166 results about "Expression data" patented technology

A system and method for processing genomic data using dynamic latent manifolds that transforms multi-modal genomic datasets into geometric representations within a curved manifold space. The system receives genomic datasets including DNA sequences, genetic variants, and expression data, then extracts biological features and assesses importance using trained neural networks. Manifold curvature values are computed based on biological significance, and genomic data is embedded as geometric structures where semantic relationships are represented through distance and curvature properties. The system generates compression pressure fields that influence processing decisions and computes optimal geodesic paths through the manifold to minimize cognitive action functionals. Adaptive compression rates are determined for different genomic regions based on geometric properties and biological importance. The manifold structure evolves through use, strengthening frequently accessed pathways while applying thermodynamic decay to unused concepts. The system supports hierarchical organization across biological scales, reversible navigation, and federated learning capabilities that enable privacy-preserving collaboration.

The invention discloses an old people abnormal expression recognition method based on gradient covariance analysis. The method comprises the steps of old people expression data set construction, time sequence optical flow feature-based facial expression region screening, abnormal expression enhancement loss estimation, gradient covariance-based facial expression region feature enhancement, micro-expression model training and micro-expression model testing. Aiming at the problems that the expression movement of the elderly is not obvious and the abnormal expression is easy to neglect, the facial expression key region is positioned by using the time sequence optical flow feature, the abnormal expression is introduced to enhance the loss so as to punish the leak detection condition, the back propagation gradient of the loss and the covariance thereof are calculated, and the region with larger covariance has more obvious feature change, so that the detection accuracy is improved. According to the method, facial expression region features based on gradient covariance are fully considered, key region analysis is enhanced, and the accuracy of abnormal expression recognition of the old people is effectively improved.

The invention relates to the technical field of language barrier evaluation, and discloses a deep learning-based language barrier executive intervention effect evaluation method. The method comprises the following steps: acquiring real-time voice data and facial expression data of a language barrier patient in intervention training through a multi-modal data acquisition device to form an original behavior feature set; performing acoustic feature hierarchical analysis on the voice data by adopting a time sequence feature extraction network to generate a voice time sequence feature vector; performing micro-expression dynamic capture on the expression data through a three-dimensional convolutional neural network to generate an expression state feature vector; inputting the two types of vectors into a multi-modal feature fusion layer to carry out cross-modal correlation analysis, and generating a comprehensive behavior evaluation matrix; on the basis of the matrix, an intervention effect analysis model driven by an attention mechanism is adopted, a behavior improvement degree index of the current intervention stage is calculated, accurate evaluation of the intervention effect is achieved, and support is provided for dynamic adjustment of language barrier rehabilitation intervention.

The invention relates to the technical field of biological information, in particular to a brain glioma microenvironment formation key molecular mechanism analysis method, which comprises the following steps: calling expression data to analyze a cell marker sequence to construct a segmentation interval, calculating a candidate factor expression direction to judge trend consistency, identifying expression aggregation difference to construct a split node section, and constructing a subsection; according to the method, partitions are constructed on the basis of marker expression syn-position, judgment is carried out in combination with candidate factor expression directions and marker trends, factor collaboration features are constructed according to the number of trends consistent times, and the semantic sorting information is generated by analyzing the channel change trends to generate drift scores, evaluating multi-label output stability screening key factors and analyzing literature word order positions. Identifying and expressing an aggregation and split structure, guiding a functional pathway to perform trend analysis in a scoring interval and construct a dynamic trajectory, judging label stability and empowerment according to pathway scoring difference, and determining a factor semantic position in combination with a literature word order structure to realize integrated support of regulation and control information and literature evidence.

The invention provides a disease marker epitope prediction and antibody screening method, which belongs to the technical field of disease markers, and comprises the following steps: firstly, establishing a training data set containing a known antigen-antibody compound structure and disease tissue expression data; and obtaining target protein sequence information through liquid chromatography-mass spectrometry analysis and carrying out sequence comparison. And then a deep convolutional neural network is utilized to extract sequence features, and surface exposure sites are identified by combining secondary structure prediction and solvent accessibility analysis. After the features are integrated with sequence evolution conservative properties, a prediction model is constructed by using a random forest classifier. The method comprises the following steps: carrying out molecular dynamics simulation on a prediction result, screening first 10% of candidate sequences through a comprehensive scoring function and K-means clustering, and finally determining an antigen epitope sequence with the strongest binding activity through verification of an antigen chip and a fluorescence labeled antibody system, so that the technical problem that specific antigen epitopes are difficult to accurately predict and recognize in the prior art is solved.

The invention relates to the technical field of biomedicine, in particular to a method for constructing a plasma ctDNA organ distribution characteristic spectrum of advanced colorectal cancer. The method comprises the following steps: collecting a peripheral blood sample at multiple time points, separating plasma by adopting a double-centrifugal method, and extracting circulating tumor DNA (Deoxyribose Nucleic Acid); carrying out whole exome sequencing based on ctDNA to obtain genome variation information and calculating variation allele frequency, and synchronously detecting the expression quantity of immune-related proteins by adopting an Olink proteomics technology; integrating the genome data, the protein expression data and the clinical pathological features, and constructing a multi-dimensional feature data matrix; and taking the organ metastasis condition confirmed by iconography as a supervision label, training a model by applying a machine learning algorithm, screening key prediction factors, constructing a quantitative prediction model, and finally generating a visual organ metastasis tendency prediction map. According to the method, early and accurate prediction of the advanced colorectal cancer organ metastasis tendency is realized through multi-omics data collaborative analysis and machine learning modeling.

Provided is a method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing, which method comprises the following steps: S1, sample preparation; S2, construction of a single-cell expression matrix; S3, cell quality control; S4, cell type annotation; S5, cell communication analysis; and S6, co-expression network analysis. The provided method for screening myocardial therapeutic targets for ischemic heart failure by using single-cell sequencing comprises performing single-cell sequencing on hearts of healthy mice and IHF mice, screening for cell types with significant differences in cardiac transcriptional profiles of the healthy mice and IHF mice, then exploring interaction characteristics of various types of cells in malignant fibrotic IHF hearts, revealing potential regulatory modules and pathways related to malignant myocardial fibrosis in single-cell expression data of IHF hearts, and performing screening to obtain Pdgfb and Tnfsf12 genes which can be used as therapeutic targets for treating myocardial fibrosis in ischemic heart failure.

The invention discloses a spatial transcriptome data analysis method based on artificial intelligence, and belongs to the technical field of spatial transcriptomics data analysis. Firstly, self-adaptive normalization and hypervariant gene screening preprocessing are carried out on original gene expression data; then constructing a hierarchical map integrating spatial proximity and transcription similarity, and ensuring the connectivity and robustness of the map through a dynamic radius pruning and neighborhood inheritance strategy; dividing positive and negative sample sets based on the atlas, and inputting a type modulation contrast graph auto-encoder for training; and finally, spatial domain identification and downstream function analysis are completed based on the low-dimensional potential representation or reconstructed gene expression matrix output by the model. The method effectively improves the accuracy and stability of spatial domain recognition, adapts to multi-technology-source data, enhances the biological interpretability of model output, and can be widely applied to biomedical scenes such as tumor microenvironment analysis and organ development research.

The invention discloses a mathematical formula identification coding method, and particularly relates to the technical field of formula coding. The method comprises the following steps: acquiring mathematical formula image data to be identified, and performing symbol boundary extraction and preprocessing to generate symbol feature expression data; and performing visual spatial layout analysis and symbol type semantic classification based on the symbol feature expression data to generate formula layout structure data and symbol semantic classification data. And through graph structure analysis based on a topological relation, determining an inter-symbol topological relation of the mathematical formula, and generating symbol topological relation data. And deriving a dimension constraint relationship and an operator dependency relationship between symbols through a mathematical meta-knowledge mining technology, and generating mathematical meta-knowledge constraint data. Initial mathematical formula structure expression data is generated through decoding of structure and semantic constraint fusion, and a formula coding sequence is generated through formula consistency verification and semantic constraint reconstruction. According to the invention, the accuracy and efficiency of mathematical formula identification can be effectively improved.

The invention discloses a microfluidic multi-organ tumor chip targeted drug test AIGC system, and relates to the technical field of medical drug test information processing. The microfluidic multi-organ tumor chip targeted drug test AIGC system comprises a multi-omics data acquisition module for acquiring and preprocessing disturbance response data and dynamic expression data; the disturbance response pre-screening module is used for carrying out strength evaluation on drug action strength and reconstructing channel mapping; the characteristic space reconstruction module is used for performing action analysis on the whole medicine action state and dynamically adjusting a liquid medicine blending rule; the drug effect prediction module is used for predicting and evaluating the response degree of the organoid under the intervention of the specific drug and deducing the individualized drug effect intensity; and the feature augmentation self-closed loop module constructs a negative supervision signal and dynamically corrects a disturbance perception and feature embedding path. The problems that current multi-omics data is high in dimension, large in redundancy and large in noise, key features are difficult to extract under limited samples, and model generalization is difficult to maintain are solved.

The invention discloses a disease-related anomaly localization protein prediction method based on deep learning, and the method comprises the steps: carrying out the protein prediction based on the proteomics expression data of normal and disease samples and a known protein interaction network in a normal state; respectively constructing a protein interaction network under the activity characteristics of the sample pathway and the disease state; the proteomics expression data and the pathway activity characteristics are fused through a cross attention mechanism, and protein characterization characteristics with pathway perception ability are constructed; respectively predicting protein subcellular localization under the normal and disease states by using a graph attention network model based on the protein characterization characteristics and the protein interaction network corresponding to the normal and disease states; disease-related abnormal localization proteins are identified by comparing predicted protein subcellular localization in normal and disease states. The method can efficiently and accurately identify the abnormal localization protein related to the disease, and has important scientific research value and application prospect.

The embodiment of the invention provides a sepsis prediction method, electronic equipment and a medium, and the method comprises the steps: inputting expression data corresponding to a plurality of different target genes of a patient into a sepsis prediction model, and obtaining a sepsis prediction result of the patient; wherein the sepsis prediction model is obtained through the following steps that a plurality of first training samples are obtained, and the first training samples comprise sample expression data of all target genes; for each first training sample, inputting the first training sample into a pre-trained analytical model to obtain a training prediction result of the first training sample and contribution relationship information between each piece of sample expression data in the first training sample and the training prediction result; and adjusting the initial sepsis prediction model according to the multiple pieces of contribution relationship information corresponding to the first training samples to obtain the sepsis prediction model. According to the embodiment of the invention, the accuracy and reliability of sepsis prediction can be improved.

The invention relates to the technical field of child emotion recognition, in particular to an emotion calculation method and device for child expressions, and the method comprises the steps: collecting and marking the multi-modal dynamic expression data of a plurality of Asian children, and constructing an enhanced mixed expression database according to the multi-modal dynamic expression data of the Asian children; performing iterative training on a pre-constructed deep convolutional neural network model by using the enhanced mixed expression database until a preset iterative period is reached, so as to obtain a deep time sequence emotion calculation model; and inputting the multi-modal dynamic expression of the child to be recognized into the depth time sequence emotion calculation model to calculate the current emotion of the child. Therefore, the problems that an existing child emotion recognition model is mostly based on European and American adult database training and mostly adopts single-frame static image analysis, and the complete dynamic process of expressions cannot be captured, so that the accuracy is remarkably reduced, the model robustness is poor, and effective application in real and continuous interaction scenes is difficult are solved.

The invention relates to the technical field of data analysis and processing, and discloses an intelligent evaluation method for a project road presentation, which comprises the following steps of: acquiring multi-modal data of a road presentation video in real time, including speech text, voice audio, expression video and PPT content, and analyzing and processing the multi-modal data through a multi-modal fusion technology to ensure feature alignment; generating a core challenge question set based on the analysis data, and verifying the payment willingness deviation ratio of a target user for commercial logic, technical implementation and a financial model; executing three rounds of progressive questioning; when the multi-modal data analysis of the project road performance is carried out, the analysis deviation of voice, text and visual data can be recognized in real time by constructing a collaborative alignment mechanism of multi-modal feature fusion, and the high conformity of inquiry problem generation and real commercial risks is ensured; and meanwhile, a confidence-driven redundancy check process is established, expression data is actively fused to correct and output when the voice recognition credibility is insufficient, and the accuracy of dynamic questioning and the key vulnerability capturing capability are improved.

The present disclosure provides a landmark feature determination method and device, electronic equipment and storage medium. The landmark feature determination method comprises: obtaining target expression data, wherein the target expression data is used to represent the expression amount of each candidate feature in different cells; the average expression amount and expression proportion of each candidate feature in different cell types are counted; for each candidate feature, the first inter-class comparison data is determined according to the average expression amount of the candidate feature in the target cell type and the average expression amount of the candidate feature in the non-target cell type; for each candidate feature, the second inter-class comparison data is determined according to the expression proportion of the candidate feature in the target cell type and the expression proportion of the candidate feature in the non-target cell type; and the landmark feature of the target cell type is determined according to the first inter-class comparison data and the second inter-class comparison data. The embodiment of the present application can solve the problem of landmark feature specificity.

This application discloses a method, system, and device for analyzing the state of the tumor microenvironment based on medical images, relating to the field of medical image processing technology. First, by acquiring medical images and gene expression data of related tissues within those images, the medical images are preprocessed to obtain image patches, and semantic features of these patches are extracted. An algorithm is then used to calculate a predefined immune resistance mechanism that matches the gene expression data as the dominant immune resistance mechanism. This dominant immune resistance mechanism is then set as a supervisory label for the model, and a prediction model is constructed. The model is trained using a large amount of data, and the medical image to be analyzed is input into the trained prediction model to obtain the predicted category of the resistance mechanism. This application can accurately predict the resistance mechanism of pathological tissues based on medical images and their related gene expression data.

The invention relates to a structural network-genetic map biological network model for predicting ischemic stroke and a construction method thereof, and the method comprises the steps: extracting and calculating seven multi-scale morphological features and pairwise Pearson correlation coefficients among the features from T1 weighted imaging data and diffusion tensor imaging data; constructing a 308 * 308 morphological similarity network matrix and a brain network module for identifying ischemic stroke neural dysfunction; 1782 sampling points are extracted from the Airy human brain map, and each sampling point comprises expression data of 10185 genes; the method comprises the following steps: mapping space coordinates of AHBA sampling points to a cortex package of a Desikan-Killiany map, carrying out normalization processing to output 308 * 10185 brain region gene-by-gene expression matrixes, and constructing a structural network-gene map biological network model for predicting ischemic stroke by adopting a partial least square regression method and a bootstrap method. Compared with the prior art, the model determines the specific molecular mechanism related to the phenotypic structure change of ischemic stroke injury, and the stroke occurrence probability is predicted according to the specific molecular mechanism.

The invention belongs to the technical field of biomedical engineering, and discloses a gene regulatory network optimization method based on a diffusion model, which comprises the following steps: acquiring gene data of cells under a steady state condition, and constructing a gene expression matrix according to the gene data; injecting Gaussian noise into the gene expression matrix based on a diffusion model method to generate a series of noisy data sequences; performing noise estimation and structure estimation on the noisy data sequence by a noise estimator and a structure estimator based on a gene regulation and control network, and performing reverse denoising processing according to the noise estimation and the structure estimation to obtain gene structure estimation after reverse denoising; performing structure optimization on the gene structure estimation after reverse denoising by adopting an acyclic constraint function and a regularization substitution method; and outputting the optimized gene structure estimation. According to the method, the regulation and control relation between the genes is accurately recognized from high-dimensional gene expression data, and the modeling precision of the regulation and control relation between the genes is improved.

The invention relates to the technical field of biomedical detection and bioinformatics, and discloses a detection method and system for social anxiety disorder risk assessment, and the method comprises the steps: obtaining transcriptome data of a peripheral blood sample of a to-be-detected object, and carrying out preprocessing and normalization to obtain a standardized gene expression matrix; extracting minimum gene set expression data containing 10 genes such as HSF5 and FADS2, and performing Z-score standardization processing by using the solidified model parameters; calling a preset weight coefficient and an intercept item to perform linear weighting and probability conversion calculation on the standardized data to obtain a disease prediction probability of the subject; and carrying out risk layering according to the optimal critical value and generating an auxiliary diagnosis report. According to the method, stable features are screened through a machine learning algorithm, the scoring model is constructed, subjectivity of traditional clinical diagnosis is overcome, and objective, quantitative and automatic evaluation of social anxiety disorder risks is achieved.

The invention provides a method for merging expression values based on an MTX family and a kit for predicting prognosis of thyroid cancer, the kit detects the transcriptional level expression quantity of the MTX gene family by combining an RT-qPCR technology with a specific primer, and the expression quantity data is substituted into a prognosis prediction model, so that the prognosis of a thyroid cancer patient is realized. Particularly, the prognosis evaluation of BRAF V600E mutant thyroid cancer patients is realized. Experiments prove that the expression level of the MTX gene family is related to thyroid cancer driving gene BRAF V600E mutation, and the prognosis of a patient is influenced by influencing the electron transfer function of the BRAF V600E mutation thyroid cancer patient, so that the MTX gene expression level detection can be used as a prognosis prediction index of the BRAF V600E mutation thyroid cancer patient, and the prognosis of the BRAF V600E mutation thyroid cancer patient is influenced. And a basis is provided for selection of operation modes of thyroid cancer patients.

Described herein are techniques for predicting whether a subject will respond to an immune checkpoint inhibitor (ICI) therapy based on RNA expression data and cytometry data obtained for the subject. In some embodiments, the techniques include: obtaining the RNA expression data, the RNA expression data having been previously obtained from a tumor sample from the subject; selecting, using the RNA expression data, an MF profile type for the tumor sample; obtaining the cytometry data, the cytometry data having been previously obtained from a blood sample from the subject; determining, using the cytometry data, a G2 score for the blood sample, wherein the G2 score is indicative a likelihood that the blood sample is of a Primed (G2) immunoprofile type of multiple immunoprofile types; and predicting, based on the selected MF profile type and the G2 score, whether the subject will respond to the ICI therapy.

The present application relates to the technical field of bioinformatics and artificial intelligence, in particular to a virtual cell construction method and system, the method comprising: taking single cell gene expression matrix and perturbation condition data as input data; constructing an encoding network based on a structural causal model to obtain latent representation, and constructing a perturbation variable according to the perturbation condition data, modeling the latent representation and the perturbation variable to obtain decoupled latent representation; constructing a continuous time evolution path from an initial distribution to a target distribution based on a flow matching model, and determining state changes according to the decoupled latent representation; numerically solving the continuous time evolution path to output virtual cell expression data. The present application is used to solve the problems of causal aliasing, insufficient distribution out-of-distribution generalization ability, unstable generation process and difficulty in counterfactual reasoning in the existing single cell perturbation prediction method.

The invention discloses a micro-expression recognition method and system based on an evaluation resampling and local connection double-branch network, and aims to solve the problems of unbalanced category distribution and low local feature extraction accuracy of an existing micro-expression data set. The method comprises the steps that an evaluation model is constructed, and vertex frames of head and tail categories of an original micro-expression data set are used for training; evaluating non-vertex frames of the tail category by using the trained model, calculating an evaluation value and screening the non-vertex frames to expand the tail category to obtain a balanced data set; constructing a local connection double-branch network comprising a global branch and a local branch, inputting the original image and the high-frequency filtering image into the global branch to obtain a global feature and a local connection weight, inputting the local image into the local branch to obtain a local feature, and weighting the local feature; and splicing the features to complete identification. Experiments show that the accuracy rate of the SAMM data set three-classification task reaches 90.23%, the method is superior to an existing method, network parameters do not need to be adjusted, universality is high, and the method is suitable for the fields of public safety, clinical diagnosis and the like.

The invention discloses an AI intervention analysis method for diabetic renal interstitial fibrosis, and relates to the technical field of biologication.The method comprises the following specific steps of sample collection and multi-omics data acquisition, specifically, kidney tissue samples of diabetic renal interstitial fibrosis patients before and after the diabetic renal interstitial fibrosis patients use glucose kidney health intervention and kidney tissue samples of healthy contrasts are collected; respectively acquiring epigenetic data and single-cell gene expression data, preprocessing, and integrating to construct a comprehensive data set; by combining the AI technology and the high-throughput epigenetic detection technology, the epigenetic modification change on a TGF-beta1 / Smads signal path in the process of intervening diabetic renal interstitial fibrosis by the Sushenkang can be deeply analyzed; a brand-new perspective is provided for understanding the occurrence mechanism of the diabetic renal interstitial fibrosis disease and the drug action mechanism of the TGF-beta1 / Smads pathway, epigenetic data is deeply mined through AI, and the specific mechanism of the TGF-beta1 / Smads pathway related gene expression affected by the TGF-beta1 / Smads pathway through epigenetic regulation is shown.

The application provides a humanoid robot facial expression mapping and calibration method, the facial expression mapping method constructs a self-supervised discrete expression data set, trains a multilayer perceptron model based on the data set, realizes preliminary mapping from expression parameters to rudder control signals, extracts expression parameters in a real person expression data set, generates robot rudder control signals through the model, constructs a self-supervised time sequence expression data set based on a redirection method, trains a long short-term memory network model, and realizes time sequence mapping of continuous expressions. The facial expression calibration method uses a visual capture device to obtain robot facial expression parameters in real time, gradually optimizes the rudder control signal, and makes the expression parameter response approach the target value. Through data-driven modeling and visual feedback optimization, the application realizes high-fidelity mapping from semantic expression parameters to multi-rudder collaborative control and systematic calibration, effectively improving the naturalness, accuracy and long-term stability of the robot facial expression.

The invention discloses a data processing method and device for VR interactive training of a cross-country container forklift. The method comprises the steps that VR interactive training data information of a user is acquired; the user VR interactive training data information comprises an action data set and an expression data set; processing the action data set to obtain a user action evaluation result; processing the expression data set to obtain a user emotion evaluation result; and according to the user action evaluation result and the user emotion evaluation result, performing personalized training suggestion and training content dynamic adjustment on the VR interactive training system. According to the invention, through efficient acquisition, deep processing and intelligent analysis of multi-dimensional data generated in the training process, the forklift driving operation level and safety awareness of trainees are accurately evaluated, the training result is fed back in real time, and the training strategy is dynamically adjusted, so that the quality and efficiency of the forklift driving VR interactive training are improved, and the training experience is improved. And the actual training cost and the safety risk are reduced.

The application relates to the technical field of mental health assessment, in particular to an AI mental health assessment and grading early warning system based on multi-modal fusion, which comprises an evaluation terminal module, a camera module and a wearable device module; the evaluation terminal module is used for mental health self-evaluation of a measured object through a PHQ-9 / GAD-7 scale, and determines a mental health grade result and grading early warning of the measured object; the camera module is used for collecting expression data of the measured object in the mental health self-evaluation process of the measured object, and obtaining an emotion recognition result of the measured object; and the wearable device module is used for collecting physiological data of the measured object. On the basis of scale data, objective data such as physiological data and expression data are added, which are part of data indexes that are difficult to conceal in human emotional responses, such as physiology and behavior, the subjective concealed components of the test are effectively reduced, multi-modal data combining subjectivity and objectivity are used for mental health assessment, and the assessment accuracy is improved.

This invention provides a training method for a psychological crisis early warning model and a psychological crisis early warning method, relating to the field of natural language processing technology. Through the psychological crisis early warning model, implicit expression data undergoes style transformation to obtain style transformation results. These results are then used to classify psychological crises, yielding target crisis classification results. The psychological crisis early warning model is then trained using the style transformation results, the target crisis classification results, and the explicit expression data corresponding to the implicit expression data. This training enables the target psychological crisis early warning model to possess strong style transformation and psychological crisis classification capabilities, thereby reducing the difficulty of detecting implicit psychological crisis data and improving the accuracy of detection.

This application discloses a method, system, device, and medium for cell type detection. The method acquires whole-genome expression data, marker gene information, and images of a batch of cells to be detected; based on the whole-genome expression data and marker gene information, it determines the first expression level of the marker gene corresponding to each cell to be detected; based on the first expression level, it filters out a set of cells with significant expression, which includes several first cells; based on the whole-genome expression data and marker gene information of the first cells, it determines the cell type of the first cells; based on the cell types of each first cell and the image, it determines the cell type of a second cell using a K-nearest neighbor algorithm; the second cell is any other cell to be detected besides the first cells. This method can quickly and accurately determine the type information of a large number of cells and can be used in single-cell type labeling. This application can be widely applied in the field of bioinformatics.

The embodiment of the invention provides a high-name-duplication user identification and retrieval method and device based on multi-feature fusion, a medium and a program product, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring a doctor-seeing information record of a to-be-tested person; obtaining N tuple data of the to-be-tested person based on the treatment information record; obtaining low-dimensional space expression data of the to-be-tested person according to the N-tuple data of the to-be-tested person; calculating the similarity between the low-dimensional space expression data of the to-be-tested personnel and the low-dimensional space expression data of the pre-processed personnel relation knowledge graph data; and extracting N tuples of which the similarity ranks at the top M in the personnel relation knowledge graph data as candidate results. The knowledge graph is constructed by utilizing the historical information of the hospital, so that the rapid matching of the information of the patient is realized.