183results about How to "Improve diagnostic capabilities" patented technology

The invention relates to power distribution network fault processing equipment applied to the technical field of power distribution, and aims to improve the fault prediction, diagnosis and processing capabilities of a power system. The equipment comprises a pre-training module, a neural network architecture module, a data acquisition layer, an edge calculation layer, a core processing layer, a context learning module, a reasoning processing module, an output and interaction layer and a continuous learning and improvement layer, and is characterized in that a large-scale pre-trained Transform model is utilized, and professional knowledge of a power system is combined; according to the method, deep understanding and analysis of a complex power grid state are realized, through multi-source data fusion, edge calculation and central cooperative processing, the equipment can monitor the power grid state in real time, accurately predict potential faults and provide intelligent diagnosis and repair suggestions, and model pre-training and optimization are performed by adopting self-supervised learning and federated learning technologies; using reinforcement learning to optimize a fault response strategy; the interpretability of the model is realized, and the decision transparency is improved.

The invention discloses an explosion-proof junction box fault diagnosis method and system based on a sensor, which are used in the field of fault diagnosis, and the method comprises the following steps: obtaining historical multi-source sensor data reflecting the state of an explosion-proof junction box; extracting a time sequence sample of the preprocessed historical multi-source sensor data by using a sliding time window method; multi-domain features reflecting fault types are extracted from the time sequence samples, and a fault diagnosis model is constructed based on the feature combination with the optimal diagnosis performance; performing feature extraction on the multi-source sensor data acquired in real time, and inputting the extracted features into a fault diagnosis model for fault diagnosis; and the operation state and the diagnosis result of the explosion-proof junction box are displayed in real time. According to the method, the subtle change of the fault signal is captured from multiple angles, the Pearson correlation coefficient is used for screening the features significantly related to the fault type, and the dimension reduction processing is carried out through principal component analysis, so that the operation efficiency and accuracy of the model are improved.

The invention relates to the technical field of medical diagnosis systems, in particular to an early gonitis diagnosis system and method based on multi-modal deep learning, a data collection module is used for collecting and preprocessing multi-modal data including knee joint X-ray image data, MRI (Magnetic Resonance Imaging) image data and clinical evaluation data; the model construction module constructs a deep learning model by combining a convolutional neural network and a Transform architecture, realizes mapping from multi-modal data to a clinical assessment phenotype label by adopting a feature automatic learning module and a potential relationship mining module, and integrates a Grad-CAM interpretability method for understanding a model prediction basis; diagnostic data are continuously collected to be fed back to the data collection module for model optimization, the convolutional network is good at capturing local image features, the Transform architecture is good at modeling a long-range dependency relationship, and the feature extraction and relationship modeling capabilities of the model are greatly improved through combination of the convolutional network and the Transform architecture.

The invention discloses a train key component online health monitoring method based on multi-sensor signal cooperation, and the method comprises the steps: selecting an indirect signal as an edge end train online abnormal state early warning index based on the correlation analysis of multi-sensor signals, and diagnosing whether the state of a train key component is abnormal or not; the vibration signals serve as signals for further diagnosis at the cloud, state features of the vibration signals are extracted at the edge end, online optimization of sparse coding is carried out, and the vibration signals are transmitted to the cloud to serve as diagnosis signal features for further diagnosis; real-time training precision and sample complexity are integrated into online estimation of a model training process, optimal features are transferred from a source domain-edge end to a target domain-cloud end, and the cloud end diagnoses the type and grade of a fault by means of the complex operation capability of the cloud end; a dynamic aggregation strategy is provided, the features of a prediction model are determined, optimization of nodes is enhanced, and a feature network is made to adapt to and complete an online prediction task.

The invention provides an oral disease early screening system and screening method based on an artificial intelligence auxiliary diagnosis system. According to the system, oral images and medical information of a patient are collected through a screening tool, an AI model is trained for auxiliary diagnosis, and intelligent services are provided for doctors in the diagnosis system. Artificial intelligence is introduced into oral medicine, the oral disease diagnosis efficiency and accuracy can be remarkably improved, support is provided for intelligent medical treatment, remote medical treatment and the like, and good application prospects are achieved. The method is suitable for the cross field of oral medicine and artificial intelligence, can be applied to hospitals, physical examination institutions and the like, and realizes early screening and early diagnosis of oral diseases.

The invention provides a dynamic load electric energy metering optimization method and system. Belongs to the technical field of data processing, and comprises the following steps: acquiring voltage and current signals in a dynamic load environment in real time by using a high-precision sensor, and discretizing the acquired signals to obtain a voltage value and a current value of a sampling point; and performing statistical analysis on the voltage and current values of the sampling points, extracting key parameters, and constructing a dynamic load feature vector based on the extracted key parameters. By collecting voltage and current signals in real time and carrying out accurate statistical analysis and feature extraction, the electric energy error value can be calculated more accurately, so that the accuracy of electric energy metering is improved.

The invention relates to the technical field of image processing, in particular to a static plantar pressure image data processing method based on an improved YOLOV5 model. Compared with traditional floor table type equipment and desktop level equipment, the static plantar pressure diagnosis system for auxiliary diagnosis of the flatfoot based on the YOLOV5 model can remarkably improve the flatfoot diagnosis effect. The improved YOLOV5 model added with the attention mechanism module can further improve the flatfoot diagnosis effect, early diagnosis and intervention are carried out on the patient, and good prognosis is achieved. Based on an advanced deep learning model and a comprehensive data analysis method, the accuracy and efficiency of flatfoot evaluation are remarkably improved through automatic auxiliary diagnosis. Through a highly automatic process, manual intervention is not needed in the whole process from data acquisition, preprocessing to diagnosis output, personal errors are reduced, and the reliability and consistency of diagnosis are improved. A non-professional user can easily use the foot sole health management system through a friendly user interface and simplified operation steps, the foot sole health management system is widely suitable for various scenes such as families, clinics and hospitals, and intelligentization and popularization of foot sole health management are achieved.

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 discloses a variable splicing marker for tuberculosis diagnosis, a screening method and application, and relates to the technical field of marker screening, and the technical key points are as follows: the variable splicing marker is AIM2-Intron3-IR; by combining bioinformatics analysis, in-vitro experiments and clinical queue verification analysis of a large amount of high-throughput sequencing data, variable splicing markers closely related to tuberculosis are mined and determined, and a high-efficiency new tool is provided for tuberculosis diagnosis.

The invention relates to the field of data processing, in particular to a data real-time monitoring method of a laser cutting machine. The method comprises the steps that data points in the operation process of the laser cutting machine are collected, the data points comprise laser power at different moments, and several neighbor data points are selected from historical data points; calculating the local fluctuation degree of the neighbor data points; then calculating the abnormal degree of the current data point; and finally, the abnormal score of the current data point is calculated, and when the abnormal score is larger than a threshold value, the laser cutting machine breaks down. The problem that the abnormal state of the laser cutting machine cannot be timely and accurately monitored is effectively solved.

The invention discloses a dry-type power transformer monitoring and diagnosis system, which relates to the technical field of power equipment monitoring and comprises the following steps: acquiring real-time monitoring data; the monitoring module receives and processes the real-time monitoring data from the data acquisition module, constructs a transformer monitoring model according to the real-time monitoring data, and generates a transformer monitoring coefficient; processing the historical data of the transformer based on a deep learning algorithm to obtain a transformer monitoring threshold value, performing comparative analysis on the transformer monitoring coefficient and the transformer monitoring threshold value, and performing intelligent diagnosis on the health state of the transformer according to an analysis result; and according to the fault diagnosis result, analyzing the insulating material performance coefficient and the temperature fluctuation coefficient of the transformer, and judging the fault diagnosis effect. The problems that an existing dry-type power transformer monitoring system is limited in monitoring parameter, low in fault diagnosis accuracy and incapable of responding in real time are solved, various working states of the dry-type power transformer can be efficiently and comprehensively monitored, and fault diagnosis can be carried out.

The invention discloses a transformer fault diagnosis method based on an adaptive fault attention residual network, and the method comprises the steps: employing an MEMS photoacoustic sensor with a capacitor resonator, analyzing a sound wave signal through a photoacoustic spectrum and a signal processing technology, collecting soluble gas in oil, and forming a parameter sequence; introducing an adaptive activation function and a fault index function; the feature distribution of a source domain and a target domain is adjusted by using an LMMD and a domain discriminator, the data of the source domain and the data of the target domain are from different distributions but share the same label space, and the edge distribution difference of the features and conditions is minimized through the LMMD and the domain discriminator to realize cross-domain invariant feature learning; the performance of the model is optimized by balancing hyper-parameters, and the loss meaning in all aspects is balanced to realize dynamic balance. The method can be effectively applied to the fault diagnosis of the transformer, and the diagnosis accuracy is greatly improved.

The present invention relates to the technical field of winding of a winding machine, and in particular to a control system based on the distribution of the initial and final speeds of the winding machine, comprising an input unit; a control unit, coupled to the input unit, for calculating a stepped curve with increasing speed; an execution unit, coupled to the control unit, for gradually increasing the linear speed of the winding machine according to the calculated stepped curve, wherein the control unit further comprises a dynamic optimization module, which adjusts the formula in real time according to the yarn tension and winding characteristics to achieve the most optimized winding speed distribution; through the precise distribution and adaptive adjustment of the initial and final speeds of the winding machine, the accuracy and adaptive ability of speed control are improved, the occurrence of the problem of white spot color difference in the inner layer is effectively avoided, and at the same time, fault prediction and diagnosis capabilities are provided, the production efficiency and product quality are improved, and significant technical advantages and economic benefits are achieved.

The invention discloses an electric power safety tool detection system and a detection method thereof, and relates to the technical field of electric power safety, and the system comprises the following components: a non-contact sensor array which integrates various non-contact sensors such as an infrared sensor, a laser range finder and an ultrasonic flaw detector, the infrared, laser and ultrasonic non-contact sensor arrays are respectively used for detecting the insulating property, the mechanical strength and the wear degree of the tool, and the multi-dimensional and high-precision automatic detection of the insulating property, the mechanical strength and the wear degree of the electric power safety tool is realized by adopting the infrared, laser and ultrasonic non-contact sensor arrays; according to the method, secondary damage possibly caused by traditional contact type detection to the tool is effectively avoided, particularly when the tool in the high-pressure and high-temperature extreme environment is treated, the safety of the detection process is remarkably improved through the non-contact characteristic of the method, subtle changes of the tool can be captured more accurately through non-contact type detection, and the detection accuracy of the tool is improved. Therefore, the detection accuracy and reliability are obviously improved.

A wind turbine generator variable pitch bearing fault diagnosis method based on signal coding specifically comprises the steps that original vibration signal data of a variable pitch bearing are acquired through a wind turbine generator state monitoring system arranged in a wind power plant; bearing vibration signals are respectively coded into a time-frequency diagram and a position matrix diagram by adopting continuous wavelet transformation and position matrix transformation, and the characteristic information of the signals is enhanced by increasing the visual dimension of data; a global feature extraction module and a local feature extraction module are respectively designed based on a global attention mechanism and a window attention mechanism, so that the model can fully learn feature information of different levels; a dual-channel parallel fusion network model is constructed according to global and local feature extraction modules to fuse global and local feature information, and the purpose of suppressing strong random noise interference is achieved. According to the method, the vibration signal characteristics of the variable pitch bearing in different states can be fully captured, and the problems of poor diagnosis performance and low precision of the variable pitch bearing caused by strong random noise interference can be effectively solved.

The invention discloses a bogie framework fault early warning method and system based on a dynamic strain signal, and relates to the technical field of bogie framework fault early warning, and the method comprises the steps: obtaining a real-time dynamic strain signal of train operation, and inputting the preprocessed real-time dynamic strain signal into a bogie framework fault early warning model, outputting a healthy dynamic strain data prediction value at the next moment through a healthy dynamic strain data prediction model in the model, and determining a dynamic strain data residual value based on the actual dynamic strain data value and the healthy dynamic strain data prediction value at the next moment; comparing the dynamic strain data residual error value with a dynamic residual error early warning threshold value and a static early warning threshold value, and performing fault early warning of different levels according to a comparison result; according to the method, historical health dynamic strain signals of train operation are utilized to train a bogie framework fault early warning model based on a health dynamic strain data prediction model and a dynamic early warning threshold generation module. According to the invention, more accurate online fault early warning can be realized.

The invention discloses a variable splicing marker for predicting the progress of tuberculosis and application thereof, and relates to the technical field of markers for predicting the progress of tuberculosis and application thereof, and the technical key points are that the variable splicing markers are DNASE1L2-IR and PHF11-IR, and the markers are used for predicting the progress of tuberculosis; the variable splicing markers closely related to latent tuberculosis and progressive tuberculosis are mined and determined by combining bioinformatics analysis and in vitro experiments of a large amount of high-throughput sequencing data, and a high-efficiency new tool is provided for predicting the progress of tuberculosis.

The invention discloses an electric tool fault type identification method and system based on data driving, which are used in the field of fault identification, and the method comprises the following steps: collecting operation data of an electric nail gun under different environment conditions and operation states; processing including time domain analysis and frequency domain analysis is executed on the operation data, and a waveform index and a rough principal component analysis method are used for extracting a feature set for distinguishing fault types in an analysis processing result; constructing a fault type identification model by using a support vector machine combination of K neighbors; performing fault type detection and recognition on the electric nail gun by using the trained fault type recognition model; and obtaining the fault type of the electric nail gun according to the analysis result. According to the invention, fault type detection and identification are carried out by using the model to quickly and accurately determine the fault type of the electric nail gun, the efficiency and accuracy of fault diagnosis are improved, and the cause and the feature of the fault are obtained by comparing the difference between different fault types.

The invention provides a traction substation grounding grid breakpoint fault diagnosis method, and belongs to the technical field of traction substation grounding grids, and the method comprises the steps: firstly constructing an equivalent simulation circuit grid model of a traction substation grounding grid, and simulating the pilot frequency AC excitation current of traction backflow; grounding grid potential distribution data are collected in different working states, and normalization is carried out to form training samples. And then constructing a fuzzy neural network fault diagnosis model, and training to obtain the fault diagnosis capability. And finally, collecting surface potential data of a to-be-detected traction substation grounding grid, normalizing the surface potential data, and inputting the surface potential data into the diagnosis model to judge whether a breakpoint fault occurs or not and the position of the breakpoint fault. According to the traction substation grounding grid breakpoint fault diagnosis method provided by the invention, the advanced electromagnetic field theory, fuzzy logic and neural network technologies are fully fused, the defects of the existing diagnosis method are overcome, and the technical problems of insufficient diagnosis precision and efficiency in the aspect of grounding grid fault diagnosis in the prior art are solved.

The snore feature extraction and analysis method comprises the following steps: (a) data acquisition: acquiring snore data of different crowds in a controlled environment through a microphone or a multi-sensor device; (b) signal preprocessing: denoising, normalizing and framing the acquired snore signals; (c) feature extraction: extracting snore features from time domain, frequency domain, cepstrum and statistical features; (d) feature optimization: performing feature screening on the basis of crowd differences to screen snore features which are most representative and mutually independent; (e) classification model training: selecting a suitable machine learning model for training; (f) system integration: constructing a snore analysis system. The snore analysis method can adapt to patient groups of different ages, genders and health states, and can be widely applied to snore analysis; the analysis accuracy and the diagnosis effect are improved through feature optimization and a personalized classification model for different crowds; the kit supports early screening and diagnosis of various sleep-related diseases, and has important clinical value.

The invention discloses an oral disease detection method based on an artificial intelligence large model, and relates to the technical field of oral medicine. Comprising the following steps: acquiring a tooth focus picture and corresponding oral cavity internal state description, respectively extracting picture feature information of the tooth focus picture and text feature information of the oral cavity internal state by adopting a feature extraction block BERT and a multi-scale feature extraction block MFEM, and identifying and eliminating false negative samples therein; a total loss function and a dynamic label distribution strategy are introduced into a YOLOv8 network, an improved YOLOv8 network is obtained, a data set is divided into a training set and a test set, the training set and the test set are sequentially input into the improved YOLOv8 network for verification, the verified YOLOv8 network is obtained, a tooth focus picture and corresponding text information are input into the verified YOLOv8 network, and focus area type detection and text description are obtained. According to the invention, detection and classification of various diseases in the oral cavity can be effectively improved, and corresponding focus area description and suggestion are given.

The invention discloses a two-stage multi-mode bearing fault diagnosis method based on a pre-training large model, and belongs to the technical field of bearing fault diagnosis. The method aims at solving the problems that a traditional method is poor in generalization and poor in robustness under multiple working conditions and small sample conditions. The method comprises the following steps: firstly, constructing a learnable multi-modal Tokens which comprises a multi-scale patch Token, a feature Token and a fault Token, and realizing efficient extraction and fusion of multi-modal features; a time-frequency semantic fusion module is introduced, and comprehensive time-frequency features are output through adaptive frequency coding, time coding and multi-modal fusion; and inputting the multi-modal feature sequence into a pre-training BERT model, and adopting a two-stage training strategy, in the first stage, performing self-supervised pre-training by taking mask signal reconstruction as a target, and in the second stage, performing parameter fine tuning by taking fault classification as a target. According to the method, the diagnosis accuracy and the cross-working-condition generalization ability under the small sample condition can be remarkably improved.

The present invention discloses an intelligent fault diagnosis method and system for explosion-proof motors in the natural gas industry, the method comprising the following steps: S10: acquiring multi-source heterogeneous data of explosion-proof motors; S20: performing enhancement processing on the multi-source heterogeneous data of explosion-proof motors to obtain an enhanced multimodal data set of explosion-proof motors; S30: performing feature extraction on the enhanced multimodal data set of explosion-proof motors using a deep learning model to obtain a high-dimensional feature vector; S40: based on the high-dimensional feature vector, a multi-task learning network is used to perform fault classification and fault severity evaluation to obtain a fault type probability distribution and a fault severity index; S50: generating an intelligent diagnosis report of the explosion-proof motor including maintenance suggestions according to the fault type probability distribution and the fault severity index. The method can make full use of multi-source heterogeneous data and realize an explosion-proof motor fault diagnosis method of intelligent diagnosis, so as to improve the diagnostic efficiency and accuracy and realize preventive maintenance.

The invention relates to the field of data analysis, in particular to a power distribution system data analysis processing method and system, and the method comprises the steps: constructing a time sequence segment which comprises a plurality of samples which comprise a plurality of features representing monitoring parameters; a plurality of historical time sequence segments are obtained, the historical time sequence segments are labeled, and labels are fault types; the matching degree of the historical time sequence segments is calculated, the historical time sequence segment with the maximum matching degree serves as the optimal matching time sequence segment, and the fault type corresponding to the optimal matching time sequence segment serves as the fault type of the real-time time sequence segment. The method has an effect of accurately extracting feature expression differences of different faults.

The invention provides a mechanical equipment migration fault diagnosis method and system based on a source field black box model, and relates to the technical field of mechanical fault diagnos.The method comprises the steps that acceleration signals of mechanical equipment in various states are obtained and preprocessed, and a preprocessed data set is obtained; constructing a source field black box model and training to obtain a trained source field black box model; constructing a target domain model; obtaining the KL divergence loss of the output of the source domain black box model and the output of the target domain model; obtaining the consistency loss of the target domain model by adopting an interpolation consistency regularization method; obtaining nuclear norm loss of the target domain model; obtaining supervised loss with an adversarial regularizer of the target domain model; optimizing the target domain model; and performing fault diagnosis on the mechanical equipment based on the optimized target domain model. The source domain knowledge is extracted and transferred to the target domain model by using the output of the source domain black box model, and source domain data and model parameters are not needed.

The invention discloses an SCADA (supervisory control and data acquisition) system optimization method for an intelligent power grid, which improves data processing efficiency and system response speed by performing data grading processing on edge computing nodes, and reduces data transmission load by adopting a differential transmission strategy and an intelligent compression technology. A deep learning algorithm is utilized to construct a self-learning fault prediction model to enhance fault prediction and diagnosis capability, a multi-layer data security and privacy protection mechanism is introduced to improve data security and privacy protection, and an intelligent scheduling module is introduced to enhance system collaboration and dynamic adaptability. According to the comprehensive optimization methods, the real-time performance, reliability and safety of the SCADA system are remarkably improved, so that the SCADA system is more suitable for the complex environment of the smart power grid, and the efficient, stable and safe operation requirements of the smart power grid for the SCADA system are met.

The invention discloses an auxiliary diagnosis system, an auxiliary diagnosis method, an auxiliary diagnosis program product and auxiliary diagnosis equipment for heart valve alignment repair, which can improve the safety and the effectiveness of a TAVR operation by analyzing operation images and problems of doctors, automatically answering related diagnosis, treatment and explaining problems and providing real-time operation suggestions and optimization schemes. The auxiliary diagnosis system is based on a trained basic visual question and answer model and comprises an input module, a visual module, a language module and an output module. The input module receives medical images and natural language questions as system inputs. The visual Transform model is used for processing the medical image and extracting visual features of the image; processing the question by a language Transform model, and generating an answer based on the visual features extracted by the visual module; and the output module outputs the generated answer in a text or voice form.

The invention relates to the technical field of clinical medicine, in particular to a metabolic marker combination, a detection kit and a detection method thereof and an auxiliary diagnosis method for female prediabetes mellitus. The metabolic marker combination is composed of Cer, Cer d18: 0 / 16: 0, Cer d18: 1 / 18: 0, Cer d18: 1 / 20: 0, Cer d18: 0 / 22: 0, Cer d18: 1 / 24: 0 and Cer d18: 0 / 24: 0. According to the invention, a group of small molecule metabolites is preferably selected. The group of small molecule metabolites are bioactive metabolites which are closely related to insulin resistance, impaired glucose tolerance and chronic inflammation, and are related to pathogenesis of prediabetes mellitus. The metabolic marker combination and the total clinical indexes are comprehensively used for assisting in diagnosing the prediabetes occurrence risk, the effect is good, and the prediabetes diagnosis performance of female people is greatly improved.

The application provides a wind tunnel pressure regulating valve opening set fault diagnosis method based on a composite criterion, relates to the technical field of equipment fault diagnosis, and comprises the following steps: training a fault diagnosis model by using seen fault sample, extracting sample features by using an encoder, and obtaining classification information and reconstruction information of the sample by using a classifier and a decoder; then, according to the output result of the model on training data, a composite criterion is constructed in a feature space, a probability space and a sample space, and is used for identifying unknown faults; finally, when a new fault sample is input, the composite criterion is used to judge the sample, accurate diagnosis of known faults and effective identification of unknown faults are realized. The composite criterion method has a clear structure, a simple implementation process, can be extended on the basis of an existing fault diagnosis model, and has good engineering application value.

The invention relates to a coil auxiliary device for reducing mammary gland MRI artifacts and a using method thereof.The auxiliary device comprises two water bags, the number of the water bags is two, each water bag is of a bag-shaped structure with one end open, the open end of each water bag is provided with a hollow tubular gasket in a surrounding mode, and the two gaskets are communicated through a water guide pipe; water guide holes are formed in the two gaskets and used for guiding out redundant water in the water bag. The present invention provides a relatively simple and economical solution compared to techniques that require complex modification of MRI equipment or use of high cost consumables. And water is used as a filling material, so that the cost is low, the water is easy to obtain, and special materials possibly causing burden to the environment are avoided.