186 results about "Disease characteristic" patented technology

The invention discloses a stone cultural relic disease identification method based on a hyperspectral image, and aims to solve the problem that stone cultural relic disease investigation depends on artificial experience and is time-consuming and labor-consuming. The method comprises the steps that a hyperspectral image of a stone cultural relic to be explored is collected and preprocessed; extracting spectral features; selecting a central wave band of three wave bands of blue light, green light and red light to construct a true color image; extracting texture features and color features of the true color image; fusing the spectral features, the texture features and the color features to obtain combined features; establishing a typical disease feature database; training a disease classification model by using a random forest, a support vector machine, a gradient elevator and a convolutional neural network; carrying out typical disease identification by using the intelligent identification model and drawing a disease cloud picture; and calculating the typical disease area proportion of the to-be-detected area to carry out disease evaluation. According to the invention, in-situ, lossless and rapid identification of stone cultural relic diseases is realized, manpower and material resources are saved, and an intelligent identification method is provided for cultural relic protection exploration work.

The invention discloses a pine wood nematode disease intelligent identification management and control method and system based on artificial intelligence, and relates to the technical field of forest management and control, and the system discloses a pine forest region division management and control module, a pine wood nematode disease identification module, a pine wood nematode disease treatment analysis module, and a region management and control sorting module. A pine forest region division control module and a pine wood nematode disease identification module are arranged, reasonable region control is performed on a pine forest region, pine wood nematode diseases in the pine forest region are accurately identified through deep analysis of multi-dimensional disease features, and a pine wood nematode disease treatment analysis module and a region control sorting module are arranged. The pine forest simulation management and control model of the pine wood nematode disease recognition area is constructed, the actual situation of the pine wood nematode disease recognition area in the subsequent management and control process is accurately analyzed, and the current disease situation, the subsequent disease control situation and the disease influence situation of the pine wood nematode disease recognition area are quantitatively analyzed.

The invention discloses an intelligent electronic medical record generation management system and method, a terminal and a medium. The generation management system comprises a data acquisition module, a template generation module, an intelligent prompt module, an editing and saving module, a data security protection module and a permission retrieval recording module. The generation management method comprises the following steps: performing identity permission verification; integrating to form multi-modal structured data, and screening and matching to generate a template framework; a diagnosis suggestion sorting table is generated, and data is stored and backed up; transmitting and storing data, and recording operation behavior data. According to the invention, real-time automatic acquisition of basic information and examination results of patients is realized; a medical record template is automatically matched and optimized based on disease characteristics of a patient and an examination result, so that the workload of a doctor is reduced, and the medical record structure conforms to clinical specifications; transmission and storage data are encrypted in a layered mode, current account information operation behavior data are recorded based on a timestamp, and safe storage and real-time management of medical record data are effectively ensured.

The invention relates to the technical field of model construction, in particular to a construction method of a disease feature recognition and evaluation model based on the Internet of Things. The method comprises the following steps that multi-modal physiological data of a patient are collected in real time through Internet of Things medical equipment, data preprocessing is conducted on the multi-modal physiological data of the patient, standard patient physiological data are generated, and the standard patient physiological data are stored in a distributed database; extracting gene sequencing data based on the biological sample library; performing global health risk factor perception on the standard patient physiological data to generate user level health risk perception data; and carrying out feature fusion on the user level health hazard perception data and the gene sequencing data to construct a disease feature matrix containing a space-time dimension. Through multi-modal data fusion, spatio-temporal evolution modeling, deep learning architecture and personalized risk assessment, the precision and timeliness of disease feature recognition assessment model construction are improved.

The invention relates to a road dielectric constant inversion and disease discrimination method based on a neural network. The method comprises the following steps: expanding the number of jump connections based on a TransUNet baseline, introducing a gating attention mechanism, and additionally arranging a multi-scale feature fusion module, an asymmetric depth-space attention module and a deep supervision solution terminal; an AdamW optimizer and a Warmup-cosine annealing learning rate scheduling strategy are adopted to construct an adaptive region weighted loss function so as to amplify dielectric constant mutation boundary loss weight. After a model is trained through a road GPR simulation data set and parameter adjustment of the set is verified to be convergent, a to-be-detected road GPR-B-scan image is input, dielectric constant distribution is output, and according to dielectric constant characteristics of different diseases, accurate discrimination of hidden diseases such as cavities, voids, loose bodies and water-rich bodies is achieved. By adopting the method, high-resolution dielectric constant end-to-end inversion can be realized, and multi-scale disease characteristics can be accurately captured.

The invention relates to the field of biological medicine, and discloses a method and system for analyzing cough sound by using disease characteristics to diagnose respiratory diseases, and the method comprises the steps: deploying a six-microphone annular array to achieve the precise positioning and triggering of a sound source; self-adaptive spectral subtraction and Wiener filtering cascade are adopted to enhance the audio; segmenting a cough segment based on energy envelope; fusing the Mel-cepstrum, the linear prediction residual error, the harmonic energy ratio and the transient zero-crossing rate to construct a pathological feature matrix; extracting local, medium-range and global time sequence features through a three-branch parallel convolutional network; inputting a disease specific classifier to discriminate asthma, pneumonia and laryngitis respectively, and applying a feature decoupling regular term to improve interpretability. The system correspondingly realizes the modularized processing flow. According to the method, the cough sound collection quality and the disease subtype recognition accuracy in a complex environment are improved, meanwhile, the thermodynamic diagram is output to assist clinical decision making, and the diagnosis credibility and practicability are enhanced.

The invention relates to the field of forestry monitoring data processing, in particular to a forestry pest and disease damage intelligent monitoring system and method based on unmanned aerial vehicle inspection, and the system comprises a data interaction unit which is used for building a real-time communication channel between an unmanned aerial vehicle and a server through employing a WebSocket protocol; the multi-scale feature extraction unit is used for constructing a three-stage pyramid convolution structure, and respectively extracting microscopic, medium and macroscopic scale features of the blade through convolution kernels of different specifications and cavity convolution; the time sequence parameter adjusting unit is used for fusing Transform and GRU, analyzing a feature map through a multi-head self-attention mechanism, learning a tree phenological law through the GRU, performing condition normalization adjustment on convolutional layer parameters based on the tree phenological law, and distinguishing physiological and disease features; and the decision fusion unit is used for splicing and fusing the composite feature map and the time sequence feature vector, carrying out parallel processing on an SVM and a Softmax classifier, and outputting a disease and pest recognition conclusion according to a dynamic threshold decision.

The invention relates to a fish disease detection method based on a deep learning image processing technology. The method comprises the following steps: collecting a fish disease image and carrying out image preprocessing; segmenting the preprocessed image by adopting a layered Mama model so as to extract a fish disease feature region; and performing feature extraction on the segmented disease feature region by using a MobileNet model, and performing disease classification through a full connection layer to obtain probability distribution of disease categories. According to the method, the hierarchical Mama model is adopted for image segmentation, the MobileNet model is adopted for disease classification, the fish disease detection efficiency and accuracy can be effectively improved, meanwhile, the challenges of poor image quality, limited training data and fish disease diversity can be coped with, and high-precision segmentation results and recognition are achieved under the condition of limited training data. According to the invention, accurate disease diagnosis can be provided, breeding operators can be helped to take measures in time, and loss caused by diseases is reduced.

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 relates to a drug-disease association prediction method and model based on NMF and GT. A drug-disease association prediction method based on NMF and GT comprises the steps of (1) obtaining a comprehensive similarity matrix of drugs and diseases according to drug fingerprint similarity, disease phenotype similarity and Gaussian kernel similarity of the drugs and the diseases; (2) extracting features of the comprehensive similarity matrix of the medicines and the diseases through non-negative matrix factorization to obtain characteristics of the medicines and the diseases; capturing a high-order structure relationship through a graph Transform to obtain similarity features of drugs and diseases; (3) respectively carrying out interactive fusion on the drug characteristics and the drug similarity characteristics and the disease characteristics and the disease similarity characteristics to obtain final drug and disease characteristic representations; and (4) carrying out association prediction according to the final characteristic representation of the medicine and the disease. The NMF and GT-based drug-disease association prediction method and model provided by the invention have good potential in the aspect of accurately predicting potential drugs.

The invention belongs to the technical field of road detection, and aims to provide a roadbed disease detection method and system, electronic equipment and a product. The method comprises the following steps: collecting multi-source detection data of a specified roadbed in real time and carrying out data fusion processing on the multi-source detection data to obtain fused detection data; performing crack detection processing on the roadbed surface image to obtain crack characteristic data of the specified roadbed, performing surface settlement analysis processing on the laser point cloud data to obtain settlement characteristic data of the specified roadbed, and performing deep disease analysis processing on the ultrasonic detection data and the electromagnetic wave reflection information to obtain the subgrade settlement characteristic data of the specified roadbed. Obtaining deep disease characteristic data of the specified roadbed; according to the crack characteristic data, the settlement characteristic data and the deep-layer disease characteristic data, constructing a disease three-dimensional model of the specified roadbed; and carrying out visualization processing on the disease three-dimensional model. According to the invention, omnibearing disease detection on the surface and the deep layer of the roadbed can be realized, and the intelligent degree is high.

The invention provides a drug-disease association prediction method and system, computer equipment and a medium, and belongs to the technical field of computers. The method comprises the following steps: constructing a drug-protein-disease heterogeneous network, and extracting a plurality of element path sub-graphs; inputting the meta-path sub-graph into a multi-scale diffusion graph convolution module, executing learnable multi-step graph diffusion on the basis of graph convolution, synchronously capturing local adjacency and high-order topological information, and generating node embedding; and performing dynamic weighted fusion by utilizing meta-path attention to obtain unified representation. In order to relieve imbalance of positive and negative samples, implementing difficult negative sampling in the embedding space, and constructing a balance training set with the positive samples; medicine-disease features are spliced, a regularization XGBoost classifier is trained, and unknown correlation accurate prediction is achieved. By adopting the method, the drug-disease association prediction precision and efficiency are improved, multi-scale topology and priori knowledge are fused, and a powerful calculation tool is provided for drug relocation.

The invention discloses an unmanned aerial vehicle self-adaptive inspection method based on multi-layer characterization learning, which comprises two stages of automatic coarse inspection and self-adaptive fine inspection, and learns the space structure, component characteristics and disease characteristics of a bridge layer by layer. In the automatic rough detection stage, in combination with a spatial clustering post-processing method and a point cloud semantic segmentation network, instance attributes of bridge components are quickly learned. Then, the point cloud space is compressed and converted by using a simulation view field model and a dimension reduction technology, and the unmanned aerial vehicle is guided to carry out efficient and comprehensive space patrol in plane geometry. In the adaptive fine detection stage, a space-channel two-dimensional optimized hybrid convolution structure Light-PVIT is utilized to learn and extract disease features in the automatic coarse detection stage as prior information. The information guides the unmanned aerial vehicle to carry out fine detection on a disease area in a small field of view. According to the unmanned aerial vehicle self-adaptive inspection method, the bridge inspection efficiency and precision are remarkably improved, and reliable technical support is provided for bridge maintenance work.

The present invention provides a hypergraph-based drug-disease association prediction method, device and electronic device, which relates to the field of bioinformatics, including: obtaining a drug hypergraph and a disease hypergraph; based on a hierarchical contrastive learning algorithm, inputting the drug hypergraph and the disease hypergraph into a target hypergraph convolution model to obtain an initial drug feature matrix and an initial disease feature matrix; performing interactive information aggregation through the initial drug feature matrix and the initial disease feature matrix to obtain a target drug feature matrix and a target disease feature matrix; performing association prediction through the target drug feature matrix and the target disease feature matrix to obtain a drug-disease potential association matrix. The present invention realizes accurate prediction of drug-disease potential association.

The invention discloses an intelligent diagnosis method for aquatic diseases, and relates to the technical field of aquatic disease diagnos.The method comprises the steps that aquaculture information is collected and stored through a data monitoring module, a preliminary diagnosis model is built through a multi-source fusion module, and the biological health state and the aquaculture risk state are evaluated; therefore, disease risks are evaluated in time to realize preliminary diagnosis and prediction, and then a deep learning model is constructed through a central processing unit to perform refined judgment on aquatic diseases, so that an aquatic disease diagnosis and prediction result based on environmental characterization is generated, and refined diagnosis and prediction on the aquatic diseases are realized. The defect of insufficient diagnosis accuracy caused by aquatic product disease characteristic differences in different environment states is overcome, the generalization ability of scheme application is improved for auxiliary reference of farmers, the deep learning model is adjusted and corrected through the optimization design module, and self-growth optimization of the deep learning model is achieved.

The embodiment of the invention provides a tunnel disease detection method and system based on an unmanned aerial vehicle, and belongs to the technical field of defect optical detection. The method comprises the steps that an unmanned aerial vehicle is controlled to fly along a tunnel to collect multichannel image data of the surface of a structure, and the flight attitude is adjusted based on environment illumination information to execute image illumination compensation; performing image alignment of each target anchor point based on a structure anchor point atlas constructed based on historical acquisition images in combination with the flight pose information of the unmanned aerial vehicle and the multi-channel image data after illumination compensation; identifying a disease area in the aligned image, extracting disease features of each target anchor point at this time, and updating the disease of each target anchor point at this time into a time sequence feature data sequence corresponding to each target anchor point; and based on the updated time sequence characteristic data sequence of each target anchor point, Bayesian point change detection is adopted to analyze the disease evolution trend of the tunnel. According to the scheme, the alignment precision, the time sequence comparability and the risk judgment capability of tunnel disease detection are integrally improved.

The invention discloses a subgrade concealment disease quantitative identification method based on a ground penetrating radar, relates to the technical field of image processing, and aims to respectively establish ground penetrating radar forward simulation models for highway subgrade cavity, crack, looseness and void diseases, simulate the propagation process of radar emission electromagnetic waves in a medium, and identify the subgrade concealment disease. A B-scan radar image with clear disease types and sizes is obtained, a data set is obtained by expanding a simulation radar image, a high-quality standard data set of highway roadbed diseases is created, and quantitative recognition of the roadbed diseases is facilitated; according to the method, highway subgrade disease characteristics of different forms, different development degrees and different fillers are analyzed, the disease characteristics at least comprise cavity diseases, crack diseases, loosening diseases and void diseases, the relation between the preset disease size and the width and area in a GPR image is analyzed, and quantitative analysis of subgrade diseases is achieved. And intelligent recognition of highway subgrade diseases is realized by using a deep learning algorithm.

The invention discloses an intelligent inquiry dialogue method and device, computer equipment and a storage medium. The method comprises the steps of obtaining an inquiry dialogue of a target user through an intelligent inquiry dialogue interface; extracting disease feature information from the inquiry dialogue; querying a medical decision database based on the disease feature information to obtain a suspected disease set; calculating a suspicion degree corresponding to a suspected disease in the suspected disease set; screening a target suspected disease from the suspected disease set based on the suspicion; according to the disease feature information, determining whether the target suspected disease has an unconfirmed disease factor; if yes, generating a question generation task based on the unconfirmed disease factors, executing the question generation task through a large language model to obtain questions for the unconfirmed disease factors, and outputting the questions through an intelligent inquiry dialogue interface. According to the method, a transparent and controllable inquiry process is provided, and the risk of diagnosis errors is reduced.

The invention provides a rapid heart failure detection system, and belongs to the technical field of medical health. The system mainly comprises a historical data acquisition and preprocessing module used for extracting and preprocessing historical data; the simulated heart failure detection model building module is used for learning the influence of different basic diseases on BNP / NT-proBNP; the real-time data acquisition and preprocessing module is used for collecting and preprocessing real-time and latest inspection data; the basic disease analysis and BNP prediction module is used for analyzing basic disease characteristics and predicting the BNP / NT-proBNP level; the risk assessment module is used for calculating a risk score and determining a risk level; and the early warning and intervention module is used for generating early warning and intervention suggestions. According to the heart failure risk assessment method, the influence of basic diseases on BNP / NT-proBNP is quantified through the model, and the accuracy and timeliness of heart failure risk assessment are improved by combining real-time data and latest inspection data.

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 drug-disease interaction prediction method and device, a medium and a product, and relates to the field of intelligent medical treatment. Firstly, drug data, disease data and biological data are acquired; carrying out preprocessing and feature extraction on the drug data to obtain drug molecular features; extracting target spot features based on the biological data; inputting the drug molecular features and the target features into a first full-connection neural network for predicting a drug-target interaction relationship; generating drug biological network characteristics based on the drug-target interaction relationship; performing preprocessing and feature extraction on the disease data to obtain disease features; training a second full-connection neural network based on the drug-biological network features and the disease features, and obtaining a drug-disease interaction prediction model after training is completed; the drug-disease interaction is predicted by using the drug-disease interaction prediction model, and the prediction efficiency and accuracy can be improved.

The invention discloses a heavy haul railway ballast sinking damage degree judgment method based on a ground penetrating radar, and belongs to the technical field of ground penetrating radar data processing, and the method comprises the following steps: obtaining ground penetrating radar data of a heavy haul railway, and carrying out the preprocessing; performing hierarchical processing on the preprocessed ground penetrating radar data; on the basis of a wave field reverse continuation principle, data matrixes of different layers are introduced to carry out offset and merging processing on the layered ground penetrating radar data, and complete disease offset data are obtained; on the basis of the complete disease offset data, obtaining disease feature data through calculation; and constructing a function relationship for judging the heavy haul railway ballast sinking groove disease degree, and obtaining the heavy haul railway ballast sinking groove disease degree based on the function relationship and the disease feature data. According to the method provided by the invention, the severity of the heavy haul railway ballast sinking groove disease can be more accurately judged.

The invention discloses a miRNA and disease relation prediction system and method based on causal feature selection. According to the system, the accuracy of miRNA and disease relation prediction is improved through a plurality of modules such as data acquisition, negative sample selection, feature selection and causal analysis, model training and evaluation and the like. The data acquisition module is used for acquiring miRNA expression data and disease characteristics from a biomedical database; the negative sample selection module is used for grouping unknown miRNA-disease relationships through k-means clustering and randomly selecting negative samples; the feature selection module adopts a causal feature selection algorithm to construct a Markov blanket so as to screen out miRNA features directly related to disease states, and redundancy is reduced; the model training module uses a single machine learning algorithm for training, and optimizes the model performance through cross validation; and finally, the evaluation module evaluates the model effect through indexes such as AUROC and AUPR. According to the method, the accuracy of miRNA and disease relation prediction is remarkably improved, and important support is provided for medical research.

The invention relates to a road infrastructure full-automatic inspection method, device and equipment based on an unmanned aerial vehicle group, and a medium thereof. The method aims at the problem that disease characteristics collected by unmanned aerial vehicles in traffic flow and vegetation shielding scenes are broken, and the method comprises the following steps of: decoupling texture and motion characteristics of a dynamic shielding area; feature maps of the effective area and the shielding area are separated; performing cross-frame splicing and continuous repair on the fracture features by using an optical flow constraint generation network, and reconstructing the geometric integrity of a shielded region; infrared data are fused to correct thermal deformation errors, and a high-precision continuous curved surface model is generated; and finally, based on a disease quantitative feature vector matching maintenance strategy, realizing full-process automation from data acquisition to decision output. According to the method, the continuous modeling precision and the maintenance decision reliability of pavement disease detection in the shielded environment are remarkably improved.

The invention discloses a pathogen transmission rapid early warning and traceability analysis method based on multi-source data fusion. The method comprises the steps of S1, collecting multi-source data such as drug sales, network behaviors, social media, outpatient diagnosis and traffic time and space; s2, through cleaning and standardized preprocessing, social text disease features are extracted by adopting BERT; s3, mining a comprehensive weak signal through single-source anomaly detection and multi-source correlation analysis; s4, fusing the features by using an Attention-LSTM model, and outputting a regional risk index; s5, training an early warning model based on historical data, and setting a three-level threshold to trigger early warning; and S6, positioning a propagation starting point in combination with the spatio-temporal data, and constructing a propagation chain through a graph neural network. The early warning is advanced by 3-7 days, the traceability precision reaches the community level, and the prevention and control precision is improved.

The invention discloses a rural highway disease monitoring system based on an SAR satellite remote sensing technology. The rural highway disease monitoring system comprises an SAR satellite data acquisition unit, a data receiving unit, a data processing unit, a disease identification unit, a disease evaluation and monitoring unit, an information management unit and a display unit. The SAR satellite data acquisition unit is used for acquiring an SAR image; the data receiving unit is used for receiving SAR image data; the data processing unit is used for processing the received image data; the disease identification unit is used for identifying and monitoring road disease characteristics; the disease assessment and monitoring unit is used for assessing road health conditions and monitoring disease changes; and the information management unit is used for data storage. The system is provided with the information management unit which can store historical data of road disease feature data, so that the disease assessment and monitoring unit can judge a monitoring result in combination with the historical data and real-time data during disease assessment, and road disease analysis is more accurate.

The invention discloses a Chinese patent medicine prescription intelligent assistance method and system, and the method comprises the steps: determining a possible traditional Chinese medicine diagnosis range through disease information / patient information, carrying out the further analysis, obtaining the disease feature information / patient feature information which needs to be obtained for determining the traditional Chinese medicine diagnosis, and determining the traditional Chinese medicine diagnosis according to the related information. The Chinese patent medicine prescription intelligent auxiliary system comprises a demand acquisition module, a feature information analysis module, an information acquisition module and a traditional Chinese medicine diagnosis analysis module. According to the Chinese patent medicine prescription intelligent assistance method and system, doctors and patients can be helped to accurately select Chinese patent medicines, and damage caused by misuse of the medicines is prevented.

The invention provides a hospitalization first-time disease course record generation method and system based on a large model, and the method comprises the steps: carrying out the text segmentation, format conversion, term standardization and theme recognition through employing a historical hospitalization document record, obtaining semantic transcription data through the similarity, building a mapping relation, and carrying out the calculation of the semantic transcription data. According to the method, the medical record documents called and stored in the electronic medical record system are processed, semantic transcription data corresponding to the medical record documents are obtained, and case medical records with similarity meeting a threshold value and stored in the electronic medical record system are matched through keyword and semantic mixed retrieval according to the medical record documents and the semantic transcription data. According to the semantic transcription data and the first course record initial cue word, constructing an optimized cue word with department and disease type characteristics, and according to the optimized cue word, the semantic transcription data and the case medical record, inputting the optimized cue word, the semantic transcription data and the case medical record into a hospitalization first course record generation model trained by adopting historical data to finally obtain a hospitalization first course record. The problem that in the prior art, the accuracy of the first record content is low is solved.

The invention requests to protect a drug comprehensive evaluation system based on machine learning and an expert database, and the system comprises a multi-source data processing module which is used for processing and standardizing the molecular structure, genome characteristics and clinical data of a drug; the expert knowledge base comprises drug attributes, disease characteristics and clinical guidelines; the prediction model cluster is composed of a deep neural network, an integrated learning model and a symbol inference engine and used for predicting the IC50 value, the clinical response rate and the adverse reaction probability of the medicine; the dynamic optimization module is used for uncertainty quantification of the model, incremental learning of a training set and dynamic updating of a knowledge base; and the interpretability output module is used for interpreting the prediction result and visually outputting the prediction result.

The invention belongs to the field of artificial intelligence and financial science and technology, and relates to a claim settlement method which comprises the following steps: analyzing an electronic medical record by adopting a trained semantic analysis model to obtain disease information; standardizing the disease information, and constructing initial multi-dimensional disease features; generating medication suggestions and side effect monitoring schemes according to the online inquiry records and the electronic prescription information, and integrating the medication suggestions and the side effect monitoring schemes into the initial multi-dimensional disease features to obtain multi-dimensional disease features; and analyzing the obtained insurance product to obtain a corresponding claim settlement rule, generating a personalized claim settlement scheme according to the claim settlement rule and the multi-dimensional disease features, and automatically executing a claim settlement process according to the personalized claim settlement scheme. The invention further provides a claim settlement device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the electronic medical record can be stored in a block chain. The claim settlement result is accurately judged, a more personalized claim settlement scheme is provided, and the claim settlement accuracy and efficiency are improved.