65 results about "Disease characteristic" patented technology

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 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 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 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 lung function evaluation method for a patient in a pneumonia rehabilitation period, and relates to the technical field of rehabilitation management. Extracting a lung function feature set based on the image data; analyzing based on multi-dimensional parameter association, and establishing a dynamic mapping relationship between the lung function feature set and associated physiological parameters; identifying abnormal associated parameters through a dynamic mapping relation between the lung function feature set and the associated physiological parameters, and when the deviation between the image feature recovery degree and the corresponding physiological parameter recovery degree exceeds a preset threshold value, judging that functional abnormal parameters exist; and matching a potential complication database based on the abnormal parameter type, analyzing the relevance between the abnormal parameter and a preset disease feature model, and outputting secondary disease risk early warning. The method has the advantages that through cross-modal fusion of the lung image features and the dynamic physiological parameters, the early recognition precision of the pneumonia convalescence complication is remarkably improved.

The invention provides a plant disease feature extraction and classification method and system based on deep learning, and relates to the technical field of plant disease identification, and the method comprises the steps: mining the interdependence rule of different disease features through constructing a disease feature symbiosis wake-up network, and setting wake-up conditions; inputting a to-be-analyzed plant disease image into the network, capturing initial disease features, triggering associated feature wakeup, and generating a disease feature symbiotic set; conducting the characteristic information according to the hierarchical relationship to generate enhanced symbiotic disease characteristics; recording morphological change details to construct a disease characteristic evolution sequence; and inputting the disease characteristic evolution sequence into a pre-trained deep learning classification model, analyzing disease essential characteristics, and generating a classification result containing a disease type and a matching basis. The method can comprehensively and accurately extract features, and improves the accuracy and reliability of plant disease classification.

The invention discloses a drug-disease association prediction method, system, device and medium, and relates to the technical field of drug relocalization, and the method comprises the steps: constructing a drug similarity network and a disease similarity network; obtaining a binary adjacency matrix through k-neighbor graphs of different nodes in the two similarity networks, carrying out weighted fusion on the binary adjacency matrix through an attention coefficient to obtain a soft adjacency matrix, and carrying out fine-grained graph convolution updating to obtain drug features and disease features; extracting heterogeneous node representations of drugs and diseases in the biochemical heterogeneous network, and carrying out dynamic weight distribution on different representations to obtain final embedding of drug nodes and disease nodes; and splicing the final embedding of the drug nodes and the final embedding of the disease nodes, and performing prediction according to the spliced features to obtain the drug-disease association probability. According to the method, the potential complementary relationship between the two is fully mined, and the heterogeneous feature fusion effect is improved, so that the performance and robustness of drug-disease association prediction are integrally enhanced.

The invention provides a disease portrait generation method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: constructing a feature association map according to multi-source data; inputting the atlas into a hierarchical graph attention network, learning a direct association and combination relationship of feature nodes by using a hierarchical structure, and obtaining an attention weight; candidate key feature nodes are screened accordingly; performing causal effect estimation on the candidate key feature nodes to obtain a causal effect value representing real causal intensity; and finally, based on the attention weight and the causal effect value, determining an association level and generating a disease portrait. According to the method, the depth feature mining capability of the hierarchical graph attention network and the counterfeit elimination and true storage capability of causal inference are fused, so that automatic discovery of the high-order combination relationship of the disease features and quantitative verification of the true causal are realized, and a dynamic disease portrait with depth correlation information and high-credibility causal interpretation can be generated.

The application relates to the technical field of pest and disease identification, in particular to a dynamic early warning and prevention and control system based on forestry pest and disease identification, which obtains crown layer leaf attribute data of target forest area tree species and excitation source physical characteristic parameters of a patrol unmanned aerial vehicle, determines aerodynamic pressure required for turning over of the crown layer leaves according to the leaf attribute data, determines aerodynamic excitation control parameters in combination with the excitation source physical characteristic parameters and a final parameter mapping model, controls the unmanned aerial vehicle to force the leaves to turn over by using a downward airflow to obtain leaf back image data, and adaptively corrects the control parameters according to an effective leaf back exposure rate; image recognition is performed on the leaf back image data to obtain pest and disease characteristic data, and a leaf stiffness index is calculated through time domain analysis; pest and disease severity is generated according to the pest and disease characteristic data and the leaf stiffness index, and a graded early warning is performed; the application can effectively obtain leaf back image information, and realizes early and accurate identification and dynamic early warning of pests and diseases.

The present disclosure describes systems configured to recognize indicators of a medical condition within a diagnostic image and predict the progression of the medical condition based on the recognized indicators. The systems can include neural networks trained to extract disease features from diagnostic images and neural networks configured to model the progression of such features at future time points selectable by a user. Modeling the progression may involve factoring in various treatment options and patient-specific information. The predicted outcomes can be displayed on a user interface customized to specific representations of the predicted outcomes generated by one or more of the underlying neural networks. Representations of the predicted outcomes include synthesized future images, probabilities of clinical outcomes, and / or descriptors of disease features that may be likely to develop over time.

The invention belongs to the technical field of data processing, and particularly relates to an internet-based traditional Chinese and western medicine intelligent diagnosis and treatment method and system and a storage medium, and the method comprises the steps that a user fills in different disease feature data through a mobile terminal module, and the mobile terminal module preprocesses the different disease feature data; the preprocessed different disease feature data are sent to the diagnosis and treatment server module through the internet module; the diagnosis and treatment server module pre-trains a diagnosis and treatment model 1, a diagnosis and treatment model 2, until a diagnosis and treatment model N. The diagnosis and treatment server module receives different disease feature data from the mobile terminal module, selects a proper diagnosis and treatment model from the diagnosis and treatment model 1, the diagnosis and treatment model 2, until the diagnosis and treatment model N, and sends the selected diagnosis and treatment model to the diagnosis and treatment server module. And inputting the different disease feature data into the selected diagnosis and treatment model, and outputting disease names corresponding to the different disease feature data by the diagnosis and treatment model. According to the invention, the disease identification accuracy can be improved.

The application belongs to the technical field of pest and disease identification, and discloses a pepper pest and disease identification method and system based on a disease grading detection double-task model. 2 The sample image is subjected to enhancement processing by using a saliency target detection model U 2 The sample image is subjected to foreground and background segmentation by using U 2 The main body of the leaf in the image is retained, the authenticity and integrity of the disease characteristics are ensured, and the diversity of the data is increased to achieve the purpose of data enhancement.

The application discloses a disease pre-examination triage auxiliary method and system, relates to the technical field of disease pre-examination triage, collects patient treatment data to generate a data group, extracts features of the data group to obtain a disease feature vector, constructs a dynamic routing layer to convert the disease feature vector into a plastic weight matrix, constructs a processing cluster layer including a quick response cluster and a deep analysis cluster, inputs the plastic weight matrix into the processing cluster layer to obtain an analysis result, and outputs a treatment guide report by fusing analysis results of multiple clusters through a decision fusion layer.

The present application relates to the technical field of intelligent wearable medical equipment, in particular to an intelligent bracelet for monitoring neurological diseases, which comprises a wristband body and an electronic monitoring system, the electronic monitoring system comprising a multi-modal physiological signal acquisition module, a microprocessor control unit, a disease characteristic analysis module, an early warning feedback module, a wireless communication module and a power management module. By collecting multi-dimensional physiological signals, Parkinson's disease targeting features, epilepsy targeting features and double disease auxiliary features are extracted, then through the basic differentiated contribution weight of the convolution long short-term memory network model and the dynamic output distribution weight adjustment strategy triggered by the pathological threshold, the spatio-temporal fusion analysis is realized and the risk probability of the two diseases and the normal state probability are output, the early warning feedback module outputs differentiated prompts according to the risk probability, the wireless communication module synchronizes data or sends emergency alerts through dual-mode communication, and the power management module realizes power supply and low power reminder.

The invention belongs to the field of bioinformatics, and relates to a circRNA-disease association prediction method based on an attention fusion graph-hypergraph convolutional network. The method comprises the following steps: firstly, constructing a circRNA-disease incidence matrix and a plurality of similarity matrixes based on a database; secondly, respectively constructing a graph adjacent matrix and a hypergraph adjacent matrix based on the similarity matrix, extracting circRNA and disease low-order local features by using a graph convolutional network, and extracting circRNA and disease high-order global features by using a hypergraph convolutional network; then, dynamically fusing circRNA and disease low-order local features obtained from the graph convolutional network and circRNA and disease high-order global features obtained from the hypergraph convolutional network through an attention aggregation mechanism, and enhancing feature interaction by means of comparative learning; then, using a variational auto-encoder to extract circRNA and disease nonlinear characteristics from the incidence matrix; and finally, integrating a plurality of circRNAs and disease characteristics, and predicting a circRNA-disease association score. According to the method, multi-level features can be effectively fused, and the prediction accuracy and robustness are improved.

The application provides a kind of style migration of aquatic organism image recognition method and system, it is related to image recognition technical field.The method includes the following steps: obtaining aquatic organism image in aquaculture environment;Disease characteristic extraction is carried out to the obtained aquatic organism image, and disease characteristic information is obtained;According to disease characteristic information, generate disease characteristic indication map;The style migration operation is executed to the obtained aquatic organism image, and the aquatic organism image after style migration is obtained;The aquatic organism image after style migration is superimposed with disease characteristic indication map and is displayed, and the superimposed display image is obtained;Using superimposed display image, disease identification is carried out to aquatic organism.The method of the application makes up the defect that traditional style migration may cause disease information loss, improves the accuracy of identification and the explainability of result, and then enhances the value of system in early warning and artificial decision.

The invention relates to the technical field of artificial intelligence, and particularly discloses a health management scheme generation method and device based on artificial intelligence. Performing entity recognition and intention recognition on the user voice to obtain disease features and user intention; obtaining target health data associated with the disease characteristics; analyzing the disease characteristics and the target health data to obtain disease information and predict health risks; and generating a health management scheme based on the user intention, the disease information and the predicted health risk. The health management scheme is generated in combination with the user intention, the individuation degree is improved, the health parameters associated with the disease features are obtained, the data richness is improved, and then the accuracy of the health management scheme is improved. When the method is applied to health management systems such as medical care, family health monitoring and the like, a health management scheme with high individuation degree and high accuracy can be generated according to the intention of the user and the specific health data, so that the user experience is improved.

The invention relates to the technical field of road engineering, and discloses a precise calculation method and system for the amount of emulsified asphalt used in pavement maintenance engineering.The precise calculation method for the amount of emulsified asphalt used in pavement maintenance engineering comprises the steps that pavement three-dimensional color point cloud data are collected and preprocessed; identifying the type and position of the pavement disease; inverting the pore characteristics of the pavement material; calculating the penetration depth of the emulsified asphalt and determining the penetration depth of a sub-region in combination with the disease identification result data set; predicting the amount of emulsified asphalt; construction process parameters are optimized; a construction operation instruction book is generated and electronized; the construction quality is monitored in real time, deviation analysis is conducted, and parameter adjustment is conducted based on a deviation analysis result; according to the invention, by introducing transfer learning and incremental learning technologies, the disease identification model can quickly adapt to disease characteristics in different regions and under different conditions.

This invention discloses a method and system for analyzing mulberry pests and diseases based on drone monitoring. The method includes: collecting multispectral and RGB high-definition images of multiple mulberry planting areas via drones along preset flight paths; preprocessing the image data to generate multi-dimensional spectral indices, analyzing image texture and color features to assess pests and diseases, constructing a fusion feature vector for the planting areas, calculating feature differences using Euclidean distance, and marking associated areas with feature deviations within a preset range; collecting multi-period associated area images, weighting and calculating preset indices and pest and disease indices, assessing pest and disease trends, and screening source areas; setting control priorities based on pest and disease characteristics, and planning drone-based pest and disease control schemes. This invention achieves precise monitoring, source location, and scientific control of mulberry pests and diseases, improving monitoring efficiency and control targeting, adapting to various planting scenarios, and effectively improving the level of intelligent management in mulberry planting.

The invention relates to the technical field of sleep detection, and discloses a detection system for obstructive sleep apnea syndrome. The system comprises a signal acquisition and enhancement module, a signal segmentation and quality evaluation module, a feature extraction and matching module, a graph structure analysis module and a risk index generation and early warning module. The signal acquisition and enhancement module acquires and enhances a multi-source physiological signal to generate an enhanced signal sequence; the signal segmentation and quality evaluation module adaptively segments the signal to screen out high-quality signal segments; a feature extraction and matching module extracts multi-dimensional feature vectors from the feature extraction and matching module, and the multi-dimensional feature vectors are matched with a standard feature database to obtain a feature difference matrix; the graph structure analysis module deduces the feature node association degree; and the risk module integrates the data, generates a risk index and decides early warning output. The system accurately captures disease characteristics, reduces missed diagnosis and misdiagnosis rates, is suitable for home and large-scale screening, and provides reliable reference for clinical diagnosis.

The invention relates to the technical field of insurance and intelligent data processing, and discloses an internet health insurance underwriting method and device based on disease characteristics, and the method comprises the following steps: S1, obtaining to-be-underwritten historical insurance data and current insurance application data; s2, constructing a health insurance special word vector library based on the historical insurance data; s3, inputting the feature vector into a pre-trained hierarchical disease recognition model, and obtaining an output disease severity score; s4, generating an underwriting risk prompt according to the disease severity score, and sending the underwriting risk prompt to an underwriting operation terminal; and S5, collecting an artificial underwriting decision result for the underwriting risk prompt, and taking the artificial underwriting decision result as a new training sample to carry out incremental updating on the hierarchical disease recognition model. According to the method, the non-standard disease description analysis precision is improved by fusing the claim co-occurrence word vector and the vertical domain attention mechanism.

The application provides a multi-modal medical report structured analysis method, system, device, medium and program product, and relates to the technical field of deep learning and medical image report analysis. The method comprises the following steps: identifying an input multi-modal file to obtain content information contained in the file; performing feature extraction on the content information to obtain a disease feature vector, and performing weighting processing on the disease feature vector to obtain corresponding disease information; performing weighted evaluation and marking on the disease data, and outputting the marked disease data. The application has the advantages of workflow context optimization and scalability based on modular skill units, noise robustness and illusion suppression in multi-modal data fusion, and disease extraction and missing detection avoidance with fine-grained context perception.

The invention relates to the technical field of pavement disease detection, and discloses a pavement disease intelligent positioning and quantifying method, which comprises the following steps: acquiring road surface image data and navigation data corresponding to the road surface image data; based on the morphological information of the disease, quantifying the disease length, the disease area, the disease average width and the disease maximum width to obtain quantified disease characteristics; establishing a corresponding relation between the image and the acquisition sequence according to the quantized disease characteristics, and searching navigation data corresponding to the skeleton pixel points; calculating latitude and longitude coordinates of scanning line boundary points according to latitude and longitude coordinates of a scanning center point in the navigation data, and converting any skeleton pixel point in the image into geographic coordinates of a disease so as to realize positioning of a disease target in a real geographic space; and making a disease GIS map based on the geographic coordinates of the diseases and the quantitative disease characteristics. According to the invention, each pixel point in the image is regarded as an independent positioning unit, so that the morphology of the disease can be presented more accurately.

The invention relates to a bridge disease identification method and device, and the method comprises the steps: carrying out the disease simulation of a typical working condition on a bridge laid with a grating array, and obtaining a multi-label training set; the typical working conditions comprise single plate stress, support disengagement, bridge unbalance loading and balance weight grade change; analyzing the disease characteristics of single plate stress, support disengagement, bridge unbalance loading and balance weight grade change, and determining at least one characteristic value; training a preset disease recognition model according to the multi-label training set and the at least one feature value of each disease feature to obtain a target disease recognition model; detecting the grating strain data of the bridge according to the target disease recognition model to obtain a disease recognition result; by constructing a multi-label training set, extracting typical disease characteristic values and training an intelligent recognition model, synchronous automatic recognition of single plate stress, support disengaging, unbalance loading and balance weight grade change is achieved, and the method has the advantages that the disease recognition efficiency and accuracy are improved, and synchronous recognition of multiple diseases is achieved.

The invention relates to the technical field of data processing, and discloses a walnut disease grading prevention and control method and system and a storage medium. The method comprises the steps of collecting multi-band spectrum and temperature data of walnut trees to calculate disease characteristic indexes, constructing an orchard network according to inter-tree positions and propagation relations, inputting a graph convolutional network to extract time-space correlation information and output a disease grading result, and performing region division based on the grading result to determine differentiated medicament schemes. And preparing an environmental response type sustained release preparation, and carrying out targeted drug delivery according to a zoning scheme. The problems that in the prior art, disease diagnosis efficiency is low, grading subjectivity is high, and a space-time propagation rule cannot be captured are solved.

The application discloses a multi-center depression recognition method and system based on decoupling of cross-subject relationship networks, relates to the technical field of medical image processing and artificial intelligence diagnosis, and comprises the following steps: acquiring subject data of a plurality of collection centers, wherein the subject data comprises brain image data; constructing an initial individual brain function network based on the brain image data; performing individual brain network representation learning on the initial individual brain function network to obtain a subject-level brain network representation; and performing decoupling learning and joint optimization based on disease-related and collection center-related cross-subject relationship networks, and then guiding iterative updating of the individual brain network through a group-level disease discrimination representation, so that the common characteristics related to depression can be better mined, the influence of the differences between the collection centers on feature learning is weakened, the representation and discrimination ability of the model for disease characteristics is improved, the model has more stable performance in a multi-center scene, and the generalization performance of cross-center data is improved.

The invention relates to the field of intelligent agriculture, and particularly discloses a macadamia nut disease detection and recognition method based on image recognition, which comprises the following steps: S100: image data acquisition: acquiring high-definition digital images of leaves and fruits at the middle and upper parts of a macadamia nut tree canopy from multiple angles under a natural light condition, the method comprises the following steps: collecting an object covering healthy leaves and disease samples which are confirmed as anthracnose, leaf blight and stem canker on site by agricultural experts, and accurately marking each collected image to generate a data set containing image file names and corresponding disease category labels, wherein the disease samples comprise different disease severity degrees, leaf growth stages and background environments; s200, performing image input processing, and taking the data set obtained in S100 as input data; s300, image processing is carried out; s400, obtaining a final recognition result; according to the technical scheme, disease features can be learned in a self-adaptive mode, complex environment interference is resisted, and recognition precision and efficiency are both considered.

The present application relates to the technical field of image generation, and in particular to an image generation method based on a generative model, a medium and equipment, a multi-channel conditional input tensor graph is constructed through disease type and spatial attribute information, different disease types are mapped according to different channels, non-zero pixel marks disease areas, so that the target image generation model can learn and generate disease characteristics by channel, and mutual interference of multi-disease characteristics is avoided; through a spatial self-adaptive normalization module built in the generator, spatial difference modulation parameters are predicted by using the multi-channel conditional input tensor graph, so that the model can inject disease types and spatial attribute information into feature maps at each intermediate generation stage, and disease and non-disease areas are processed in a targeted manner, the relative characteristic relationship of the disease area is retained, high-frequency texture details are strengthened, and the local characteristics of the target disease image are enriched, so that high-quality image generation can be controlled according to user-specified attributes.