189 results about "Diseases types" patented technology

The invention discloses a tunnel lining disease automatic identification method and system based on multi-source data fusion, and relates to the technical field of facility detection, and the method comprises the steps: collecting multi-modal time sequence data, carrying out the time-space alignment, and obtaining a time sequence multi-source data set; reconstructing a tunnel center line based on a vehicle pose and constructing a lining structure consistency coordinate framework, and performing structured projection and distortion correction on alignment data to obtain a multi-modal fusion data set; dividing a two-dimensional structure grid under the coordinate framework, extracting and fusing geometric, texture, depth and energy features, calculating a structure consistency damage index, and extracting a suspected disease area; and calculating a disease credibility index and judging a disease type in combination with multi-modal physical evidence, mapping a suspected disease region back to a three-dimensional space, completing disease boundary extraction and geometric quantization, and outputting structured disease information. According to the method, the structure expression and the structured alignment of the cross-modal data under the unified geometric reference are realized by constructing the consistent coordinate framework of the lining structure.

The invention discloses a traditional Chinese medicine famous prescription and operation multi-agent collaborative consultation method and system. The system comprises a medical case knowledge base module, an expert knowledge base module, a medicine knowledge base module, a pre-differentiation module, a multi-agent diagnosis module and the like. The method comprises the following steps: constructing structured experience medical case data, an expert experience knowledge graph and a traditional Chinese medicine knowledge graph knowledge base, carrying out mixed retrieval to carry out pre-differentiation, calling multi-agent consultation according to preliminary disease differentiation and syndrome types, and obtaining an expert diagnosis scheme for risk verification and prescription adjustment optimization. According to the invention, a multi-agent collaborative diagnosis technology is utilized, and a multi-agent architecture for simulating real traditional Chinese medicine consultation is constructed. A plurality of expert agents perform independent diagnosis and complement each other, so that the limitation of a single agent is avoided, and comprehensive and accurate diagnosis is provided. And in combination with expert experience knowledge graph and big language model reasoning, the system can flexibly differentiate symptoms according to the symptoms of the patient, accurately judge disease types and syndrome types, and improve the differentiation accuracy.

The invention relates to the technical field of artificial intelligence and medical decision systems, and discloses a reasoning agent construction method and system for intelligent medical treatment guidance, and the method comprises the steps: constructing a hierarchical modal completion network and a modal correlation knowledge graph; constructing a medical feature cross-modal mapping network based on comparative learning, and mapping different modal medical data to a shared feature space; according to the uncertainty of the complemented data, constructing an uncertainty quantitative model and automatically adjusting a diagnosis confidence threshold; aiming at different disease types and symptom combinations, constructing a disease modal incidence matrix and a modal reliability evaluation network; through weighted voting, evidence convergence and specialist authority evaluation, cross validation and collaborative decision-making of multiple specialist knowledge are realized, and a final diagnosis suggestion is formed; according to the invention, the problem of limited diagnosis capability caused by lack of medical data in a medical resource limited environment is solved.

The invention relates to a tunnel three-dimensional disease intelligent identification system and method based on a large model, the system comprises a point cloud data acquisition module and a processor, and the processor comprises a data processing module, a three-dimensional tile optimization module and a disease identification module. The data processing module carries out standardization, noise reduction and registration processing on the received point cloud data; the three-dimensional tile optimization module constructs a multi-level tile pyramid structure based on the registered point cloud data, establishes a mapping relation between a space coordinate and a tile index, compresses tile data based on a curvature point cloud simplification algorithm and adjusts texture quality to form a three-dimensional tile image; calculating a comprehensive score of the tile quality to verify the quality of the three-dimensional tile image; a disease identification module extracts multi-modal fusion features of the preprocessed three-dimensional tile image and geometric features corresponding to disease types; fusing the multi-modal fusion feature and the geometric feature to obtain a joint fusion feature; performing field fine tuning on the joint fusion features; and obtaining a disease identification result.

The invention relates to the technical field of peanut disease intelligent monitoring scheme design, in particular to a peanut disease intelligent monitoring method and system and electronic equipment. RGB and near-infrared images are synchronously acquired through a dual-channel acquisition device, after illumination compensation, defogging and geometric correction, multi-scale features are extracted by using a transfer learning optimized Eff cientNet-B4 network, and spectral information is fused by using a dual-path CBAM attention mechanism to generate a disease sensitive feature vector. The cascade classifier realizes disease type identification and severity grading based on ResNet-34 and a random forest model, and predicts a disease development trend in combination with an LSTM time sequence model. And integrating a U-Net segmentation network to generate a visual report, and associating an expert knowledge base to output a prevention and treatment scheme. According to the method, the problems of low detection precision, poor model generalization ability and insufficient decision support of a traditional method are solved, and an efficient and accurate disease management tool is provided for peanut planting.

The invention relates to a bridge technical condition evaluation method and system. The method comprises the steps that bridge basic information input by a user is acquired; generating bridge structure information according to the bridge basic information; determining a disease type and a disease index of each part according to a bridge disease library; according to the disease type and the disease index of each part, calculating the score of each part deducted according to the disease; calculating the score of each part type in each structure according to the score of each part deducted according to the disease; redistributing the weight of each part type in each structure, and obtaining the technical condition score of each structure based on the score of each part type in each structure; performing weighted calculation on the technical condition score of each structure to obtain an overall technical condition score of the bridge; and determining a technical condition grade according to the overall technical condition score of the bridge. According to the invention, automatic evaluation of the bridge state is realized, and the efficiency and accuracy of bridge maintenance and management are greatly improved.

The invention discloses a chronic disease evaluation system and method based on a weak AI medical large model platform, and particularly relates to the technical field of medical artificial intelligence. The system constructs a multi-modal input matrix and generates fusion features by collecting multi-modal data such as electronic health records, wearable device time sequence signals and medical images; calculating a risk index by using cooperative work of a cloud large model and an edge end lightweight model and combining a current state and a historical trend; an early warning threshold value is dynamically adjusted according to the disease type and historical risk data, and risk early warning is achieved; key features are extracted, similar cases are matched, and an interpretability report is generated; and updating edge end model parameters through federal learning to protect data privacy. The problems of data splitting, static assessment, resource dependence and the like in a traditional chronic disease assessment system are solved, comprehensive, dynamic and accurate assessment of chronic diseases is achieved, and the method has high clinical application value.

The invention relates to the technical field of bridge disease diagnosis and maintenance, in particular to a bridge disease diagnosis and maintenance measure recommendation method which comprises the following steps: constructing a bridge disease database which comprises feature data and cause data of various types of bridge diseases and maintenance measure data matched with the various types of diseases; receiving field disease information of a target bridge input by a user, wherein the field disease information comprises a disease type and a disease characteristic parameter; performing matching analysis on the field disease information and data in the bridge disease database, and diagnosing a disease cause of the target bridge based on a matching result; according to the diagnosed disease causes, one or more maintenance measures corresponding to the disease causes are called from the bridge disease database and output as recommended schemes, and a complete data link from disease detection to maintenance decision is established by constructing the standardized bridge disease database and a matching mechanism.

The invention provides an artificial intelligence corpus construction method, device and equipment based on multi-modal data, and relates to the technical field of data processing. The specific implementation scheme is as follows: acquiring health data corresponding to a disease type; according to the health data, multi-modal features corresponding to the patient codes are generated; extracting medical entities and association relationships according to the multi-modal features, and generating knowledge entries; and constructing an artificial intelligence corpus according to the knowledge entries. According to the scheme, information of different data modes can be integrated, and limitation of single-mode data is avoided.

The invention provides a dynamic medication dosage optimization method fused with reinforcement learning. The method comprises the following steps: acquiring a first physiological index parameter of a target patient; obtaining a first diagnosis report of the target patient, wherein the first diagnosis report comprises basic information of the target patient and first disease information of the target patient; the first disease information comprises a first disease type and first disease description information; determining a first reinforcement learning algorithm corresponding to the first disease type; determining a first control parameter of the first reinforcement learning algorithm according to the first physiological index parameter and the basic information; and performing operation on the first disease description information through the first reinforcement learning algorithm and the first control parameter to obtain a first medication dosage parameter. Based on the application, the drug effect can be consistent with the physical condition of a patient, and poor drug effect and excessive side effects caused by too strong drug effect are avoided.

The invention discloses a case resource integration data system based on big data analysis, and belongs to the technical field of medical data. The method comprises the following steps: acquiring hospital case data and corresponding disease type data to construct a resource integration range, acquiring a personal case information set provided by medical consultation of a patient, performing sensitive data extraction on the personal case information set to obtain a dynamic case parameter set, and sending the dynamic case parameter set to a data risk analysis module; the multi-source data acquisition module processes the personal case information set as follows; according to the method, a data integration-risk analysis-clinical intervention closed-loop system is constructed, preorder data standardization integration guarantees analysis reliability, accurate risk analysis provides a direction for intervention, multi-level alarm and pre-plan matching is achieved through linkage of the preorder data standardization integration and the accurate risk analysis, prediction diagnosis reports and intervention suggestions are automatically generated, invalid operations are reduced, the clinical decision-making efficiency is improved, and the system is suitable for large-scale popularization and application. And meanwhile, through dynamic threshold updating and system self-iteration optimization, the adaptability and practicability of the system are continuously enhanced.

A real-time artificial intelligence (AI) framework is provided for the automated analysis of radiological images and detection of disease, such as extracapsular extension (ECE) in prostate cancer. The system includes a dual deep learning architecture comprising a first convolutional neural network (CNN) for identifying diagnostically relevant image slices from three-dimensional MRI data, and a second CNN for classifying disease presence based on those slices. A preprocessing pipeline standardizes and harmonizes image input, and cropping algorithms isolate the region of interest for enhanced model performance. This framework enables scalable, high-accuracy diagnosis across various imaging modalities including but not limited to MRI, CT, PET, ultrasound, and diverse disease types, improving clinical decision-making and supporting integration into real-time radiology workflows.

The invention relates to a medical diagnosis method and system based on multi-modal retrieval enhancement and guide guidance. The method comprises the steps that text information including reports and / or electronic health records and medical image information are obtained; encoding the medical image information and the text information by using an image encoder and a text encoder respectively to obtain visual features and text features; respectively utilizing a guide branch decoder and a label branch decoder, taking diagnosis guide features and disease type labels of samples in the training stage as queries of a Transform structure, taking splicing features obtained by splicing text features and visual features as keys and values, and decoding to obtain first prediction probability distribution and second prediction probability distribution of disease types, so as to obtain first prediction probability distribution and second prediction probability distribution of the disease types; and a final disease prediction result is obtained. According to the method, disease specificity knowledge is dynamically retrieved based on a multi-source medical knowledge base, redundancy and noise are removed through a large language model, a standardized and structured diagnosis guide is generated, and explicit guidance of the knowledge is achieved.

The invention belongs to the field of bioinformatics, and relates to a protein post-translational modification prediction method based on multi-modal deep learning. The method comprises the following steps: firstly, performing multi-modal feature extraction by inputting a protein sequence and three-dimensional structure data to obtain a sequence feature vector and a structure feature vector; secondly, carrying out feature fusion by adopting a cross-modal attention mechanism and a self-adaptive gating network; then, combining the fusion features with the disease type information, and performing fine adjustment on the prediction probability through a disease specific coding network; then, using a multi-task learning framework to predict the locus probabilities of various protein post-translational modification types in parallel; finally, feature importance is calculated through a gradient back propagation technology, and a comprehensive report is output in combination with variation influence analysis. According to the method, high-precision and explainable protein post-translational modification prediction with disease perception capability is realized, and an important calculation and analysis tool is provided for revealing a disease molecular mechanism and finding accurate drug targets.

The invention discloses a brain disease risk prediction method and system based on big data analysis, and belongs to the technical field of brain disease risk prediction. The method comprises the following steps: carrying out standardized preprocessing and tagged classification on brain disease related big data to generate a feature data set; mining specific disease characteristics and risk factors in the set, and carding an association rule; training a risk prediction sub-model for each disease type based on the data, and building a multi-sub-model hierarchical prediction system; and collecting to-be-predicted object data, matching a disease type, and calling the corresponding sub-model to complete risk assessment. The system comprises multiple modules for collaborative operation, and a full-process closed loop of data storage, feature processing, model management and result output is realized. According to the scheme, the pertinence, the accuracy and the efficiency of risk prediction are improved, the traceability of the whole process and the dynamic optimization of the model are realized, and reliable technical support is provided for early screening and risk early warning of brain diseases.

The invention discloses a disease type prediction method and system based on a medical bill and a pseudo-label mechanism, and the method comprises the steps: obtaining a medical bill set which comprises a medicine list, a doctor-seeing department and patient portrait information, and carrying out the standardization preprocessing, and generating standardized bill data; respectively inputting the standardized bill data into a dynamic reasoning knowledge base and a pre-trained large language model, generating a first pseudo-label disease set through a rule reasoning engine, and generating a second pseudo-label disease set through the guidance of a semantic understanding cue word template; and fusing the two pseudo-label disease category sets to obtain a fused pseudo-label, training a multi-label disease category classification model by taking the fused pseudo-label as a target, and finally realizing disease category prediction of the to-be-predicted medical bill data. According to the invention, through a dual pseudo-tag generation mechanism fusing rule reasoning and a large language model, the problem that the medical bill data lacks a real disease category tag is solved, the accuracy and reliability of disease category prediction are improved, and a reliable solution is provided for deep utilization of the medical bill data.

The invention relates to pattern recognition and anomaly detection in the technical field of computers, and discloses a laying hen disease early-stage AI early warning method and a laying hen disease early-stage AI early warning system. The method comprises the following steps: obtaining a laying hen behavior video stream, an excrement image and body temperature data, and carrying out preprocessing such as timestamp alignment and equipment number mapping to generate a training and reasoning input structure; then target tracking, key point coding, region segmentation, contour feature coding and body temperature fragment statistical coding are executed to extract a multi-source feature sequence; performing time window alignment, scale normalization, pattern recognition and anomaly detection processing on the multi-source features to realize disease type recognition; and finally, generating an early warning event based on rule triggering and threshold judgment, and forming closed-loop disposal through prevention and treatment scheme retrieval and instruction arrangement. According to the method, collaborative analysis and early abnormity early warning of multi-source heterogeneous data are realized, and the disease recognition accuracy and early warning timeliness are remarkably improved.

The invention discloses a multi-modal data processing method and device based on an attention mechanism, equipment and a medium. Living habits of a user and living and medical characteristics of genes are combined on the basis of image data and electronic medical records. The method is advantaged in that the problem of difficult fusion of heterogeneous multi-source data is solved through uniform characterization of features of four-modal data, adaptive adjustment of attention weight of each modal is carried out through the entity association relationship in the entity relationship knowledge graph, and fusion precision of full-modal fusion features is improved. Through the anti-illusion prediction model, disease type and probability prediction is carried out for individual differences (user tags and full-modal fusion features) of target users, and the accuracy of disease prediction results is improved. The multi-modal data processing method can be applied to user body evaluation in the financial field and disease type prediction in the medical field so as to improve the risk prediction accuracy in the financial field and the disease type prediction accuracy in the medical field.

The invention provides a disease consumable recommendation method and system based on multi-agent cooperation, electronic equipment and a medium, and relates to the technical field of medical consumable recommendation. According to the technology of the invention, the related data of disease consumables are obtained based on the data sensing agent, and feature extraction is carried out on the related data of the disease consumables; constructing a consumable knowledge base based on the consumable related data; based on the extracted features, matching the AI decision agent in a consumable knowledge base, and outputting a plurality of intelligent recommendation results; the multiple intelligent recommendation results are fused, a medical consumable recommendation scheme is generated, and the service execution agent executes related services based on the medical consumable recommendation scheme. The disease type consumable recommendation technology can realize full-process intelligent management from disease type identification to consumable recommendation, has strong learning ability, reasoning ability and decision-making ability, and can provide accurate and personalized consumable recommendation services for different disease types.

The invention provides a special disease first-aid full-process auxiliary decision-making method based on multi-modal perception and AI, and relates to the technical field of medical first aid, and the method comprises the steps: obtaining first-aid field multi-modal data, constructing a feature dependence graph, and recognizing the type and symptom features of a special disease; indexing special disease diagnosis and treatment data based on the symptom feature vector, and determining a reference case and a treatment path; the action space is constructed in combination with clinical specifications, the expected income is predicted by applying Monte Carlo search, and the optimal treatment scheme is generated, so that the first-aid special disease recognition accuracy can be improved, the treatment decision time can be shortened, and the first-aid intervention effect can be improved.

The invention discloses a bridge apparent disease recognition method, which comprises the following steps of: 1, acquiring a two-dimensional image sample of a bridge apparent disease, marking a disease type according to a preset disease classification standard, and constructing an initial image data set; step 2, labeling the initial image data set to generate a label data set, and dividing the label data set into a training set, a verification set and a test set according to a ratio of 7: 2: 1; step 3, constructing a CSW-YOLO v9 model on the basis of a YOLOv9-m model architecture; 4, configuring hyper-parameters of the CSW-YOLO v9 model, performing iterative training by using the training set, performing performance verification through the verification set, and finally generating an optimized weight file; and step 5, inputting a to-be-detected bridge image into the CSW-YOLO v9 model loaded with the weight file, and outputting an apparent disease type and position information. The bridge apparent disease identification method provided by the invention is suitable for intelligent detection work of bridge multi-disease and small-target feature tasks.

The invention discloses a pet disease medical intelligent diagnosis system, and relates to the technical field of data analysis, the pet disease medical intelligent diagnosis system comprises a processing module, a judgment module and a verification module, first data features are obtained, first analysis is carried out to obtain the skin disease type of a current pet, severity levels are divided, the disease type is verified and updated, and the final disease type is output. The image features and biochemical data are combined, the diagnosis accuracy is improved, historical data are used for analysis, the mode and features of the disease can be better recognized, the diagnosis efficiency is improved through automatic image and data processing, the disease types are verified according to the expert diagnosis result, the model is updated, and the diagnosis efficiency is improved. The system can continuously learn and optimize, the accuracy and reliability of diagnosis are improved, medical resources can be reasonably allocated through the intelligent diagnosis system, veterinarians can more concentrate on complex and emergency cases, and the quality and efficiency of overall medical services are improved.

The invention relates to a remote electrocardio telemetering method and system, a terminal and a medium, and belongs to the technical field of electrocardio monitoring. The remote electrocardio telemetering method comprises the steps that a cloud end distributes monitoring equipment and initial parameters according to patient information; the edge end receives real-time electrocardiogram data, obtains environment data in combination with the position of a patient, collects physiological data through a wearable sensor, constructs a spatio-temporal context sensing map, fuses the data to generate situation enhanced electrocardiogram signal representation, inputs a comprehensive risk prediction model to obtain a dynamically updated prediction risk value and uploads the prediction risk value; the cloud judges the predicted risk value, if the predicted risk value is larger than a threshold value, a matched disease type is called, and differential prompts are generated and pushed to the patient and the doctor in combination with the co-disease relation network; if the electrocardiogram data does not exceed the threshold value, carrying out deep analysis on the electrocardiogram data, identifying abnormity, determining an abnormity level, generating a personalized report in combination with treatment history, pushing the personalized report, and triggering a clinical response protocol. The method has the beneficial effect of feeding back the abnormal electrocardiogram condition of the patient in time.

Nucleic acid error suppression is provided. In various embodiments, DNA is extracted from a collection of plasma samples. A sequence library with duplex adapters is prepared by ligating a duplex adapter having a Unique Molecule Identifier (UMI) to an end of each of a plurality of strands of the extracted DNA and amplifying the extracted DNA with a first polymerase chain reaction (PCR). A subset of the whole genome library is selected and amplified with a second PCR to increase an amount of PCR duplicates. A plurality of duplex reads is sequenced from the amplified subset aligned to a host genome and denoised based on said alignment. A variant presence is detected in at least one of the plurality of duplex reads. A signature of the variant is determined, which is compared to a collection of disease-specific variant signatures. A disease type is determined based on the comparison.

The invention discloses an intestinal polyp identification method based on images and multi-modal data. The method comprises the following steps: acquiring pathological images of a plurality of microscopes, objective magnification information corresponding to the pathological images, and text information corresponding to enteroscopes; performing data preprocessing on each frame of acquired pathological image and text information to obtain a training data set; a multi-scale multi-modal feature classification network is constructed, the training data set is used for training, and a trained multi-scale multi-modal feature fusion classification network is obtained; the method comprises the following steps: collecting a microscope pathological image of a patient, obtaining corresponding enteroscope text information, respectively carrying out data preprocessing, and inputting into a multi-scale multi-modal feature fusion classification network to obtain disease category prediction of a model. According to the method, pathological images and enteroscope text information are fully fused, accurate classification and prediction of disease types are finally achieved, the effect and efficiency of multi-modal diagnosis of intestinal diseases are improved, and therefore the accuracy of patient diagnosis is improved.

The invention belongs to the technical field of outpatient service management, and discloses a generative AI-based outpatient service management method and system, and the method comprises the steps: obtaining the basic information, symptom description and historical medical record data of a patient when the patient sees a doctor through an AI outpatient service management system, and carrying out the semantic understanding and preliminary triage prediction of the symptom of the patient through a generative AI model, and generating possible disease types and corresponding department suggestions. According to the invention, semantic understanding and triage prediction are carried out on the symptoms of the patient through the generative AI, and intelligentization of the appointment process is realized. Patients do not need to manually select cumbersomely, and the system automatically generates department suggestions, so that the appointment time is greatly shortened, and the convenience and efficiency of appointment of the patients are improved. The constructed patient priority evaluation model and the department resource state model enable the calculation of the reservation time to be more scientific and reasonable, coordinate the time arrangement of the reservation and the preliminary diagnosis stage, effectively avoid reservation conflicts and long-time waiting, and improve the overall efficiency and accuracy of outpatient reservation.

The invention discloses a medical insurance payment-oriented disease type cost benchmark model establishment method and application. The method comprises the steps of collecting medical insurance or hospital historical disease type settlement data; performing standardization processing on historical disease category settlement data, and performing data statistics on charging items according to disease categories; establishing a disease cost benchmark model, and calculating benchmark frequency and benchmark number of the disease by using historical disease settlement data; adjusting the obtained benchmark frequency and benchmark number by using a large language model to obtain recommended benchmark frequency and benchmark number; in combination with diagnosis and treatment specifications of professional clinical personnel and clinical habits of an overall planning area, the recommended benchmark frequency and benchmark number are manually adjusted to obtain disease benchmark values, and the disease benchmark values are added into a disease data set. Through the constructed case cost benchmark model, disease cost standardization management is promoted through benchmark management, diagnosis and treatment behavior standardization and medical insurance payment cost management and control are achieved, and the balance between compliance medical treatment and economical medical treatment is achieved.

The invention relates to the technical field of medical health monitoring, and discloses a remote monitoring system with intelligent voice, which comprises an intelligent monitoring module, a communication network module, a cloud platform module and a user terminal module, the intelligent monitoring module comprises a multi-mode sensor unit, a voice interaction unit and an edge calculation unit; by arranging the intelligent monitoring module, intelligent recognition and analysis of user voice are facilitated, natural voice instructions are supported, the problem that a traditional system is complex in operation is solved, meanwhile, multi-modal data are fused, a user personalized baseline is dynamically adjusted based on historical data of a user, and the user experience is improved. Therefore, the method can adapt to users of different ages and disease types, generates the environmental factors to correct the personalized baseline, and can adjust the physiological parameter weight according to the users of different disease types, thereby improving the accuracy of the personalized baseline, effectively reducing the probability of missing report and false report, and improving the intelligence.

The invention relates to the technical field of intelligent medical treatment, in particular to an AI-based brain disease test data management intelligent question-answering system, which comprises a multi-modal data acquisition module, an intelligent data processing module, an AI core model module, an interaction service module and a treatment guidance module, data aggregation is realized through a hospital interface, an equipment protocol and the like, and cleaning, standardization and multi-modal fusion are performed on multi-source data; precise question answering is achieved through the intelligent question answering unit, brain disease classification and stage division are completed through the patient layering unit, and a personalized scheme is generated by means of the treatment scheme generation unit; the interaction mode can be dynamically adjusted according to the cognitive level of a patient, emotion is recognized, guidance is provided, a doctor is supported to correct a scheme, and an iterative model is learned through feedback. The problems that in the prior art, the disease type is single, the data dimension is limited, and interaction adaptation is insufficient are solved, multi-disease-type whole-course precise management of brain diseases is achieved, and scheme scientificity and doctor-patient interaction experience are improved.

The invention relates to the technical field of biomedical information, in particular to an umbilical cord mesenchymal stem cell donor matching database construction method based on HLA (human leukocyte antigen) typing, which comprises the following steps of: recording and storing data of a donor in a database to finish initial construction of the database; regularly rechecking the donor data record stored in the database, and updating the state identifier of the donor data record when the rechecked data is changed by a preset degree; and receiving a matching request containing the human leukocyte antigen typing data of the target patient and the target disease type, performing donor screening in the database by using a preset comprehensive matching degree model based on the matching request, and optimizing parameters of the comprehensive matching degree model through a machine learning model based on the matching record and the clinical curative effect data corresponding to the matching record. According to the method, the accuracy and effectiveness of umbilical cord mesenchymal stem cell treatment are improved, and meanwhile, the system has continuous self-evolution ability.