376 results about "Early detection" patented technology

The invention discloses a power transformation equipment working condition detection system and method based on self-adaption, and relates to the field of power equipment state monitoring. The method comprises the following steps: collecting multi-source monitoring data of power transformation equipment and carrying out standardization processing to generate standard time sequence data; dividing a time window based on working conditions, extracting characteristic parameters, and constructing a characteristic track; establishing and updating an adaptive reference library containing standard reference points and health tolerance boundaries under different working conditions based on historical health data; comparing the real-time characteristic track with a standard reference point and a health tolerance boundary of a corresponding working condition in a self-adaptive reference library, calculating a deviation degree and generating an operation health index; and determining a dynamic early warning threshold value according to the historical operation health index data, and judging the state of the equipment by comparing the real-time operation health index with the dynamic early warning threshold value and a preset rigid alarm threshold value. And early discovery of weak degradation of the internal coordination relation of the power transformation equipment is realized by constructing a comparison mechanism of a self-adaptive health benchmark and a feature trajectory.

The invention discloses a diabetic foot early-stage detection method combining infrared and visible light imaging, and relates to the technical field of diabetic foot medical imaging diagnos.The diabetic foot early-stage detection method comprises the steps that multi-mode image collection and standardization processing are conducted, infrared images and visible light images are synchronously collected, and standardization processing such as size normalization is conducted; image registration and space alignment are carried out, marking points are set based on foot anatomical features, and feature extraction, mismatching point elimination and transformation matrix calculation are carried out; extracting and screening multi-dimensional features, extracting temperature and structural features, and screening by using a Relief-F algorithm; feature lesion recognition and classification are fused, and lesion probability is output through a double-branch convolutional neural network; carrying out detection result verification and feedback optimization, and comparing a clinical diagnosis optimization model; and generating a detection report and storing data, and generating a report containing the fused image. The early lesion detection precision is improved through multi-modal fusion, individual and environment differences are adjusted and adapted in a personalized mode, and reliable technical support is provided for clinic.

The invention provides a cardiovascular trend prediction method and system based on time sequence medical health data, and relates to the technical field of medical health data analysis. The method comprises the following steps: collecting time sequence medical health data of a patient through a multi-source sensor, wherein the time sequence medical health data comprises dynamic physiological indexes such as heart rate, blood pressure and oxyhemoglobin saturation; performing time calibration and feature extraction on the acquired multi-source data; constructing a time sequence feature model based on the time sequence features, and fusing the time sequence feature model with the static information of the patient to generate a comprehensive feature vector; using a deep learning model to train historical data, and learning a dynamic change rule of cardiovascular health indexes; predicting the change trend of the cardiovascular health indexes in real time, and evaluating the risk level of cardiovascular diseases; the model parameters are optimized through online learning, the prediction precision is improved, the change trend of cardiovascular health indexes can be predicted in real time, and support is provided for early discovery and personalized medical treatment of cardiovascular diseases.

The invention relates to the technical field of power equipment fault detection, in particular to a fault detection method for a ring main unit, and aims to solve the problems that in the prior art, the ring main unit of an urban power distribution network and an industrial park faces complex working condition challenges for a long time, and the challenges include severe load fluctuation, electromagnetic interference superposition and multi-node collaborative abnormity. According to the method, the problem of multi-node collaborative anomaly detection under complex working conditions is effectively solved, and the risk of misjudgment caused by electromagnetic interference and load fluctuation is reduced. The position of an abnormal terminal is accurately positioned, the fault cause is judged, and operation and maintenance resource waste caused by blind inspection in a traditional method is avoided. Through the synergistic effect of multi-modal data fusion and topology learning, the early slight anomaly detection rate is remarkably improved, and progressive faults are prevented from developing into equipment damage accidents.

The invention relates to the technical field of energy storage Internet of Things and digital twinning, in particular to a digital twinning model data processing method applied to the energy storage Internet of Things, which performs multi-modal data synchronous acquisition through a three-in-one sensor, optimizes storage management through a dynamic cache allocation mechanism, and improves the reliability of data acquisition through the three-in-one sensor. Micro short circuit and voltage unbalance detection is completed through high-frequency harmonic extraction and difference frequency voltage scanning, the event cross trigger establishes a bidirectional feedback mechanism, and the lightweight decision tree verifier outputs a fault code. According to the digital twinborn model data processing method applied to the energy storage Internet of Things, the early detection sensitivity of micro short circuit and voltage imbalance is high, outdated redundant calculation is avoided, parameter configuration is optimized through a periodic diagnosis report, different conditions are flexibly adapted, and support is provided for an intelligent health management system of energy storage equipment.

The invention, which belongs to the technical field of medical image processing and computer vision, discloses a multispectral imaging-assisted post-radiotherapy skin injury early detection system comprising a multispectral data acquisition module, a deep tissue feature extraction module, a three-dimensional lesion segmentation module and a space-time tracking evaluation module. The subcutaneous 2.5 cm depth tissue information is obtained through multispectral imaging at the wave band of 400-1350nm, blood perfusion, melanin concentration, collagen structure and other physiological parameters are inversed based on the radiation transfer theory, a three-dimensional medical image segmentation algorithm is adopted to achieve three-dimensional accurate segmentation of an injury area, a spatio-temporal evolution model is established to predict the injury development trend, and the damage development trend is predicted. The radioactive skin injury can be detected in the subclinical period, the detection time window is advanced by 5.2 days on average, the occurrence rate of severe dermatitis is reduced by 65%, and a basis is provided for clinical timely intervention.

The invention discloses a digital management system for the health state of a patient after an anorectal operation, and relates to the technical field of digital management of the health state after the operation. Comprising a data acquisition and processing module which is used for calling an anorectal postoperative clinical path rule engine based on real-time symptom data input by a patient and the number of postoperative days, and dynamically generating and pushing a corresponding structured interactive data acquisition sequence; and the feature extraction module is used for carrying out real-time feature extraction on a postoperative recovery related image provided by a patient through a preset image analysis model at a patient terminal side so as to generate a multi-modal structured data set containing time sequence physiological indexes and image feature vectors. According to the method, clinical priori knowledge is fused through the multi-modal neural network, a compound and atypical early complication mode is deeply recognized from time sequence physiological indexes and image features, the early discovery rate of complication is increased, and the false alarm rate caused by conventional recovery variation is reduced through explainable risk contribution factor output.

The invention discloses an intelligent disturbance and analysis method and system for hemodialysis data, and relates to the field of hemodialysis data analys.The intelligent disturbance and analysis method comprises the steps that in the hemodialysis process, disturbance signals are superposed through a blood input pump, so that blood parameters input into a dialyzer generate preset configuration changes, and therefore input disturbance signals are formed; in the disturbance applying period, blood parameters of the blood output end of the dialyzer are collected in real time, and corresponding output response signals are formed; performing cross-correlation analysis on the input disturbance signal and the output response signal, and calculating a time delay parameter between the input disturbance signal and the output response signal; periodically and repeatedly detecting, carrying out regression analysis on the time delay parameter, and when the time delay parameter is continuously increased along with time, judging that the dialyzer has a blockage trend and outputting early warning information. According to the method, early detection on the dialyzer blockage is realized through the dynamic time-lag characteristics under the condition of not increasing a sensor, and the safety of a hemodialysis system is improved.

The invention relates to a spinal degenerative disease grading early warning and rehabilitation management system and method, and belongs to the technical field of medical equipment. The system comprises a system control module, a first-stage detection module, a second-stage detection module, a third-stage detection module, a data processing module, an evaluation module and a rehabilitation management module. The first-stage detection module is used for acquiring foot pressure distribution data of a patient; the secondary detection module collects electromyographic signal data of muscles around the spine of the patient; the third-stage detection module is used for collecting vertebral bone mineral density data of the patient. The data processing module is used for preprocessing and analyzing the collected data; the evaluation module is used for carrying out grading evaluation on the spinal degenerative lesion; and the rehabilitation management module formulates a personalized rehabilitation scheme according to the evaluation result. The system adopts a logic mechanism of hierarchical detection and hierarchical intervention, automatically adjusts intervention measures according to different evaluation indexes, and realizes accurate rehabilitation management. The risk of spinal degenerative diseases is found in an early stage, a targeted rehabilitation scheme is provided, and spinal diseases are effectively prevented and managed.

A PLC redundancy control method, system, and storage medium are disclosed, relating to the field of industrial control technology. An arbitration server periodically collects multi-dimensional health parameters of each redundant PLC and dynamically adjusts the weights of these parameters based on this data. This ensures that the health score criteria accurately reflect actual operational risks, avoiding misjudgments caused by static weights. The arbitration server's time-series prediction model calculates early warning health scores based on historical data and weight change trends. This allows for early detection of potential future failure trends in redundant PLCs. Combined with dual threshold decision-making, proactive early warning switching of redundant PLCs can be implemented, achieving accurate judgment and real-time maintenance of PLC primary and backup roles.

Described herein are gene features that provide prognosis, diagnosis, treatment and molecular subtype classification of cancer by genomic and epigenomic profiling, including immune checkpoint regulators such as Programmed Death Ligand 1 (PDL-1). Using the methods and compositions described herein, specific and sensitive detection of biomarkers of interest is provided. Such biomarkers indicate disease pathogenesis, which provides opportunities for selection of treatments, including treatment regimens intended to overcome tolerance mechanisms.

The invention discloses a preparation method and application of near-infrared fluorescent dye DOM-O for acute alcoholic liver injury detection. The specific chemical structural formula of the near-infrared fluorescent dye DOM-O is shown in the specification. The near-infrared fluorescent dye prepared by the invention has excellent fluorescence imaging performance, can realize specific fluorescence imaging diagnosis of the acute alcoholic liver injury, and provides an imaging dye with application prospect for early detection and clinical diagnosis of the acute alcoholic liver injury.

The invention relates to the field of seismology, signal processing and artificial intelligence, namely to automated methods and systems for early detection of earthquakes based on the analysis of seismic data, and can be used to recognize and classify longitudinal waves (P-waves) preceding the main seismic shocks, using deep learning and computer vision methods. The aim of the present invention is to create an automated early earthquake detection system using the classification of seismic signal spectrograms generated by the complex Morlet wave CWT using the YOLO deep neural network architecture. The technical result is an increase in the accuracy and speed of early earthquake detection by analyzing the time-frequency characteristics of seismic signals and automatically localizing P-wave signatures using a neural network model. The device includes a seismic sensor, an analog-to-digital converter, and a microprocessor implementing time-frequency analysis and neural network detection algorithms. The seismic signal is recorded in real time, digitized, segmented into time intervals, and subjected to preliminary digital processing, including noise filtering and amplitude normalization. Each time interval is converted into a time-frequency representation using a continuous wavelet transform, generating a two-dimensional distribution of signal energy over time and frequency. Based on the obtained data, a spectrogram is generated and fed to the YOLO neural network detection model, which is capable of automatically detecting and localizing longitudinal P-wave signatures. Based on the neural network analysis, a determination is made regarding the presence of a P-wave and its arrival time is determined. If a predetermined threshold is exceeded, an early warning signal is generated. The system provides for data accumulation and the possibility of subsequent retraining of the neural network model.

The invention provides application of desulfurization vibrio in screening of drugs or functional food for preventing and treating sarcopenia. According to the application disclosed by the invention, a main intestinal flora desulfurization vibrio capable of promoting amyotrophy is firstly found, and the desulfurization vibrio can be used as a main target for screening sarcopenia and can be used for screening medicines or functional foods for preventing and treating sarcopenia. Sinopenia susceptible people are screened through abundance of desulfurization vibrio in excrement, and early discovery and targeted early intervention can be carried out; aiming at the mechanism that desulfurization vibrio in intestinal tracts promotes occurrence and development of sarcopenia, drugs or functional food for treating sarcopenia can be further screened, and then substances / new therapy for preventing and treating sarcopenia are developed.

The invention provides a methylation marker for detecting liver cancer and application of the methylation marker, and relates to the technical field of biomedicine. The invention provides a methylation marker combination for detecting liver cancer of each stage, the methylation marker combination comprises one or any more of BEST4 sequences of BEST4 genes or genomes, SFMBT2 sequences of SFMBT2 genes or genomes and SPAG6 sequences of SPAG6 genes or genomes, and the combination can accurately distinguish healthy people or patients with liver benign diseases from liver cancer patients and can be used for detecting the liver cancer of each stage. Moreover, early discovery and early treatment of the liver cancer can be realized, and a new direction and possibility are provided for early diagnosis and individualized treatment of the liver cancer. According to the invention, modeling is carried out by using a method based on machine learning, three different liver cancer diagnosis models are constructed, and the models are rapid in diagnosis, can realize in-vitro noninvasive, high-sensitivity and high-specificity diagnosis, and are suitable for wide popularization.

The application discloses a joint cartilage stress dynamic monitoring system based on flexible sensing and AI and belongs to the technical field of medical health monitoring.The application solves the problem that the prior art can only measure single-point pressure or strain and cannot obtain full-field three-dimensional stress distribution of a cartilage contact surface, and the system function stops at monitoring and does not form a monitoring-to-warning closed loop, quantifies uncertainty through a Bayesian neural network, provides a reliable basis for clinical decision-making, combines multidimensional biomechanical characteristics and an attention mechanism, accurately focuses on a stress key area, significantly improves the precision and individualization level of joint cartilage health evaluation, helps early detection and prevention of injury, classifies stress abnormalities, implements accurate early warning, and formulates an individualized treatment scheme according to uncertainty results and cartilage health indexes, and customizes a rehabilitation training plan according to user conditions, and improves the scientific nature of joint injury prevention and rehabilitation.

This system provides a detection system for longitudinal belt tearing in a belt conveyor that can provide early information, immediately stop operation, and minimize belt tearing. [Solution] A detection wire 2 is stretched directly below the belt B along the carrier-side roller C of the downstream belt conveyor A2 of the loading section of the belt conveyor, and connecting wires 3 and 4 are stretched near the side of the belt B. The detection wire 2 and the connecting wires 3 and 4 are connected by a detachable connector 1 that detaches when tension is applied, forming a single loop closed circuit, a first circuit X, and a second circuit Y that supplies power to the first circuit X. The system is configured such that the second circuit Y detects that the first circuit X has been cut when the detection wire 2 is cut or the detachable connector 1 comes off due to contaminants T in the conveyed material S, and then issues an appropriate alarm.

A wearable monitor is configured for early detection of internal bleeding events and particularly hematomas after vascular catheterization including cardiac catheterization. The wearable monitor is placed over an access location following the vascular catheterization procedure and the wearable monitor system periodically runs diagnostic test to detect changes in the tissue around the vascular access location. An emitter device may input an emitter input into the tissue and a detector device may then measure the emitter input after it has passed through the tissue, an emitter output. A computer may receive the data from the detector device and operate a program that determines changes in the tissue and in particular, pooling of blood in the tissue. The emitters may be electrodes that input an electromagnetic input, or an ultrasound emitter that inputs ultrasound sound waves, or a Near IR emitter that emits Near IR electromagnetic radiation.

This invention relates to the field of fatty acid detection technology, specifically to a method for determining 11 polyunsaturated fatty acids based on LC-MS / MS. The method includes: preparing a calibration solution; hydrolyzing bound fatty acids in a blood sample, adding a precipitant, centrifuging, and collecting the supernatant as the test sample; detecting the test sample using liquid chromatography-tandem mass spectrometry, and substituting the detection results into the calibration curve equation to obtain the content of the 11 polyunsaturated fatty acids in the test sample. This method can rapidly and accurately detect the content of 11 polyunsaturated fatty acids, providing a more accurate basis for clinical diagnosis and assisting in the early detection and treatment of diseases; at the same time, it can also provide more targeted nutritional advice to individuals, promoting health management.

AI-Driven Multimodal Framework Computer for Early Detection and Remote Monitoring of Chronic Disease

The present application relates to the technical field of diagnostic markers, in particular to a marker combination and its application in diagnosing active tuberculosis and distinguishing between latent tuberculosis infection and active tuberculosis. The lectin combination provided by the present application can be used as a marker for ATB diagnosis and distinguishing between LTBI and ATB, and has high specificity and sensitivity. The lectin combination provided by the present application in combination with detection of specific antibodies of mycobacterium tuberculosis antigens can further improve the diagnostic effect. The marker and its detection products provided by the present application have good application potential in ATB diagnosis and distinguishing between LTBI and ATB, and are expected to overcome the limitations of existing diagnostic techniques, improve the diagnostic accuracy and sensitivity of tuberculosis, and provide new ideas and technical means for early detection, precise treatment and effective prevention and control of tuberculosis.

The invention relates to medical detection equipment, in particular to an integrated portable instant urinalysis device for pregnant women. The invention relates to a pregnant woman urinalysis device, and aims to provide an integrated portable instant pregnant woman urinalysis device so as to solve the problem that a conventional pregnant woman urinalysis method is complex and time-consuming at the present stage, realize low-cost and rapid multi-index detection and contribute to early detection of various diseases in the gestation period. According to the technical scheme, the integrated portable instant urinalysis device for the pregnant woman is characterized in that the device is formed by correspondingly assembling an upper chip layer and a lower chip layer; a sample introduction area located in a central area and a plurality of detection channels radially arranged outwards from the sample introduction area are arranged in the device, and each detection channel comprises a reaction detection area and a waste liquid area which are led out from the sample introduction area and are sequentially communicated through a shunting channel, and air holes formed above the detection channels.

The invention discloses a fundus lesion evaluation method and device for chronic nephropathy, and the method comprises the steps: obtaining an ultra-wide-angle fundus image of a to-be-detected target object with chronic nephropathy, and extracting fundus features from the ultra-wide-angle fundus image; based on the extracted fundus features, utilizing a multivariable regression model to predict a fundus lesion state; performing multi-modal fusion processing on the fundus lesion state, the patient information of the to-be-detected target object and the clinical information; and performing lesion probability prediction processing of a dynamic confidence interval on the multi-modal fusion features to generate a chronic kidney disease fundus lesion risk level. Therefore, the fundus lesion risk grade of the chronic kidney disease can be accurately predicted, and the reliability of evaluation of the fundus lesion risk grade of the chronic kidney disease is improved; in addition, a set of evaluation standards for the risk level of fundus lesions complicated by early chronic nephropathy are also constructed, so that the purposes of early discovery and early treatment of patients are achieved, and irreversible fundus lesions caused by CKD are reduced.

The application discloses a CKD and IgAN early detection method based on blood mass spectrum analysis, utilizes liquid chromatography-tandem mass spectrometry technology to detect 24 kinds of compounds of a plasma sample in a targeted manner, utilizes a logic regression to establish an early detection model of CKD and IgAN, and verifies the prediction accuracy of CKD and IgAN in a cohort. Through analysis of plasma samples of healthy controls and CKD patients (including IgAN and non-IgAN patients), it is proved that the melatonin or melatonin+tryptophan level in the plasma is an effective method for detecting early CKD, and the melatonin, tryptophan, indole-3-lactic acid, indole-3-carboxylic acid, kynurenic acid and indole-3-methyl aldehyde are effective methods for detecting early IgAN. The application provides effective blood markers for early detection of CKD and IgAN.

This invention relates to the field of digital asset analysis technology, and more particularly to the RWA digital copyright trading system in the cultural industry. It includes constructing a cultural asset value memory network and extracting implicit cultural value transmission relationships to provide a comprehensive and accurate data foundation and value correlation basis for subsequent transaction analysis and risk assessment; calculating the discrete entropy of trading groups to promptly identify abnormal group structures, enabling early detection of potential transaction risks and abnormal market fluctuations; tracing the transmission path and calculating the attenuation coefficient when a sudden change in discrete entropy is detected to pinpoint the source and intensity of risk propagation, providing crucial clues for risk prevention and control; calculating the repricing step probability by combining transmission intensity and price fluctuations to quantitatively assess the likelihood of significant price changes in target assets; and locking suspected bubble trading nodes and isolating the links when the repricing step probability exceeds a risk threshold, effectively blocking risk spread and ensuring the stability and security of the trading system.

The invention provides a child reading disorder early detection method and system based on cognitive combination, and the method comprises the steps: generating a picture naming test data set based on a first test parameter, and generating a plurality of groups of digital sequence data sets based on a second test parameter; and displaying the pictures in the picture naming test data set to a test child object, determining a naming result when each picture is named and total consumed time when all the pictures are named, and sequentially playing the digital sequences in each group of digital sequence data set to the test child object according to a sequence that the lengths of the digital sequences increase progressively, determining a retelling result of each group of digital sequences; based on the naming result and the total time consumption, determining a correct naming time average value, and based on the retelling result, determining the number of correct retelling digital sequences; and based on the correct naming time average value and the correct retelling number sequence number, determining whether the test child object has an early risk of reading disorder. According to the invention, early risk early warning of children's reading disorder is realized.

A deep learning system that detects and counts A-lines in lung images obtained with the VSI protocol, allowing untrained personnel to perform scans. By segmenting and counting A-lines, the system distinguishes between healthy lungs and conditions such as pneumonia or edema, without a specialist intervention. The Attention U-Net architecture improves accuracy, and A-line counting provides quantitative data for diagnostics. This approach aims to expand access to diagnostics, specially in resource-limited areas, offering a scalable solution for early detection of lung disease.

The invention relates to the technical field of thyroid gland, in particular to a thyroid gland abnormity intelligent early warning system based on pattern recognition, which comprises a density distribution judgment module, a boundary amplitude recognition module, a function trend screening module, a multi-source trend fusion module and a risk level calibration module. And analyzing the pixel gray level distribution change trend of each region. According to the method, the spatial distribution and density characteristics of the thyroid ultrasound image are automatically extracted, local structure differences are subdivided, accurate capture of regional anomalies is achieved, active association between the image and data is completed in combination with dynamic changes of time sequence biochemical indexes and comprehensive trend judgment, multi-source information is subjected to standardized fusion, and the accuracy and the reliability of the system are improved. A hierarchical decision in a continuous risk interval is formed, an early warning result takes structural parameters, function changes and detection time as core elements, a closed loop of an early signal identification and risk calibration process is realized, and the early discovery efficiency of high-risk anomalies is improved.

The present application relates to the technical field of data processing, and discloses a method and system for judging power violation operation in advance through intention recognition, wherein a first interest point feature group in monitoring image data is detected according to a work task, the range of a carrier carrying at least one suspected target source is determined, the position of the suspected target source and a second interest point feature group are detected, the second interest point feature groups of several suspected target sources are compared, the human skeleton key node and the hand skeleton key node in the monitoring image data are detected, the position relationship data set of the hand skeleton key node and the suspected target source is obtained, the position relationship data set and the corresponding time dimension unit time are taken as input training parameters of a neural network model to be trained, so as to obtain a prediction model, and the current action is determined, and the next action is predicted and determined based on the prediction model. The present application can give a warning for several seconds in advance to ensure safety.

The invention relates to the technical field of equipment monitoring, and provides a hydroelectric generating set intelligent early warning method and system fusing working condition data, and the method comprises the steps: accessing multi-source heterogeneous operation data, and carrying out the format unification and working condition label preprocessing of the data; based on a parameter probability density analysis and Gaussian fitting method, establishing a mapping relation between real-time data and unit operation conditions; a multi-monitoring-point behavior relation model is constructed, dynamic threshold analysis, trend analysis and analogy analysis are combined, system abnormity is recognized, and graded early warning is generated; on the basis of a neural network evaluation model, unit health state grades including normal, attention, abnormity and severity levels are divided, and an equipment health evaluation result is output; a working condition-behavior model is constructed, a current operation working condition is identified, future behaviors of the unit are predicted, and early detection and early warning of an abnormal state are realized; and integrating the working condition-behavior model into a unit monitoring system to realize real-time working condition identification, behavior prediction and model self-learning feedback.