steps: acquiring multi-modal brain image data of a patient, dynamically compensating the difference between haemodynamic delay and white matter conduction velocity through a nerve-blood vessel coupling model, and calculating the post-stroke cognitive impairment; precise space-time alignment of function and structure signals is realized; a pulse neural network dynamic entropy change model is constructed based on the alignment data, functional connection strength change is coded into a pulse distribution sequence, and Gamma wave band synchronization enhancement and other compensatory features are extracted; constraining rationality of brain network reconstruction by adopting an energy optimal transmission model, and generating a compensatory thermodynamic diagram; and real-time calculation of the edge end is realized through pulse time sequence compression coding. The limitation of traditional static analysis is avoided, the algorithm achieves microsecond response on embedded equipment through the pulse coding technology, and a reliable stroke rehabilitation evaluation tool is provided for primary hospitals.

The invention relates to a personalized scientific education system based on AI agent driving, and belongs to the technical field of informatics. The education system runs a main agent, a user twinborn agent, a learning planning platform and a graph structure database. The main agent is used for collecting knowledge states and learning behaviors of users, constructing a user model, and training one or more user twin agents based on the user model. And the user twinborn agent is used for simulating learning behaviors and results of the user under different learning paths. And the learning planning platform combines the user model and the knowledge graph stored in the graph structure database to generate a plurality of candidate learning paths for the twin intelligent agent of the user to perform analogue simulation. And the main agent selects an optimal path according to a simulation result and implements the optimal path to the personalized teaching process of the user. According to the technical scheme, high-adaptability learning path recommendation and dynamic adjustment are realized, and the intelligent level of individualized teaching is improved.

The invention relates to the technical field of bioinformatics, and provides a visual analysis method and system for a rice multi-tissue single cell expression profile. The method comprises the following steps: comparing sequencing data of an original single cell transcriptome of a rice tissue to obtain a standardized transcriptome data set; performing batch effect correction and integration on the standardized transcriptome data set to obtain a whole plant expression matrix; performing cell type annotation on the whole plant expression matrix to obtain a cell type annotation system; carrying out visual dimension reduction processing on the whole plant expression matrix fused with the cell type annotation system, and carrying out co-expression network construction to obtain a modular tissue correlation analysis model; and establishing an interaction end based on the module organization correlation analysis model, and realizing data visualization analysis through the interaction end. The invention provides a one-stop analysis platform for rice cell heterogeneity research, functional gene mining and molecular breeding.

The invention relates to an oncogene prediction method based on graph variation self-coding, and belongs to the field of bioinformatics. The method is based on a dual-path neural network framework: a main path processes an original network and features enhanced by a variational auto-encoder (VAE) by using a graph attention network (GAT) so as to capture a complex relationship between nodes; the auxiliary path generates an auxiliary network and features containing global information through an APPNP algorithm, and the auxiliary network and features are aggregated through GraphSAGE to retain structural information. The model introduces jump connection and residual connection to relieve gradient disappearance and enhance feature complementarity. And finally, integrating dual-path information output prediction through a linear layer. The method is verified on a plurality of biological network data sets, the prediction accuracy, robustness and hidden relation recognition capability are remarkably improved, and a reliable tool is provided for cancer research.

The invention relates to the technical field of bioinformatics, in particular to a gene pool data retrieval method and system and a storage medium. The method comprises the following steps: acquiring multi-source genome data, performing quality control on the multi-source genome data based on an FAIR principle, and performing efficient storage and indexing by utilizing a distributed storage system so as to obtain a distributed genome database; performing cross-species homology analysis on the distributed genome database, and constructing a multi-level evolutionary tree model, so as to obtain evolutionary relationship map data; mapping gene structures and functions among different species according to the evolutionary relationship map data so as to obtain cross-species gene mapping data; receiving retrieval query data input by a user; and performing query type identification on the retrieval query data to obtain optimized query data. Through the multilevel evolutionary tree model and cross-species gene mapping, gene function comparison and prediction among different species are realized.

The invention belongs to the field of bioinformatics, and relates to an improved integrated deep learning cell communication ligand-receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and dimensionality reduction on biological sequence features of a ligand and a receptor, and constructing multi-modal feature input; secondly, constructing an improved deep neural network branch, introducing a batch normalization layer and a Leaky ReLU activation function, solving the problems of gradient disappearance and neuronal necrosis, and improving regularization strength to prevent overfitting; meanwhile, an enhanced heterogeneous graph auto-encoder branch is constructed, the graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the prediction probability of a heterogeneous graph auto-encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a complex cell communication network.

The invention belongs to the field of bioinformatics, and relates to a molecular property prediction method based on local graph features and global relationships, which comprises the technologies of deep learning, comparative learning, graph neural network and the like. Firstly, data processing is carried out, input data is prepared, and initial representation of a molecular map is constructed. Secondly, extracting local features of the molecular map through a local topological structure feature network, and focusing on local information of atoms in molecules and adjacency relations of the atoms; secondly, performing global feature extraction on the molecular graph by utilizing a global attention network, and capturing a global dependency relationship and long-range interaction among molecules; and finally, combining the local features and the global features through feature fusion, and performing final prediction. According to the method, through a double-branch network architecture, details of local structures in molecules can be concerned, and global interrelationships among the molecules can be captured, so that more comprehensive and accurate feature representation is provided for prediction of molecular attributes.

The invention relates to the technical field of gene analysis bioinformatics, and particularly discloses a gene data analysis method based on a large language model.The method comprises the steps that the quality of gene data to be analyzed is analyzed, whether the gene data to be analyzed is available or not is judged, and a gene sequence segmentation scheme of the available gene data is determined; inputting the gene data after gene sequence segmentation into the large language model to obtain a gene data association feature data set and a gene data analysis result, determining a gene data analysis accuracy feature value, and judging whether the gene data analysis result conforms to expectation or not; the problems that quality and integrity problems of traditional biological data challenge the accuracy of analysis results and effective integration and analysis of multi-modal data still need to be improved are solved, the data processing efficiency is optimized in the whole process, repeated calculation, misjudgment and invalid results are reduced, the analysis efficiency is improved, and resource waste is reduced.

The invention relates to the technical field of bioinformatics and computational biology, and provides an anti-cancer drug target screening method and system based on hypergraph-knowledge graph dual-channel, a hypergraph-knowledge graph dual-channel architecture is constructed through a feature extraction module, a self-adaptive multi-modal fusion module dynamically integrates multi-source heterogeneous data, and the anti-cancer drug target screening method and system based on the hypergraph-knowledge graph dual-channel architecture are obtained. The method comprises the following steps: adaptively fusing high-order interaction features and semantic association features of a gene, generating a high-quality negative sample by using a generative adversarial network to optimize a training process, finally optimizing feature representation through a discriminator, and calculating a synthetic lethal probability score between any gene pair. According to the application, firstly, the extracted multi-dimensional features are normalized through the feature extraction module, then the importance of different features is dynamically weighted based on the attention mechanism, finally, the synthesis lethal relationship prediction probability of the gene pair is output through the multi-layer sensor, and under the theoretical framework of the gene synthesis lethal effect, the synthesis lethal effect of the gene pair is predicted. And an innovative solution is provided for anti-cancer drug target screening.

The invention provides an environment art design major online education learning path recommendation system, and relates to the technical field of online education, and the system comprises a user information obtaining module which is used for obtaining the basic information, learning background, interest preference and learning target of a user; the course resource database module is used for storing various online course resources of the environmental art design major, including course videos, teaching courseware, practice items and case analysis; and the learning behavior analysis module is used for analyzing behavior characteristics of the user in the learning process, including learning styles, learning habits and content preferences, and acquiring a learning behavior mode of the user. According to the method, the user information is comprehensively acquired, the learning behaviors are analyzed, the personalized learning path is determined, and real-time evaluation and adjustment are performed, so that the learning efficiency, satisfaction and achievement of the user are effectively improved.

The invention provides a spatial domain identification method based on data interpolation and cell type deconvolution, and belongs to the technical field of bioinformatics. In order to solve the problems that gap information between adjacent points cannot be utilized in low-resolution spatial transcriptome data and prior information of cell types in a tissue space structure level cannot be fully integrated in a traditional method, the method comprises the following steps: acquiring a spatial transcriptome data set and a single-cell RNA sequencing data set, and performing data preprocessing on the acquired data sets; and carrying out data interpolation on the preprocessed spatial transcriptome data, and carrying out cell type deconvolution in combination with single-cell RNA sequencing data. And constructing a deep learning model based on the graph convolutional network. And training a deep learning model according to gene expression information, spatial position information and cell type information of the spatial transcriptome data after cell type deconvolution by using a self-supervised contrast learning strategy. And performing spatial domain identification on the to-be-detected data based on the trained model.