1173 results about "Early detection" patented technology

The invention discloses a chronic disease early detection method and system based on a multi-modal large model, and relates to the technical field of intelligent medical treatment and artificial intelligence, and the method comprises the steps: obtaining a multi-modal data stream of a target user in a target time window from a pathology database, and generating an original multi-modal data set; performing timestamp unification and numerical value standardization processing on the original multi-modal data set to obtain a time sequence feature sequence; inputting the time sequence feature sequence to the multi-modal large model to obtain an abnormal symptom feature; calculating the similarity between the abnormal symptom features and feature vectors of marked cases in a historical case library, and determining matched cases; a diagnosis result and a development process of the matched case are extracted, a disease risk level and a development trend corresponding to the original multi-modal data set are determined in combination with the medical knowledge graph, and a pathology assessment result is obtained; and generating an early warning signal containing the risk type and the intervention suggestion according to the pathological assessment result. By implementing the application, the accuracy of early detection of chronic diseases can be improved.

The invention pertains to an early detection method for network unreliable information using ensemble learning, within the field of early detection technology for unreliable network data. It involves the following steps: (1) converting input text sequences into word vector sequences; (2) inputting these word vectors into three base models—Transformer, Bi-SATT-CAPS, and BiTCN—for classifying unreliable information; (3) training and predicting with these models to generate new training and test data sets; (4) weighting and merging these new data sets to create a new training set for the meta-learner SVM; (5) training the new set with the meta-learner SVM to obtain the final classification result. This method retains the text's grammatical and structural features, using only blog posts and early comments to accurately detect unreliable information. By employing an improved weight fusion strategy, the method leverages the strengths of the three base models to enhance early detection effectiveness.

The invention relates to the technical field of production debugging control, and discloses a production debugging control method and system for a plastic container. The method comprises the following steps: arranging a plurality of different sensors in plastic container blow molding equipment, and simultaneously collecting blow molding process parameter data; performing wavelet threshold denoising and anomaly identification on the blow molding process parameter data to obtain a process parameter anomaly identification result; establishing a transfer function matrix between the process parameters and quality indexes according to the process parameter anomaly identification result, and obtaining a target process parameter set through multi-target optimization; and inputting the target process parameter set into a double-integral enhanced recurrent neural network for parameter regulation and control calculation to obtain a process parameter adjustment amount. According to the method, early detection and accurate positioning of process abnormity in the blow molding process are realized, the quality fluctuation risk and the defective product rate are greatly reduced, and the technical problem that different response characteristic parameters are difficult to coordinate is solved.

The invention discloses a multi-modal cognitive impairment evaluation system based on a multi-dimensional cognitive function hypergraph. The multi-modal cognitive impairment evaluation system comprises a multi-modal signal acquisition system, a cognitive function evaluation normal form design, a universal feature extraction framework of each modal and online hypergraph learning based on a multi-dimensional cognitive function. The system constructs a dimensionality evaluation normal form containing the cognitive function based on multi-modal physiological and behavior signals, so that comprehensive evaluation of the cognitive function of the elderly is realized. Firstly, physiological and behavior responses of a user under different tasks and situations are recorded and analyzed in real time by fusing multi-mode signals such as electroencephalogram, electrocardio, voice and facial expressions. And then, mining fusion representation among the multi-modal signals, generating unified feature representation, introducing an online hypergraph of clinical knowledge, capturing a complex multi-modal data relationship, and providing an analysis result containing cognitive function evaluation. Finally, in combination with the technical means, the system can realize early detection of cognitive impairment.

The invention discloses a GIS-based urban underground pipe network monitoring and early warning platform, and relates to the technical field of underground pipe network early warning. A data acquisition module is used for acquiring a pressure fluctuation signal in a pipeline, fluid flow data, a pipe wall vibration spectrum and surrounding soil moisture content change data in real time to form a multi-source time sequence data set; the feature extraction module is used for performing wavelet packet decomposition on the pressure signals, extracting high-frequency-band micro pressure pulsation features, processing vibration data by adopting empirical mode decomposition, and separating a normal operation mode and an abnormal disturbance component of a pipeline, so that the early detection capability of micro leakage is remarkably improved, the false alarm rate is reduced, and meanwhile, high-precision positioning is realized; reliable technical guarantee is provided for safe operation of the underground pipe network, and resource waste and safety accidents caused by tiny leakage are effectively avoided.

This document generally describes methods and systems for generating digital reconstructions of tissue from humans or other species. The method can, for example, include receiving, at a computing system, image data of a tissue sample, where one or more sections of the tissue sample are stained with hematoxylin and eosin (H&E), registering the image data to generate registered image data, identifying tissue subtypes based on application of a machine learning model to the registered image data, annotating the identified tissue subtypes to generate annotated image data, and determining a digital volume of the tissue sample in three dimensional (3D) space based on the annotated image data. The disclosed technology can provide for single-cell analysis and other analysis of tissue samples, such as early detection of cancer in human tissue samples.

Methods and systems for predicting the onset and progression of neurodegenerative diseases using blood test data and machine learning models are disclosed. The method involves receiving blood test values for a target subject over a previous time period, extracting features from the blood test data, and applying a trained predictive model to compute a risk score indicating the probability of developing a neurodegenerative disease. The system includes a processor that executes code to perform the method and can be implemented as a standalone application, cloud-based service, or integrated with healthcare systems. The predictive models are trained using supervised learning on labeled data and can include statistical models or machine learning algorithms. The systems and methods enable early detection of neurodegenerative diseases, personalized risk prediction, and integration with existing healthcare infrastructure. By leveraging blood test data and machine learning techniques, patient outcomes may be improved.

The subject matter described herein includes methods, systems, and computer readable media for early detection of a neurodevelopmental or psychiatric disorder using scalable computational behavioral phenotyping. According to one method for early detection of a neurodevelopmental or psychiatric disorder using scalable computational behavioral phenotyping includes obtaining user related information, wherein the user related information includes metrics derived from a user interacting with one or more applications executing on at least one user device; generating, using the user related information and a machine learning based model, a user assessment report including a prediction value indicating a likelihood that the user has a neurodevelopmental or psychiatric (neurodevelopmental / psychiatric) disorder and a prediction confidence value computed using relative contributions of the metrics to the prediction value generated using the machine learning based model; and providing the user assessment report to a display or a data store.

The invention discloses a PCB surface defect detection method and system, an electronic device and a medium, and relates to the technical field of defect detection, the connection condition caused by excessive tin soldering of a tin bridge or adjacent welding spots can be automatically identified by judging the connection state between the welding spots, an overall detection instruction is triggered, early detection is facilitated, connectivity defects are avoided, and the detection efficiency is improved. And the welding spot connection reliability is improved. The image is divided into a plurality of image blocks, the texture difference data of each image block is obtained, the texture abnormal state of the surface of the PCB can be finely analyzed by calculating the texture deviation coefficient, the subtle texture change in different areas can be identified by the block analysis method, and surface defects such as scratches and bubbles on the surface of the PCB can be accurately detected. And in combination with the texture deviation coefficient and the geometric features of the welding spots, unified normalization fitting is performed on the defect features to generate the defect recognition index, so that the defect features of different types can be captured, and the robustness and accuracy of the model can be improved.

The invention discloses an aircraft intelligent maintenance and diagnosis system, and relates to the technical field of aircraft maintenance and diagnosis, and the system comprises a data collection module which is used for collecting various data in the actual operation of an aircraft. According to the invention, a digital twin model construction module is utilized to accurately simulate the operation state and fault condition of the aircraft, early discovery and accurate diagnosis of potential faults are realized, the pertinence and effectiveness of maintenance are improved, immersive fault experience and maintenance guidance are provided for maintenance personnel through fusion of VR / AR technologies, and the maintenance efficiency is improved. The maintenance accuracy and efficiency are improved, pre-diagnosis and maintenance scheme verification are carried out on the digital twin model, unnecessary disassembly and detection work is avoided, the maintenance time and labor cost are reduced, potential safety hazards can be eliminated in time through accurate fault diagnosis and an effective maintenance scheme, the flight safety of an aircraft is improved, and the safety of the aircraft is improved. And the maintenance scheme is comprehensively evaluated and optimized, so that the performance and the structural integrity of the maintained aircraft are ensured.

The present invention relates to the fields of child health monitoring and artificial intelligence technology, and provides a child scoliosis monitoring and early warning system. Existing methods rely on static inspections, and it is difficult to capture abnormal dynamic changes of the spine in real time. The present invention acquires multimodal data through posture sensors, acceleration sensors, and image acquisition devices, combines convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to perform deep feature extraction, and uses hidden Markov models (HMMs) to identify children's behavioral contexts and dynamically adjust the early warning threshold. The system uses a Q-learning algorithm to generate personalized health intervention strategies, and uses differential privacy and homomorphic encryption technologies to ensure data security. Finally, the system generates a three-level health warning signal to immediately remind children to adjust their postures and notify guardians. The present invention realizes real-time monitoring and personalized intervention of spinal health, provides an efficient and safe health management solution, and promotes the early detection and correction of scoliosis.

The invention relates to the technical field of wind power generation, in particular to a blade icing diagnosis method and device, an intelligent deicing system and a storage medium. According to the invention, blade icing diagnosis is carried out by adopting multi-modal data, the early detection accuracy of blade icing is improved, the false alarm rate of single sensor detection is reduced, and accurate diagnosis of blade icing is realized. When the multi-modal data is processed, a dynamic alignment algorithm is adopted, the limitation that traditional data alignment only depends on timestamps is broken through, meteorological data is introduced to serve as an alignment constraint condition, and the problem that physical relevance of the multi-modal data is missing is solved. And meanwhile, a hybrid expert architecture is adopted to process the multi-modal data, so that effective fusion and high-robustness diagnosis of the multi-modal data are realized.

The present disclosure relates generally to the field of wireless communications, and particularly to a network-based mechanism for early detection of failures during fast Master Cell Group (MCG) recovery. For this purpose, a Secondary Node (SN) may determine that a User Equipment (UE) experiencing an MCG failure is unable to receive a command for handover (HO) from a source MCG to a target MCG. This determination may be based on the occurrence of at least one of the following events: (i) a recovery timer (e.g., T316 timer) has expired at the UE, and (ii) a number of retransmissions performed by the SN to deliver the HO command to the UE reaches a threshold before the recovery timer expires. The SN may then report the fast MCG recovery failure to a Master Node (MN), with the indication that event (i) and / or event (ii) has occurred. This may allow the MN to take appropriate actions (e.g., properly correct the recovery timer), which may in turn decrease the interruption time caused by similar MCG failures and save network resources in the future.

An exemplary system and method are disclosed that is configured to detect cognitive impairment (e.g., early cognitive impairment) or assess cognitive function by analyzing, via machine learning and artificial intelligence analysis, behavioral metadata collected from a smart app during the course when a subject is using a cognitive test instrument for cognitive tests that incorporate motor activity (e.g., drawing or writing). The machine learning and artificial intelligence analysis can execute features associated with the test taker's metadata (e.g., time spent on task or questions, changing answers, referring back to the previous question), drawing qualities (e.g., line straightness, completeness), among others.

The invention discloses a three-dimensional positron emission tomography data image classification method based on multi-stage progressive feature extraction, and is applied to the technical field of Alzheimer's disease auxiliary diagnosis. The auxiliary diagnosis method comprises the following steps: acquiring and preprocessing PET image data of an Alzheimer's disease patient; improving the reliability of the data set by using data enhancement; performing long-range dynamic modeling on the three-dimensional voxel sequence through a stacked Lmamba block; global context semantic adaptive fusion is realized through a layer-by-layer cross-scale channel attention fusion module (CSACF), and a channel spatial perception module (CSPM) is constructed to optimize spatial feature fusion; an inverted bottleneck module is mixed with long-distance space and position information to enhance the capturing capability of the model on detail features; and finally, predicting the disease category probability through global average pooling, full connection and softmax functions. According to the method, the precision of AD early diagnosis and MCI conversion risk prediction can be greatly improved, the defects of a medical image diagnosis method of a convolutional neural network (CNN) and Transform in long-range dependence on modeling and calculation complexity are overcome, and the method has good application prospects and is suitable for AD early detection and MCI conversion risk assessment.

The invention discloses an insulin resistance early detection method, system, equipment and medium, and the method comprises the following steps: screening an obtained insulin resistance potential factor and an insulin resistance result corresponding to a first to-be-detected sample individual to obtain a key detection factor; different to-be-screened neural network models are evaluated to determine a to-be-optimized detection model, and the evaluation process is designed to perform screening according to feedback of the different to-be-screened neural network models in clinical prediction. And based on the key detection factor and the insulin resistance result, carrying out interpretability analysis on the to-be-optimized detection model to obtain a target insulin resistance detection model, and applying the target insulin resistance detection model to actual insulin resistance early detection. The method provided by the invention is low in detection cost, simple in operation steps and high in detection accuracy, and the insulin resistance risk is identified in an early stage by analyzing conventional physical examination data.

The invention discloses a heart disease prediction system based on an electrocardiosignal and a large model, and relates to the field of electrocardiosignal processing, and the system is characterized in that the electrocardiosignal of a user is collected by using a heart coat, and baseline offset suppression and noise processing operation are carried out on the electrocardiosignal by using a data processing center; a variability sequence is extracted from the processed electrocardiosignal, an electrocardiograph scatter diagram is drawn according to variability, and scatter points in the scatter diagram are numbered and coded in a partitioned mode to obtain the sequence information entropy of the heart rate variability sequence; segmenting the electrocardiosignal based on the sequence information entropy and the wave crest, obtaining a waveform segment of a single cardiac pulse according to a segmentation result, inputting the waveform segment into the large medical prediction model, and outputting the characteristic change of the electrocardiosignal; and comparing the feature change with a pre-stored disease feature standard and outputting a disease prediction result. According to the method, the complex characteristics of the electrocardiosignals and the prediction capacity of the large model are combined, and the diagnosis precision and the early-stage discovery rate of heart diseases can be remarkably improved.

Provided are a system and a method for detecting fire event in underground utility tunnels based on acoustics. The system is for early detection of fire situations in an underground facility based on sound. The system includes a sound acquisition device that is installed in the underground facility and collects acoustic signals in real time, and a fire situation early detection server that predicts occurrence of electric sparks and infers a fire risk based on the acoustic signal collected by the sound acquisition device.

Various techniques for providing a DNS Early Threat Executive Response System (DETERS) are disclosed. In some embodiments, DETERS is a comprehensive DNS threat detection, response, and reporting system with a modular analytics architecture that allows for early detection of suspicious activity in near real-time. DETERS can identify threats before they are able to spread or compromise systems. DETERS uses a combination of streaming and batch processing, as well as historical DNS information. The DNS-centric design allows a DNS resolver to quickly mitigate threats and for the reporting system to alert users allowing them to take further actions that are reflected in the DNS resolver response policy.

Provided in the embodiments of the present disclosure are a wearing deviation measurement method and apparatus, and an electronic device and a storage medium. The method comprises: collecting an eye image; obtaining at least two eye feature points according to the eye image, wherein the eye feature points are distributed on the contour of an eye area in the eye image; and obtaining deviation information according to location features of the at least two eye feature points, wherein the deviation information represents a deviation type and / or a deviation amount of the current wearing pose of a terminal device relative to a standard wearing pose. An eye image is collected, and a deviation type and / or a deviation amount of the current wearing pose of a terminal device relative to a standard wearing pose are / is obtained on the basis of eye feature points on the contour of an eye area in the eye image, so as to realize early detection and prompt when a wearing pose is abnormal, thereby avoiding the problem of the accuracy of gaze point detection being reduced due to the fact that the wearing pose of the terminal device is abnormal, and improving the image display effect and the human-computer interaction efficiency.

The invention relates to a system for early detection and risk prediction of schizophrenia. The system comprises a feature extraction module which performs parallel processing on a multi-mode original signal; the fusion prediction module fuses the voice feature signal, the behavior feature signal and the clinical feature signal through a cross-modal attention weighting mechanism to generate a fusion feature vector, and outputs an initial risk prediction signal through a machine learning classifier based on the vector; the dynamic updating module receives an incremental characteristic signal triggered by newly added collected data of a user, generates an updating risk prediction signal through incremental learning, and feeds back the updating risk prediction signal to the fusion prediction module to optimize the weight of a classifier; and the visual output module converts the updated risk prediction signal into a dynamic risk trajectory diagram and an interpretable feature contribution thermodynamic diagram. The system for early detection and risk prediction of schizophrenia can solve the problems that early screening of schizophrenia lags behind and dynamic risk assessment is missing.

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 an older child infantile autism evaluation system based on multi-modal data analysis. The older child infantile autism evaluation system comprises a multi-modal signal acquisition module and a multi-modal feature extraction and fusion module. The multi-modal signal acquisition module is used for acquiring physiological signals, behavior signals and scale data; and the multi-modal feature extraction and fusion module is used for extracting and fusing the features of the modal signals, generating unified feature representation, and providing an analysis result including autism evaluation in combination with scale data so as to realize the autism evaluation of the older children. According to the invention, early detection of older children can be realized.

The invention belongs to the technical field of magnetic nanoparticle imaging, particularly relates to an MPI magnetic particle imaging method and system for early detection of an AD patient, and aims to complete MPI time sequence imaging by using a diffusion model and solve the problem that the feature dimension of an input signal in the diffusion model is not matched with the resolution of an expected generated image. The method comprises the following steps: constructing a generative network model, and performing staged training; mPI time sequence one-dimensional signals are collected, and image reconstruction is carried out based on the generative network model to obtain MPI time sequence images; carrying out registration and dividing a brain region; and constructing a functional connection diagram and a functional connection matrix by using the RV coefficient, and drawing the brain network. According to the method, by constructing the Seq-VAE / Regression / Sig-LDM step-by-step network and performing staged training, free input of a signal sequence of a fixed feature dimension is realized, the image sequence reconstruction effect is improved, a default mode network is drawn based on the reconstructed image sequence, and accurate data is provided for subsequent application.

The invention relates to the technical field of molecular biology, in particular to application of a gene marker in early screening of multiple alimentary canal cancer species, an early screening model construction method and a detection device.WGS low-depth sequencing is conducted on plasma sample cfDNA, and three difference characteristics including copy number change, copy number change, cfDNA fragment change, cfDNA fragment change, cfDNA fragment change, cfDNA fragment change, cfDNA fragment change, cfDNA fragment change, cfDNA fragment change, cfDNA fragment change, cfDNA fragment change, cfDNA fragment change and cfDNA fragment change are analyzed according to a high-throughput sequencing result. The method comprises the following steps of: performing training modeling by using a convolutional neural network according to a DNA fragment size ratio and a nucleosome covering mode, realizing high-specificity and high-sensitivity accurate early detection on a plurality of cancers under low sequencing depth, and performing training modeling by using five algorithms, namely a generalized linear model, gradient boosting, a random forest, deep learning and extreme gradient boosting respectively, so as to realize accurate early detection on the plurality of cancers under low sequencing depth. The generalized linear model is subjected to secondary set training to construct multi-feature algorithm integration, and the purpose of tissue traceability detection of multiple cancer species is achieved.

A cardiovascular disease early warning and intervention system based on a multi-modal large model comprises a user interface layer, a multi-modal information module, a natural language processing module, a medical knowledge base, a data management module, an intelligent recommendation module, a doctor assistance module, a data analysis module and a safety and privacy protection module. Through fusion of multi-modal data and application of a deep learning algorithm, the system can provide personalized medical suggestions and health management schemes, significantly improve the quality and efficiency of medical services, help users to better manage their health, help doctors to diagnose and treat, and improve the working efficiency and diagnosis accuracy of the doctors; in a word, the cardiovascular disease early warning and intervention system based on the multi-modal large model realizes remarkable improvement in the aspects of understanding patient demands, personalized suggestions, data analysis, knowledge base integration and the like, can effectively improve the efficiency and quality of medical follow-up visit, and provides powerful support for early discovery and intervention of cardiovascular diseases.

The invention discloses a preparation method of Au (at) Au (at) Ag / Pt / M NPs and application of the Au (at) Au (at) Ag / Pt / M NPs in klebsiella pneumoniae detection, and belongs to the technical field of analysis and detection. The invention particularly relates to development of a signal probe and combination and assembly of the signal probe and lateral flow immunochromatography (LFIA) for klebsiella pneumoniae detection. Au (at) Au (at) Ag / Pt / MNPs is a multi-element noble metal nanoparticle with plasma resonance property, peroxidase-like activity and light stability, shows strong detection potential in three modes of colorimetric (CM), catalytic colorimetric (CL) and surface enhanced Raman scattering (SERS), and is loaded into LFIA to construct a multi-mode LFIA (CM / CL / SERS-LFIA). The kit is expected to be used for direct multimode detection of klebsiella pneumoniae in clinical sputum samples, and a new early detection strategy is provided for respiratory tract infection diseases such as klebsiella pneumoniae.

The invention relates to the technical field of medical clinical auxiliary diagnosis, in particular to an orthopedic postoperative complication early detection and early warning system based on data analysis, which comprises a multi-modal sign synchronous acquisition module, a sign dynamic difference quantification module, a complication feature association module, a grading early warning decision module and a dissection calibration visualization module. Wherein the multi-modal sign synchronous acquisition module is used for acquiring postoperative data of a patient; calculating a difference quantization parameter; the complication characteristic association module is used for identifying associated risk characteristics of the osteofascia ventricular syndrome, the deep venous thrombosis and the fat embolism; and the grading early warning decision module is used for generating a three-level early warning instruction based on the associated risk characteristics. According to the system and the method, through a multi-modal sign synchronous acquisition, difference quantification and grading early warning linkage mechanism, accurate recognition, risk grade division and visual intervention guidance of postoperative complications are realized, and the continuity of postoperative monitoring and the timeliness of early warning are improved.

The invention discloses a basin-type insulator internal defect detection system and method based on pulse infrared thermal waves, and relates to the technical field of nondestructive testing and computer vision, and the basin-type insulator internal defect detection system comprises a pulse infrared thermal wave excitation module, an infrared thermal imaging module and a data processing module; the pulse infrared thermal wave excitation module is used for enabling the surface of the insulator to generate thermal response in a pulse infrared thermal wave excitation mode; the infrared thermal imaging module captures the temperature change of the surface of the insulator in real time and performs early detection; and the data processing module is used for data preprocessing, feature extraction and defect detection. According to the method for detecting the internal defect of the basin-type insulator based on the pulse infrared thermal wave, the pulse infrared thermal wave excitation and the high-precision infrared thermal imaging technology are combined, so that the bubble defect in the insulator is efficiently detected. The pulse infrared thermal wave technology generates instantaneous temperature change on the surface of the insulator, so that the bubble defect is more obvious, and the detection sensitivity and speed are improved.

The invention provides a scoliosis screening method and system, and relates to the technical field of medical artificial intelligence, human body image data on a low-light detection garment worn by a patient is collected, the human body image data is preprocessed and sent to an SSD module, the SSD module is used for predicting the position of a low-light light source, and a scoliosis detection result is obtained. The key point detection module is used for detecting human body scoliosis, high-precision detection frame information in the detection result is used as a basis, a human body posture result predicted by the key point detection module is restrained and corrected, then human body scoliosis grading is performed, a human body scoliosis detection result is displayed and fed back to a patient, high-precision detection of scoliosis is achieved, and the system has the advantages of high efficiency, non-invasiveness and light weight; the scoliosis early detection rate and grading intervention precision can be remarkably improved, and the problems that traditional medical diagnosis is low in efficiency, large in radiation risk, insufficient in precision and difficult to operate on embedded equipment with limited resources are solved.