79 results about "Early identifications" patented technology

This invention relates to the field of medical information management technology, including an AI-based endometriosis management system. The system comprises a thickness dynamic monitoring module, an inflammation level assessment module, a lesion spread analysis module, a periodic lesion tracking module, and a disease progression analysis module. In this invention, AI is used to segment ultrasound image data, enabling dynamic monitoring of lesion areas. Lesion boundaries are extracted, and changes in local inflammatory marker concentrations are calculated, making the screening of abnormal inflammatory areas more accurate and enabling early identification of lesion development. The tracking of periodic lesions, combined with analysis of lesion contour changes and area increases / decreases, enhances the quantitative control of disease progression. The future development trend of lesions, combined with the calculation of periodic inflammatory pathways, makes the prediction of disease evolution more consistent with physiological changes. Multi-dimensional lesion analysis combined with AI intelligent calculation expands the diagnosis and treatment of endometriosis from static assessment to dynamic trend prediction, improving the adaptability of personalized treatment plans.

This invention provides a method and apparatus for early identification of cotton fields based on differences in crop planting and management, relating to the field of smart agriculture technology. The method involves acquiring surface reflectance data of a target area during historical years' mulching periods to determine a surface reflectance image sequence. Based on the image sequence, a mulching difference index and an average texture feature index are determined to characterize the difference in mulched area ratio between cotton and non-cotton fields. A classifier is trained using the mulching difference index, the average texture feature index, and the location information of cotton and non-cotton fields from historical years to obtain an early cotton field identification model. The surface reflectance data of the target area during the current year's mulching period is input into the model to obtain the early cotton field identification results. This invention enables early mapping and meets the early management decision-making needs of cotton in areas such as irrigation water allocation and agricultural insurance, without waiting for cotton canopy development or canopy closure.

The present invention relates to a composition, a diagnostic kit, and a diagnostic method for diagnosing drought stress in plants. Specifically, the present invention provides a composition comprising a biomarker consisting of one or more genes selected from the gene group listed in Table 1, and a kit and method capable of diagnosing the drought stress state of a plant using the same. The composition and diagnostic method according to the present invention have the effect of rapidly and accurately determining whether a plant is under drought stress by quantitatively measuring the expression level of a specific biomarker gene, thereby enabling the early identification of the growth status and appropriate countermeasures.

This invention discloses a method for identifying intrahepatic cholangiocarcinoma (ICC) based on serum peptide characteristics and its application. The invention collects serum samples from three groups: individuals with ICC, those with benign liver disease, and healthy controls. Peptide characteristic peaks associated with ICC are screened, and a model is constructed using a logistic regression algorithm. This model demonstrates excellent discriminative ability on both the training and independent test sets, with AUCs reaching 0.986 and 0.963, respectively, significantly outperforming traditional tumor markers CA19-9 and CEA. The peptide characteristic peak combination and the discriminative model constructed based on it can be used for early detection of ICC, differentiation between benign and malignant cases, and screening of high-risk populations. It offers advantages such as rapid detection, standardized procedures, strong early ICC identification capability, and low false positive rate, providing a highly sensitive, specific, and widely applicable non-invasive screening tool for clinical use.

This invention relates to the field of construction monitoring technology, specifically to a method and system for monitoring structural offsets using BIM technology. The method involves using a multi-source sensor cluster to collect real-time location information of multiple components within the construction structure, obtaining real-time monitoring data. Based on the real-time monitoring data and the preset coordinates of the components in the BIM model, a first offset evaluation index for the target component is calculated. The component importance is then obtained. The first offset evaluation index is corrected based on the component importance score to obtain a second offset evaluation index. This second offset evaluation index is compared with a preset offset threshold to determine monitoring and early warning information. This approach enables dynamic assessment of offset risks, establishes an early identification mechanism for potential problems, and optimizes the early warning system. It can dynamically capture offset trends and comprehensively consider them, achieving early risk identification and optimized early warning, thereby improving the accuracy and efficiency of construction structures.

The application discloses a combined biomarker for predicting hypotension in dialysis and a detection kit thereof, and the combined biomarker is 3-hydroxyisovaleryl carnitine, valine, phenylalanine and decadienoyl carnitine. The combined biomarker can better predict the risk of hypotension in dialysis of a subject in a discovery phase (AUC 0.804, sensitivity 77.5%) and an independent sample verification phase (AUC 0.804, sensitivity 90.0%). The detection method has the advantages of simple operation, few detection indexes, low cost, good specificity, high sensitivity, good repeatability and the like, and provides a simple and reliable method for early identification of a high-risk person of hypotension in dialysis and implementation of prevention intervention.

This invention discloses a postoperative bile fistula risk prediction system for hepatobiliary surgery patients, belonging to the field of bile fistula risk data processing technology. The system includes an evidence-driven variable acquisition module, a standardization judgment module, a time-series feature generation module, a feature screening module, and a dynamic prediction module. It performs time-series reconstruction and evolutionary feature extraction for dynamic monitoring indicators of postoperative bile extravasation. Through a structural-biochemical consistency constraint screening method, it fuses and filters features of structural risk channels, biochemical risk channels, and time-series evolution channels. Based on core predictive feature data, it constructs a bile fistula risk prediction model and dynamically predicts the probability of bile fistula occurrence at different postoperative time points through a time-series rolling update mechanism, outputting risk levels and early warning results. This invention enables early identification, dynamic assessment, and individualized intervention support for bile fistula risk, improving the accuracy of prediction results and its clinical application value.

The present application belongs to the technical field of medical artificial intelligence auxiliary decision-making, and particularly relates to a method for predicting postoperative delirium risk of elderly abdominal surgery patients based on perioperative clinical characteristics. The present application extracts the demographic characteristics, preoperative laboratory indicators and intraoperative anesthesia-related characteristics of elderly abdominal surgery patients based on an electronic medical record system, and constructs a full-feature data set for postoperative delirium risk prediction. Further, a machine learning algorithm is used to establish a postoperative delirium risk prediction model, and the target model is screened in combination with the model stability evaluation result. On this basis, the model is optimized by using SHAP feature contribution analysis and recursive feature elimination strategy, and a simplified prediction model with fewer variables and better generalization performance is obtained. The prediction model can realize early identification of high-risk groups of postoperative delirium of elderly abdominal surgery patients during the perioperative period, has high discrimination ability, stability and clinical applicability, and can provide a reliable tool for precise prevention and auxiliary decision-making during the perioperative period.

This invention discloses an intelligent scheduling method and system for temperature-controlled paths in the cold chain distribution of poultry products, belonging to the field of cold chain logistics technology. By dividing the poultry product cold chain distribution network into several sub-paths and deploying temperature sensors, humidity sensors, and multispectral imaging units, a data acquisition time series is constructed to achieve multi-source monitoring of the environmental conditions in the distribution process. Based on image data, high-incidence temperature control failure areas are standardized and visual characteristics of quality deterioration are detected, enabling early identification of potential temperature control risks. Temperature control disturbance characteristic values ​​are calculated by combining temperature signals, humidity signals, and visual information, and the disturbance evolution amplitude and risk trend values ​​are calculated based on the time series to form a comprehensive temperature control characteristic value. The node temperature control correlation index is calculated and combined with thresholds for screening, enabling dynamic scheduling and early warning of abnormal temperature control paths. This improves temperature control accuracy and sensitivity, allows for rapid location of abnormal paths, and enhances the safety and management efficiency of the poultry product cold chain distribution.

A method, system, device, and medium for processing symptom cluster data of head and neck cancer patients based on sentinel symptoms are disclosed. The method includes: obtaining symptom data of head and neck cancer patients during radiotherapy and chemotherapy through longitudinal surveys; processing the symptom data using exploratory factor analysis to extract several symptom clusters; performing association rule analysis on the symptom cluster data based on the Apriori algorithm, and determining the sentinel symptoms within each symptom cluster by combining the time of first symptom appearance; and constructing an intervention plan for gastrointestinal symptoms using the sentinel symptoms as intervention targets. This invention achieves accurate determination of sentinel symptoms through longitudinal survey data, combined with the Apriori algorithm and the time of first symptom appearance, and constructs intervention plans based on the MRC complex intervention framework, realizing early symptom identification and providing precise and personalized symptom management support for head and neck cancer patients.

The present application relates to the technical field of coal mine safety production monitoring, and specifically discloses a coal mine hidden danger intelligent identification system and early warning device fusing AI video analysis. The system comprises a video acquisition module, an AI visual analysis and calculation module, a three-dimensional situation generation module, an early warning decision and path planning module, a device control module and a feedback adjustment module. The video acquisition module acquires coal flow surface images in real time, the AI visual analysis and calculation module adopts an optical flow method to track particle motion characteristics and outputs arching probability indexes, the three-dimensional situation generation module converts two-dimensional coordinates into arching point three-dimensional coordinates, the early warning decision and path planning module generates a collision-free motion trajectory, the device control module drives the early warning device to perform an arch breaking action, and the feedback adjustment module dynamically adjusts arch breaking parameters according to updated arching probability indexes after arch breaking. The present application realizes early identification, accurate positioning and adaptive disposal of coal bunker blockage hidden dangers in coal mines, and significantly improves the safety level of underground production.

This application provides a method and system for early debris flow identification based on SAR technology combined with a 3D model. Interferometric SAR data is selected as the wide-area coverage data for the target area, and fully polarimetric SAR data is selected as the local refined data for the target area. Phase unwrapping is performed on the wide-area coverage data to generate a wide-area digital elevation model (DEM). Polarization decomposition and phase optimization are performed on the local refined data to generate a local DEM. The wide-area DEM and the local DEM are fused to obtain a hybrid DEM. Based on the hybrid DEM, topographic factors are extracted to generate DEM spatial data to support debris flow susceptibility analysis. Early debris flow identification is performed based on historical debris flow data, historical precipitation data, regional soil moisture content, and DEM spatial data, yielding identification results. A differential monitoring and early warning system based on SAR technology and a 3D model is established. Remote sensing data generated by SAR technology is applied to debris flow disaster identification, improving the early identification capability of debris flow hazards and conducting debris flow hazard deformation prediction analysis.

This invention relates to the biomedical field, specifically to biomarkers, kits, and their applications for assessing immune dysfunction in HIV-infected individuals. This invention discloses the application of the molecular marker SMPD1 and / or substances that detect said molecular marker in the assessment, auxiliary assessment, screening, and risk stratification of immune dysfunction in HIV-infected patients with hyperlipidemia. This invention is the first to identify an immunometabolic paralysis subtype in HIV-infected individuals with hyperlipidemia. SMPD1 expression was significantly upregulated in these subtype patients, and its high expression was significantly associated with the risk of immune reconstitution failure. A combined diagnostic model based on SMPD1 and triglycerides demonstrated excellent diagnostic efficacy. Therefore, substances that detect SMPD1 can be used for the assessment and risk stratification of immune dysfunction in HIV-infected patients with hyperlipidemia. This invention provides an effective tool for the early identification and risk stratification of these patients and has broad clinical application prospects.

This invention discloses a risk prediction device and storage medium for metabolic-associated fatty liver disease in children or adolescents. The two-stage cascaded screening strategy and model of this invention provide a highly accurate, accessible, and scalable solution for the early identification of metabolic-associated fatty liver disease in children or adolescents. This invention utilizes different anthropometric data to identify high-risk groups in routine school physical examinations, followed by further screening using a comprehensive serological index at community health centers, thereby achieving accurate diagnosis of metabolic-associated fatty liver disease and providing clinicians with a non-invasive auxiliary diagnostic tool that combines high precision, high accuracy, and broad accessibility.

The application discloses an ultrasound examination and clinical management method for a fetus with umbilical cord torsion and related equipment. The application triggers a suspected umbilical cord torsion risk identification process based on the complaint information of a pregnant woman, and determines whether to enter key ultrasound screening through preliminary assessment. Through multi-section analysis of ultrasound image data, the morphological characteristics of the umbilical cord are extracted, and whether the fetus has growth restriction is evaluated in combination with the fetal growth parameters to form an ultrasound key screening result. Based on the preliminary assessment result and the ultrasound screening result, the risk level of the fetus is determined, and a corresponding clinical intervention strategy is formulated according to the risk level. Through stratified screening, information fusion and risk grading, the standardized management of the fetus with suspected umbilical cord torsion is realized, the early identification ability of the umbilical cord torsion is improved, the timeliness of clinical intervention is improved, and effective decision support is provided for the clinic.

This invention discloses a heart failure readmission risk assessment system based on cardiac imaging feature analysis, belonging to the field of cardiac imaging analysis technology. By analyzing speckle texture features in ultrasound images and combining them with respiratory phase and image quality information, a dynamic and regular quantitative index is generated, thereby achieving accurate risk assessment. It can identify potentially high-risk patients in advance, even those with normal or mildly abnormal ejection fractions, and effectively addresses the impact of respiratory phase changes and image quality interference on assessment results. By weighted fusion of image quality and physiological state information, the stability and accuracy of the assessment results are improved, making it particularly suitable for primary healthcare institutions. This invention provides a more objective and accurate risk assessment, offering a scientific basis for doctors to develop personalized treatment plans, thereby significantly improving the early identification and treatment outcomes of heart failure patients.

The application provides an early warning and automatic fire extinguishing system based on image fire identification and sound wave positioning, relates to the technical field of fire prevention and control, and comprises a multispectral image acquisition module, a sound wave detection array module, a central fusion control unit, a hierarchical warning module, a fire extinguishing execution module, a fire extinguishing strategy database and a cloud data processing module, the multispectral image acquisition module is used for collecting infrared thermal imaging images and visible light high-definition images of a monitoring area in real time; the application adopts a flame early identification algorithm of multispectral image and dynamic feature fusion, carries out double verification through infrared image high-temperature abnormal area screening and visible light image flame dynamic feature extraction, combines time sequence continuous matching and area growth trend judgment, greatly reduces the false alarm rate caused by interference factors such as strong light reflection, welding sparks and high-temperature steam, can accurately identify the early stage of smoldering fire and open fire, improves the identification accuracy, and gains valuable time for early fire disposal.

This invention discloses a method and related equipment for predicting and intervening in the risk of stillbirth related to umbilical cord torsion. The method acquires multimodal clinical data, including the pregnant woman's complaints and prenatal ultrasound examination information, and standardizes the data to extract core predictive factors such as changes in fetal movement, signs of umbilical cord root torsion, and fetal growth restriction, as well as auxiliary predictive factors such as abnormal amniotic fluid volume and abnormal placental-umbilical cord insertion, constructing a feature vector that the model can process. Based on a pre-trained risk prediction model, the risk of stillbirth related to umbilical cord torsion is quantitatively assessed, a comprehensive risk score is output, and risk levels are classified. Based on the risk level, a pre-set clinical intervention strategy is automatically matched to form standardized treatment recommendations, which are then output to the clinical terminal. This method overcomes the limitations of traditional methods that rely on low sensitivity of single ultrasound diagnosis, achieving early risk identification and tiered management.

This invention belongs to the field of biomedical technology and discloses a bile acid-based predictive model for the severity of acute pancreatitis, its detection method, and its application. This invention establishes a combination of biomarkers that accurately reflect the pathological changes of acute pancreatitis by performing targeted metabolomics analysis on multiple small bile acid metabolites in serum, and further constructs a predictive model suitable for early clinical warning and severity stratification. This invention also provides a corresponding detection method for rapid and quantitative detection of the bile acid metabolites, enabling early identification and auxiliary diagnosis of severe acute pancreatitis.

The application discloses a kind of adenocarcinoma risk prediction methods, by collecting the cell thin layer diagnosis and treatment data of different diagnosis and treatment time nodes of the same patient, constructs patient lesion longitudinal health data set, generates cell timing health data sample set;Based on the cell timing health data sample set, the first health assessment feature, the second health assessment feature and the third health assessment feature are extracted to carry out multi-dimensional health data splicing and cross-dimension weight fusion processing, generate lesion evolution comprehensive health data;The lesion evolution comprehensive health data is input into health data evaluation model, quantitatively calculates the individual health risk index of patient adenocarcinoma long-term progress, generates individual health risk assessment result output, realizes single physiological cell level dynamic evolution analysis, improves the accuracy of adenocarcinoma early identification and progress risk assessment, especially in the prediction field of cervical adenocarcinoma has more accurate prediction effect.

This application provides a method, apparatus, device, and medium for detecting physiological state. The method includes: first, acquiring the potential signal and environmental parameters from the skin surface of the worker; then, processing the potential signal using a signal inversion algorithm to determine the cell ion concentration; next, extracting the characteristic frequencies of the environmental parameters if the cell ion concentration is within a preset threshold; further, calculating a dynamic equilibrium coefficient based on the cell ion concentration, potential signal, and characteristic frequencies; and finally, determining the worker's physiological state based on the dynamic equilibrium coefficient. This method enables early identification of latent fatigue at the cellular level, improving workplace safety.

This invention discloses a method for monitoring and early warning of natural gas pipeline leakage risks based on big data analysis, belonging to the field of pipeline safety monitoring technology. It includes processing multi-source operational data, generating pressure and flow feature vectors and acoustic feature matrices, and performing spatiotemporal fusion. Historical maintenance records are used to enhance the context of the fused data, and the enhanced features are input into a risk identification network along with meteorological data to calculate the comprehensive risk score and level for each location. For high-risk locations, multi-step risk evolution simulation is performed to predict future trend curves and high-risk time windows. All results are combined with spatial information to generate a dynamic distribution map, based on which graded, regional, and time-sensitive early warning commands are issued. This method achieves deep risk identification by integrating historical knowledge and realizes predictive early warning by combining dynamic simulation, improving the early identification capability of hidden dangers and the predictability of early warnings.

The present application relates to the technical field of intelligent image recognition, and particularly relates to a fruit and vegetable disease image recognition grading early warning method and system, comprising: acquiring time sequence images of the same target leaf, and registering the time sequence images based on the contour features and main vein endpoint features of the leaf; extracting the leaf vein structure in the registered images, establishing a leaf vein conduction network containing nodes and paths according to the connection relationship and thickness of the leaf vein, and dividing the leaf vein levels. The present application realizes early identification and grading early warning of fruit and vegetable diseases by continuously observing the same target leaf in time sequence, combining the structural features of the leaf vein conduction network and the color gradient variation law along the vein. The biological characteristics of disease spreading along the leaf vein first are utilized, and the leaf vein system is taken as the main carrier for disease monitoring, so that the problem that the prior art does not distinguish the different propagation characteristics of diseases in the leaf vein and the leaf tissue is solved, and the extremely light and slightly colored areas formed around the leaf vein in the early stage of the disease can be effectively captured.

The application provides application of intestinal flora as a marker in preparation of a product for evaluating treatment effect of inflammatory bowel disease. The feasibility of using a microbial spectrum to predict the curative effect of drug treatment on patients with inflammatory bowel disease is achieved, and the achievement can accurately identify patients who need to optimize the treatment scheme in the early stage of the disease, thereby significantly improving the prognosis of the patients. Meanwhile, a construction method of an intestinal flora biomarker prediction model is provided, and through the combination of Faecalibacterium prausnitzii, Blautia massiliensis and Phascolarctobacterium faecium, the precise prediction of the invalid condition of drug treatment is realized, which highlights the potential of the new tool for early identification of patients who may need to optimize the treatment.

This application relates to the field of industry security joint defense technology and discloses an industry security joint defense method based on federated game theory. This method collects multi-source data reported by each joint defense member, performs cross-organizational correlation analysis on threat perception data to identify early threat signals spreading across multiple organizations, thereby quantifying the threat pressure experienced by each member. It then assesses the cooperation risk by combining the resource status and behavioral characteristics of each member, and further infers the diffusion process of risk from individual members to the overall joint defense network based on cooperation risk and trust assessment data. Finally, it generates collaborative tasks and incentive constraint schemes for each member based on the risk evolution prediction results. This application achieves early identification of cross-organizational threats and quantitative assessment of cooperation risks, and can generate differentiated collaborative defense schemes in the early stages of threat diffusion, improving the overall defense effectiveness of the industry joint defense system.

This application provides a method for damage detection and early warning of conveyor belts based on multispectral vibration sensing, applied in the fields of mine safety monitoring and industrial equipment fault diagnosis. The method includes: collecting the multispectral radiation response and structural vibration response of the conveyor belt during operation; constructing a joint distribution map of energy absorption and phase hysteresis to identify spectral anomaly regions; analyzing the consistency of vibration propagation to locate regions of enhanced vibration reflection or abnormal dissipation; performing spatiotemporal consistency verification on spectral anomalies and vibration distortion characteristics to confirm structural damage and its spatial location; tracking the rate of change of vibration reflection and the spectral spread rate to construct physical criteria for damage evolution and output risk level warnings. This invention, through multispectral and vibration dual-mode fusion detection and spatiotemporal verification, achieves early identification and precise location of internal damage to the conveyor belt, effectively preventing serious accidents such as belt breakage and improving the predictive maintenance level of equipment operation.

This application relates to the field of infectious disease surveillance technology, providing a method, device, and electronic device for assessing the impact of infectious diseases based on public observation. The method includes: acquiring social media text related to a target region and a target infectious disease within a target time period; constructing a heterogeneous location association network based on the posting location, mention location, and sentiment attributes of the social media text; identifying potential risk points of the target infectious disease based on the heterogeneous location association network; and assessing the impact of the target infectious disease based on the potential risk points. This application enables early identification of potential risk points and early assessment of the impact of infectious diseases, gaining a crucial time window for subsequent prevention and control responses, thereby supporting accurate and proactive early warning and prevention and control decisions.

The application discloses a kind of crop disease early identification method based on computer vision, belong to image recognition technical field, specifically include: acquisition crop leaf under multiple continuous excitation period under the delayed fluorescence time series image;Extract the fluorescence intensity value of each grid unit in each cycle and construct response curve;Each curve is numerically fitted, and fluorescence kinetic characteristic vector is extracted;The characteristic vector of all grid units in the same cycle is arranged according to spatial position, and feature distribution diagram is formed;The trend of change of the characteristic value of each spatial position in the continuous cycle is tracked, and fluorescence kinetic abnormal point is identified;Abnormal points are spatially clustered and merged into abnormal regions, the average rate of change of the characteristic intensity of each region and the average rate of area expansion are calculated and combined into kinetic evolution characteristic vector;Evolution characteristic vector is matched with pre-constructed disease response mode library, and the corresponding disease type is output.The application realizes the non-invasive early identification of disease incubation period.

The application discloses a VTE real-time monitoring and intelligent prevention and treatment system and belongs to the technical field of intelligent prevention and treatment, comprising: a baseline construction module, which is used for collecting multi-dimensional VTE parameters, calculating the normal fluctuation range of each parameter to form an initial individual baseline, and constructing an adaptive individual baseline through threshold calibration; a trend identification module, which is used for dynamically setting the length of a sliding window, calculating the trend slope and VTE accumulation bias of VTE, constructing trend constraints and accumulation bias constraints, and determining that there is a continuous abnormal deviation when both constraints are not met at the same time and the length of continuous deviation exceeds a certain time; a time sequence risk prediction module, which is used for calculating the deviation value of each parameter of a target patient, constructing a space-time fusion feature matrix, inputting a time sequence risk prediction model, and outputting a VTE risk probability; and an early warning intervention module, which is used for double determination intervention, setting three-level early warning and intervention measures, calculating an improvement rate to verify the prevention and control effect in real time, and realizing early identification and intelligent prevention and control of VTE.