449results about How to "Easy diagnosis" patented technology

The invention relates to the technical field of unit fault diagnosis, in particular to a hydroelectric generating set fault diagnosis method and system based on digital twinning, and the method comprises the steps: dividing starting stages of a target hydroelectric generating set, analyzing starting transfer-in interference terms, building a filtering channel, evaluating the interference degree, combining real-time data anomaly analysis, and building an early warning model through a sensor algorithm; a digital twinborn model is established based on equipment topology, and a potential fault is predicted in combination with an early warning model, so that the technical problems that temporary normal fluctuation and a hydroelectric generating set fault are difficult to distinguish through an empirical threshold in a hydroelectric generating set starting process and the fault diagnosis analysis is limited are solved; effective separation and quantitative evaluation of various interference factors under different working conditions in the starting stage are realized, and the abnormality in the starting process is accurately captured by constructing a real-time data abnormality analysis module, so that the sensitivity of fault symptom detection is improved, a starting clamping stagnation early warning model is established, and the early warning efficiency is improved. And the fault diagnosis precision of the hydroelectric generating set is comprehensively improved.

The invention provides a live pig health monitoring method based on multi-source information fusion, and the method comprises the steps: collecting the sound data of a live pig in a natural environment, carrying out the classification and preprocessing of the sound data, forming a sound sample data set, inputting an LSTM neural network model, and carrying out the training, and obtaining a sound detection model; acquiring a live pig attitude image to obtain an attitude sample data set, and inputting the live pig attitude sample data set into the YOLOv5s model for training to obtain an attitude detection model; inputting live pig sound data collected in real time, preprocessing the data, and inputting the data into the sound detection model to obtain sound features; inputting live pig attitude images collected in real time into the attitude detection model to obtain attitude features; measuring the body temperature of the live pig by using an infrared temperature sensor, measuring the weight of the live pig by using a weight sensor, and fusing the sound feature, the posture feature, the body temperature and the weight to obtain a fusion vector; and inputting the fusion vector into a random forest classifier for decision making, and determining the health state of the live pig. According to the method, multi-modal data are fused, comprehensive evaluation and diagnosis of the health condition of the live pigs are facilitated, and the accuracy and robustness of the whole monitoring system are further improved.

The invention relates to the technical field of computer deep learning, and relates to an equipment fault diagnosis network model training method based on multi-source domain knowledge joint migration, and the method comprises the steps: constructing a training sample set, a verification set and a test set, and training a pre-constructed equipment fault diagnosis network model through the training sample set, optimizing parameters of the equipment fault diagnosis network model to obtain a trained equipment fault diagnosis network model; verifying the trained equipment fault diagnosis network model by adopting a verification set, evaluating the diagnosis performance of the trained equipment fault diagnosis network model, and selecting an optimal equipment fault diagnosis network model; and testing by adopting the test set optimal equipment fault diagnosis network model, and evaluating the performance of the optimal equipment fault diagnosis network model. According to the method, multi-source-domain cooperative training is carried out on the model by utilizing a collected multi-source-domain data set which is composed of a plurality of labeled source-domain data sets and unlabeled source-domain data sets and contains a small number of labels, domain invariant features in fault samples are learned, and shared diagnosis knowledge is migrated to a target domain from labeled data and unlabeled data together; therefore, the trained equipment fault diagnosis network model can complete a cross-domain fault diagnosis task without obtaining a target sample in advance.

The invention belongs to the field of fault diagnosis, and particularly relates to a main shaft bending common characteristic diagnosis system based on a stacking integration method, and the system comprises the following modules: a sensor module which is used for collecting a vibration signal of a main shaft in real time; the data acquisition and processing module is used for converting an analog signal into a digital signal and preprocessing the signal acquired by the sensor; the feature extraction module is used for extracting features from the preprocessed vibration signals; the machine learning diagnosis module analyzes and classifies the extracted features by using stack integration based on a plurality of trained machine learning models, and judges the health state of the main shaft; and the output module is used for outputting the diagnosis result and sending out an alarm signal when abnormality is detected. Real-time monitoring and automatic diagnosis of the main shaft bending fault are achieved, compared with a traditional method depending on expert experience and manual detection, the fault detection efficiency and accuracy can be greatly improved, and misjudgment and missed judgment risks caused by human factors are reduced.

The invention relates to the technical field of nursing scheme intelligent recommendation systems, in particular to a cardiology patient personalized nursing scheme intelligent recommendation system optimization method, which comprises a dynamic rare case recognition engine for accurately capturing and recognizing cardiology rare cases by using medical data and text data, and comprises a data preparation module, a data retrieval module, a data processing module and a data processing module, the candidate sample classification and recognition module is used for recognizing whether rare cases exist or not, and the problems existing in the prior art are effectively solved by introducing innovation modules such as a dynamic rare case recognition engine, a knowledge graph builder and a rare case simulation generator. According to the invention, continuous learning and updating can be realized, rare cases can be accurately identified, and customized nursing suggestions can be provided based on individual features of patients. Meanwhile, the decision-making process of the system has good interpretability, decision-making basis can be provided for doctors, and usability and credibility of the system in clinical practice are greatly improved.

The invention discloses a mode switching optimization method, system and device for an audio input player and a storage medium. A persistent audio capture session is created, and a background service process is called to maintain the persistent audio capture session; audio data are continuously captured and stored in a preset buffer area, the preset buffer area is provided with a plurality of variable circular buffer areas with controlled sizes, and each variable circular buffer area is used for monitoring and caching the audio data of one corresponding audio input interface; adjusting processing pipeline configuration based on the corresponding audio data in the preset buffer area under the control of a preset switching instruction; and the playing module is switched to a target mode of the switching instruction based on a preset smooth switching module to perform audio playing. By maintaining a persistent audio capture session and dynamically processing an audio processing pipeline, the problem of reopening capture is solved, and seamless mode switching is realized.

The invention discloses an electric power fault diagnosis and prediction system based on big data, particularly relates to the field of intelligent diagnosis of an electric power system, and aims to solve the problems of fault response delay and insufficient diagnosis precision. Localized intelligent diagnosis of power faults is realized, and stable operation and rapid recovery of a power system are guaranteed; a priority classification and sorting uploading mechanism is combined to ensure that key data is transmitted preferentially, diagnosis errors caused by delay are reduced, and the analysis stability of a central dispatching center is enhanced; through comprehensive judgment of a global deviation aggregation index and a trust stability harmonic measurement index, accurate classification of local node diagnosis controllability is realized, more accurate key data is guaranteed to be obtained, finally, the fault response capability of a system and the safety of a power system are improved, and powerful technical support is provided for intelligent diagnosis and maintenance.

The invention discloses a hydroelectric generating set fault diagnosis method based on a chaos Kepler optimization algorithm, and belongs to the technical field of hydroelectric generating set fault diagnosis. A Tent chaos function is introduced to replace an initial population of a Kepler optimization algorithm (KOA), and by improving the uniformity and uncertainty of the population, the convergence speed of the KOA algorithm is increased, and the global search capability is improved. An improved Kepler optimization algorithm is adopted to optimize hyper-parameters of the BiLSTM, a BiLSTM prediction model CKOA-BiLSTM of the hydroelectric generating set is established, the robustness and generalization ability of an existing model are improved, and the model is made to be suitable for processing complex nonlinear fault signals of the hydroelectric generating set. The model shows the optimal diagnosis effect on recognition of complex fault types, and the research can be used as a beneficial supplement of an existing hydroelectric generating set state monitoring and fault diagnosis system.

The invention discloses a power distribution area energy optimization system, which comprises an energy optimization controller, a multifunctional protocol converter, a new energy device and a monitoring master station, and is characterized in that the monitoring master station is connected with the energy optimization controller, and the energy optimization controller is connected with the multifunctional protocol converter; the energy optimization system comprises a unified power flow calculation method and an energy optimization method, and the energy optimization system of the power distribution area realizes efficient management and optimization of energy through combination of various technical means, and provides powerful support for development of an intelligent power grid and a distributed energy system.

The invention discloses a bearing fault diagnosis method and device, and relates to the technical field of bearing fault diagnosis, and the method comprises a model training step, a model integration step and a fault diagnosis step, and the model training step provides a trained diagnosis model for the model integration step. The anomaly detection model output in the model integration step is used as the model algorithm input in the fault diagnosis step, and the model integration step is used for integrating a plurality of models to improve the diagnosis accuracy and the system robustness; the model integration step comprises the steps of multi-model integration, dynamic model selection and anomaly detection. According to the bearing fault diagnosis method, the model integration step is designed, the advantage integration function of multiple models is achieved, the limitation problem when a single model processes complex and changeable fault modes is solved, fault diagnosis is difficult to comprehensively and accurately conduct, the accuracy and robustness of a bearing fault diagnosis system are improved, and the fault diagnosis efficiency is improved. Fault detection under complex working conditions can be better handled, and the probability of false alarm and missing alarm is reduced.

The invention discloses a method and system for assisting bronchus endoscope microscopic examination, electronic equipment and a storage medium. The method comprises the following steps: acquiring a bronchus endoscope examination image in real time; operating a first pre-trained classifier to classify the bronchus endoscopic examination image, wherein the bronchus endoscopic examination image comprises mark identification information used for identifying at least two inner cavities; in response to the classified bronchus endoscope examination image, obtaining an identification result of the bronchus endoscope examination image, and calling a state request for obtaining the microscopic examination condition of the bronchus endoscope; and feeding back the real-time microscopic examination condition of the bronchus endoscope according to the state request. According to the scheme, the current position of the bronchoscope can be intelligently identified by utilizing the bronchus tree model, so that a doctor is assisted in judging and updating the position of the bronchoscope, missing detection of a key position or wrong detection of other positions can be prevented, meanwhile, the microscopic examination operation level, the examination image quality and the like are evaluated in real time, and the operation efficiency is improved. Therefore, the reliability and the accuracy of bronchoscopy results are improved.

The present invention relates to a molecular imaging probe for tumor diagnosis, and provides a sugar nucleic acid molecular imaging probe, the probe comprises a DNA aptamer sequence and more than one glycosyl nucleoside module, and is labeled by radionuclide, the DNA aptamer sequence can specifically target tumor cells after functional folding; the glycosyl nucleoside module is a glycosyl modified nucleoside derivative, is accurately modified at the 5'end or / and the 3 'end of the aptamer through DNA solid-phase synthesis, has the imaging characteristics of biological safety, high sensitivity and high resolution, and has an excellent diagnosis effect on part of cancers which are difficult to diagnose by a traditional method. The invention also provides a preparation method and a use method of the sugar nucleic acid molecular imaging probe, so that sugar modification of the aptamer is convenient, rapid, accurate, controllable and modularized, and the sugar nucleic acid molecular imaging probe has huge clinical application potential and transformation value in cancer diagnosis.

The invention discloses an equipment part fault diagnosis method, device and equipment based on sensing data mixed noise suppression, and relates to the field of fault diagnosis. The diagnosis method comprises the following steps: acquiring vibration signals of an equipment part under different fault types; performing data preprocessing on the vibration signal to obtain a sample, taking a fault type corresponding to the vibration signal as a real label, and constructing a training set and a verification set; using the training set and the verification set, and using a migratable Adaboost algorithm and an adaptive cost sensitive learning method to train a plurality of deep learning models to obtain a plurality of trained deep learning models; and performing fault diagnosis on the to-be-tested part of the equipment by using the plurality of trained deep learning models. According to the invention, the accuracy of equipment part fault diagnosis in the mixed noise environment is improved.

The invention relates to the technical field of tumor image analysis systems, in particular to a multi-modal tumor image feature extraction and fusion analysis system and a method thereof.The multi-modal tumor image feature extraction and fusion analysis system comprises a data acquisition module, a multi-modal feature extraction and fusion module, a tumor prediction module and an optimization module; according to the method, the quality and robustness of feature extraction are effectively improved, and the problems of noise and artifacts in medical images can be better solved. In the feature fusion stage, an innovative weighted fusion strategy is adopted, complementary information between different modals is fully utilized, the problem of information redundancy is effectively avoided, the convolutional neural network and the bidirectional LSTM network are combined, spatial features can be captured, time sequence information can be effectively utilized, and the method has the advantages of being high in robustness and high in robustness. Therefore, the prediction accuracy of the tumor development trend is improved, the genetic algorithm is introduced to optimize network hyper-parameters, and the innovative optimization method enables the model to better adapt to specific data sets and tasks.

The invention provides an OPM-MEG and MRI registration method and system based on a binocular camera, and the method comprises the steps: firstly carrying out the calibration of the binocular camera, and obtaining a calibration parameter through a checkerboard calibration method; then, binocular images of the testee wearing the brain magnetic measurement device are collected; performing three-dimensional reconstruction, obtaining an initial disparity map through epipolar correction and an SGBM binocular stereo matching algorithm, optimizing the disparity map to generate a three-dimensional point cloud, and performing noise reduction and smoothing processing by using MLS and Poisson surface reconstruction technologies; in the face point cloud registration stage, point cloud is cut by taking a nose tip point as a sphere center, ICP registration is carried out, and a transformation matrix from a camera coordinate system to an MRI coordinate system is obtained; and finally, in a helmet positioning stage, detecting a three-dimensional coordinate of a ball target on the helmet, calculating a transformation matrix from a helmet coordinate system to a camera coordinate system, and unifying the helmet coordinate system to an MRI coordinate system. According to the invention, a one-button registration process can be realized, the registration time is shortened, the registration precision is improved, and a basis is provided for improving the source positioning precision.

The invention discloses a characteristic value calculation method based on electrocardio data, belongs to the field of medical data analysis, and provides an electrocardio data analysis and processing method. Comprising the following steps: decomposing different frequency components of an electrocardiosignal by using wavelet transform, and analyzing on different time scales. Wavelet transformation is relatively suitable for detecting QRS wave groups, and due to the fact that the wavelet transformation can highlight transient characteristics of signals on different frequency bands, complex electrocardiosignal processing is more efficient. In addition, wavelet transform is not only suitable for wave crest detection, but also can help to remove signal noise and improve signal definition. The deep learning technology is used for automatically extracting the features from the electrocardiosignals, a feature extractor does not need to be manually designed, related medical professional knowledge and the artificial intelligence technology are combined, wave crests in the electrocardiosignals can be more accurately detected, and meanwhile anomaly detection can be carried out, such as early signs of heart diseases such as sinus arrhythmia and the like.

The invention discloses a feature extraction and recognition method for skin lesion hyperspectral image classification, and belongs to the technical field of image processing, and the method comprises the following steps: S1, carrying out the preprocessing of a hyperspectral image; s2, extracting diversity features of the preprocessed hyperspectral image; s3, the extracted features enter a feature fusion module, and feature fusion is realized through a three-dimensional full convolutional neural network of multi-granularity feature fusion; s4, after feature fusion, lesion region segmentation is carried out in a segmentation module; s5, classifying the segmented features in a classification module; s6, enabling the classified lesion areas to enter a detection module, and improving the detection precision and real-time performance of lesion cells in the microscopic hyperspectral image; and S7, verifying a detection result of the detection module by a clinician according to a pathological result of a pathological expert. The feature extraction and recognition method for skin lesion hyperspectral image classification has higher diagnosis and classification capabilities, and is suitable for various medical image analysis tasks.

The invention belongs to the technical field of unit fault diagnosis, and discloses a fault diagnosis method and system for a fault large-scale unit based on sharing and specific representation learning. The method not only can be applied to multi-measuring-point fault diagnosis of a large-scale unit, but also has wide applicability. In other multi-sensor systems or complex equipment, the fault detection effect can also be improved through multi-view representation learning. The invention provides a weighted sharing-specific multi-view depth representation learning fault diagnosis method for fault diagnosis of multi-measuring-point data of a large-scale unit. The method consists of two parts, namely sharing and specifically representing a learning network and weighted multiple similarity loss and orthogonal loss. A multi-measuring-point data fault diagnosis problem of a large-scale unit is solved by sharing a specific representation learning network, and weighted multiple similarity loss and orthogonal loss are used to further improve the feature extraction capability of the network and reduce redundancy.

The invention provides an image optical anti-shake method for an endoscope camera system. The method comprises the following steps: acquiring motion data of an endoscope camera in real time; based on the collected motion data, using an image processing algorithm to analyze and identify the mode and degree of image shake, and calculating the compensation amount required by optical and electronic anti-shake; a high-precision optical anti-shake assembly is integrated, and according to the calculated compensation amount, an optical path or the position of a lens is rapidly adjusted through the optical anti-shake assembly so as to compensate for image offset caused by movement of the camera; an electronic anti-shake assembly is integrated, reading data of an image sensor or parameters of an image processing chip are adjusted through an electronic control system, and residual shake is further eliminated; sensor data and image quality are monitored in real time, optical anti-shake parameters are dynamically adjusted, and continuous image stability and clearness are ensured. Real-time, stable and clear output of images is achieved through the optical technology, and a better auxiliary diagnosis means can be provided for doctors.

The invention provides a permanent magnet synchronous motor electrical fault diagnosis method, and relates to the technical field of motor fault diagnosis. Comprising the following steps: S1, extracting historical electrical fault key characteristic quantity sets of a plurality of motors; s2, performing corresponding classification division on the plurality of historical electrical fault key characteristic quantity sets and different motor operation state categories to form a plurality of historical data sample sets; s3, calculating the prior probability of each motor operation state category; s4, extracting a newly collected electrical fault key characteristic quantity set to obtain a posterior probability of each motor operation state category; s5, supplementing and updating the characteristic quantity data to obtain the posterior probability of each motor operation state category; and S6, labeling the result, and storing the labeled information into a sample information base to update the historical data sample set. According to the method, the problem that most of current fault diagnosis methods cannot well meet the fault diagnosis requirement under the background of big data is solved.

The invention discloses application of LECT2 as a diagnosis and treatment target of hepatic ischemia reperfusion injury. According to the present invention, the Print method operation patient sample is detected to discover that the expression of the LECT2 in the hepatic ischemia reperfusion injury is up-regulated; on the basis, a liver ischemia reperfusion injury model is constructed by knocking out a mouse through the LECT2 gene, and a result shows that the liver ischemia reperfusion injury of the mouse is obviously reduced by knocking out the LECT2 gene and transferring the LECT2-shRNA into the LECT2 gene, and the liver ischemia reperfusion injury of the mouse is aggravated by LECT2 recombinant protein; it is shown that inhibition of expression of LECT2 is beneficial to improvement or treatment of hepatic ischemia injury; furthermore, the invention further finds that liver injury can be relieved by inhibiting the combination of LECT2 and Tie1; the LECT2 / Tie1 signal channel can also be used as a target spot for treating the liver ischemia reperfusion injury.

The invention discloses a rheumatoid arthritis diagnosis product and system and application thereof, and relates to the technical field of rheumatoid arthritis. The diagnostic marker comprises calcium, a ratio of arginine to ornithine, a ratio of phenylalanine to tyrosine, an anti-cyclic citrullinated peptide antibody and lysine. The invention provides a biomarker capable of accurately diagnosing rheumatoid arthritis RA, which comprises five indexes, namely calcium, the proportion of arginine to ornithine, the proportion of phenylalanine to tyrosine, an anti-cyclic citrullinated peptide antibody (anti-CCP) and lysine. The five indexes have the technical advantages of few indexes and excellent diagnosis performance. A diagnosis model constructed based on the five indexes has good sensitivity, specificity and accuracy. The kit can be used for diagnosis of rheumatoid arthritis.

The invention provides a vehicle diagnosis method and device, a chip, a vehicle, a computer readable storage medium and a computer program product. The method comprises the following steps: receiving a first hypertext transfer protocol HTTP request message sent by diagnosis equipment; wherein the first HTTP request message is used for requesting a target resource in the target node; the communication protocol used by the target node is a unified diagnostic service UDS communication protocol; the first HTTP request message is converted into a UDS request message, and the UDS request message is sent to a target node; receiving a UDS response message sent by the target node; wherein the UDS response message comprises resource information of a target resource in the target node; and converting the UDS response message into a first HTTP response message, and sending the first HTTP response message to the diagnostic device. According to the invention, the method can be better suitable for the whole vehicle topology which is high in HPC node occupation ratio and is added with Ethernet connection; and meanwhile, the node using the UDS communication protocol in the vehicle can be diagnosed.

The invention relates to the technical field of diagnosis markers and diagnosis methods for mental diseases of children and adolescents, in particular to a plasma metabolism diagnosis marker and diagnosis system for depression of children and adolescents. The plasma metabolism diagnosis marker comprises at least one of cyclic adenosine monophosphate, methylpyridine, unsaturated undecylenyl carnitine, histidine, prolyl hydroxyproline, tryptophan and glucuronic acid. According to the scheme, non-target metabonomics data is analyzed, and seven candidate diagnostic markers for depression of children and adolescents are screened and determined from common differential metabolites. Then, through targeted metabonomics research, diagnostic markers and combinations thereof with ideal diagnostic effects are determined, and then a diagnostic model is constructed. And a sex factor is also incorporated into the diagnosis model, so that the diagnosis effect is further improved. According to the technical scheme, the technical problem that in the prior art, reliability and popularization potential of depression plasma metabolism biomarkers of children and adolescents are not ideal can be solved, and the depression plasma metabolism biomarkers have ideal popularization and application prospects.

The invention relates to the technical field of molecular biological detection, and particularly discloses a quadruple fluorescent PCR primer probe combination, a reaction system and a detection method for identifying a mycoplasma gallisepticum wild strain and a vaccine strain. The quadruple fluorescent PCR primer probe combination for identifying the mycoplasma gallisepticum wild strain and the vaccine strain comprises a universal primer probe group for detecting mycoplasma gallisepticum and a specific primer probe group specially aiming at three vaccine strains F-36, TS-11 and 6 / 85 respectively. According to the quadruple fluorescent PCR primer probe combination, the reaction system and the method for identifying the mycoplasma gallisepticum wild strain and the vaccine strain, the purpose of simultaneously detecting and distinguishing DNA of the mycoplasma gallisepticum wild strain and DNA of three different vaccine strains (F-36, TS-11 and 6 / 85) of the mycoplasma gallisepticum wild strain can be achieved, so that detection and differential diagnosis are more efficient, economical, simple and convenient; the kit has the advantages of high sensitivity, good specificity, rapidness, high flux, simplicity and convenience in operation and the like.

The invention discloses a multifunctional intelligent stomach tube, and belongs to the field of medical instruments, the multifunctional intelligent stomach tube comprises an outer tube mechanism, the outer tube mechanism comprises a stomach tube body and a rubber head, one end of the inner wall of the stomach tube body is provided with a slidable medicine feeding mechanism, and one end of the inner wall of the stomach tube body is slidably connected with the outer wall of a movable cylinder; a vertically-through pipe connecting through hole is formed in one end of the top of the movable cylinder, a medicine storage cavity is formed in the outer wall of the movable cylinder, a bottom cover is installed under the movable cylinder, and an illuminating lamp is installed at one end of the bottom of the bottom cover; by arranging the pushing inner pipe, the medicine feeding mechanism can be pushed, by arranging the medicine storage cavity, medical staff can place medicine powder or unground medicine in the medicine storage cavity, then the medicine is conveyed into the human body at a time through pushing of a movable cylinder, and the bottom face of the inner wall of the medicine storage cavity is arranged to be an inclined face, so that quick release of the medicine is facilitated; not only is the accuracy of medicine feeding improved, but also the situation that the medicine outlet is blocked is avoided.

The invention discloses a method and a device for determining a checking result of target equipment and electronic equipment. The method comprises the steps that a checking instruction sent by a target object is received, and the checking instruction carries a target device identifier; in response to the verification instruction, establishing a remote connection with the target device according to the target device identifier; determining a target checking list according to the equipment parameters of the target equipment and the to-be-executed task; and controlling the target equipment to check according to the target check list to obtain a check result. According to the method and the device, the problems that a conventional checking means is adopted for detection when checking is carried out on the target equipment before the to-be-executed task is executed, pertinence is not achieved, and the technical problem that accurate and timely checking is difficult due to the fact that timely updating is difficult to carry out after the equipment is updated in the prior art are solved.

The invention discloses a photovoltaic array fault diagnosis method, device and equipment, a medium and a program product, relates to the technical field of photovoltaic power generation, and discloses a photovoltaic array fault diagnosis method comprising the following steps: obtaining characteristic parameters of a photovoltaic array; wherein the characteristic parameters at least comprise a maximum power point voltage, a maximum power point current and a fill factor; based on the maximum power point voltage, the maximum power point current and the fill factor, performing fault diagnosis on the characteristic parameters of the photovoltaic array by using a pre-trained integrated support vector machine model to obtain a fault diagnosis result of the photovoltaic array; wherein the integrated support vector machine model is obtained by combining a support vector machine with integrated learning training. According to the technical scheme, the integrated support vector machine model has the advantages of the support vector machine in the aspect of classification and the characteristics of integrated learning in the aspect of integrating the advantages of a plurality of weak classifiers at the same time, so that the integrated support vector machine model has excellent diagnosis accuracy in the aspect of fault diagnosis of the photovoltaic array.

The invention discloses a hydroelectric generating set fault identification method and a hydroelectric generating set fault identification system based on ICEEMDAN-VMD-GAF and PCNN-BiGRU. The method comprises the following steps: firstly, collecting a vibration signal, decomposing the signal by using an ICEEMDAN algorithm, and extracting an IMF component; secondly, secondary decomposition and reconstruction are carried out on IMF components through a VMD and a wavelet denoising method; then, converting the IMF reconstruction component into a two-dimensional image by using a GAF algorithm; and finally, extracting image features by using a PCNN network, and inputting the image features into a BiGRU network for classification so as to realize fault identification. According to the method, the advantages of ICEEMDAN-VMD-GAF and PCNN-BiGRU are combined, fault features can be more effectively extracted from complex noise signals, one-dimensional vibration data are converted into two-dimensional image data, and the PCNN-BiGRU capability is fully utilized, so that the recognition accuracy is improved, and the prediction accuracy and the fault degree diagnosis capability are enhanced.

The present invention belongs to the field of biomedicine, and specifically relates to a combination and application of joint detection of biomarkers for Alzheimer's disease. The present invention provides an application of a biomarker group in detecting Alzheimer's disease, characterized in that the biomarker group includes: a) at least two of APOE4, GDF-15, UCH-L1 and PF4; or b) at least one of APOE4, GDF-15, UCH-L1 and PF4, as well as phosphorylated Tau protein and total Tau protein. The present invention also provides a kit for detecting Alzheimer's disease. The present invention contributes to a more effective detection and diagnosis of Alzheimer's disease, and provides a new path for the detection and prevention of AD.