78results about How to "Improve diagnostic capabilities" patented technology

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 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 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.

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

The present invention relates to the field of electronic product detection technology, and specifically to an intelligent detection system and method for consumer electronic functional devices, including a signal acquisition module, an interference identification module, a signal processing module, a data analysis module, an anomaly detection module, an adaptive learning module, and a central control unit; wherein the signal acquisition module: collects original electrical signals; the interference identification module: identifies and isolates electromagnetic interference and radio frequency interference components in the signal; the signal processing module: extracts and restores the original interfered signal; the data analysis module: determines the functional status of the product; the anomaly detection module: determines whether there is an anomaly in the functional status of the product; and the adaptive learning module: automatically adjusts the signal processing parameters and analysis algorithms. The present invention, through continuous data collection and analysis, not only improves the accuracy of fault diagnosis, but also reduces unexpected downtime through predictive maintenance, providing greater value for consumer electronic manufacturers and users.

The application discloses a strategy for extracting fault features of electronic circuit systems, named as SSEST strategy, which is used for perceiving global information and paying attention to notable local information, and mining important local information means realizing expression of intermittent fault features of electronic circuit systems, specifically, first, S transformation is performed on a circuit output time sequence signal to acquire time-frequency domain features, then a squeeze and excitation network attention module is used to distribute channel weights, subsequently, input into a Swin Transformer framework, and pay attention to local information related to faults from global signals, and deep mining is performed on fault features, and two electronic circuits are taken as experimental circuits, the proposed diagnostic strategy realizes rapid and high-precision diagnosis, and shows that the proposed multiple attention mechanism is efficient for feature mining of intermittent faults of electronic circuit systems.

The application discloses a resource self-adaptive federated learning multi-modal knee osteoarthritis auxiliary diagnosis method, which comprises the following steps: a server initializes a multi-modal model for knee osteoarthritis diagnosis, and pre-deploys a low-rank adaptive plug-in in a trainable layer; after a client reports local hardware resources, the server allocates a resource echelon and issues a two-dimensional clipping ratio; the client clips the model in depth and width based on the clipping ratio, activates a LoRA module of a specified layer, loads local knee osteoarthritis X-ray images and clinical text data for multi-modal training, and establishes semantic mapping of image features and clinical descriptions; the client only uploads an activated LoRA parameter update amount, the server aggregates and optimizes a global model through weighted average aggregation and key layer compensation; and the learning ability of the model to key discriminative features of knee osteoarthritis is strengthened in combination with real-time resource monitoring and knowledge distillation. The application covers heterogeneous computing power devices through a resource self-adaptive mechanism, and improves the participation efficiency of primary medical institutions.

The application discloses a polypeptide ligand for brain glioma diagnosis and preparation and application of a radioactive molecular probe. The polypeptide targeting a TREM2 protein has the amino acid sequence as shown in the following: HLRKLRKR. The targeted polypeptide designed by the application has the characteristics of small molecular weight, strong blood brain barrier penetration and strong TREM2 protein binding capacity. The radioactive nuclide labeled molecular probe prepared by the application has the characteristics of strong targeting and easy observation as a brain glioma imaging drug, and is convenient for clinical application. The application improves the diagnosis effect of brain glioma and provides a new idea for early discovery and early treatment of brain glioma.

The invention belongs to the technical field of biological detection, and particularly relates to a logic gate detection method and kit based on cycle index amplification combined with CRISPR / Cas12a. The main process of the detection method is as follows: miRNA-210 is combined with a template to activate cycle index amplification, and the obtained product P1 can be used as a catalyst of Cas12a / crRNA1. When the miRNA-21 exists, the miRNA-21 can be used as crRNA2, the crRNA1 and the crRNA2 cooperate to form a crRNA complete spacer region sequence, the Cas12a trans-cleavage activity is activated, and a detectable fluorescence signal is generated. According to the invention, miRNA-21 is used as a spacer sequence in a crRNA structure, the general applicability of miRNA as a functional element is shown, and an AND logic gate is established to realize simultaneous detection and result logic judgment of multiple miRNAs.

The invention provides a message processing method, and the method comprises the steps: receiving a message, and carrying out the mirroring of a target message to a mirroring server under the condition that the message is determined to be the target message based on a pre-configured access control list, and the target message is a message which is not matched in the access control list. According to the embodiment of the invention, the message is filtered by using the access control list, the possible abnormal message is quickly separated, and the possible abnormal message is mirrored to the mirroring server, so that the processing amount of message analysis of the mirroring server can be reduced, and the network diagnosis capability is improved. The invention further provides a message processing device, electronic equipment and a readable medium.

The preparation method of the novel CEST polymer contrast agent provided by the present invention solves the technical problems in the prior art that the exogenous small molecule contrast agents are easily metabolized, have low accumulation efficiency and short accumulation time at the lesion site, and the multifunctional modification and purification steps of the small molecule contrast agents are cumbersome. The preparation method is: aminosalicylic acid is coupled with an acryloyl chloride compound or an acrylic isocyanate compound to prepare a salicylic acid derivative monomer, and under the action of a free radical initiator, the salicylic acid derivative monomer undergoes a polymerization reaction to prepare a novel CEST polymer contrast agent. The preparation process of the method is simple, and polymer contrast agents of different molecular weights and functions can be obtained by adjusting the free radical polymerization conditions and copolymerizing with other functional molecules. The polymer contrast agent of the present invention has high accumulation efficiency and long accumulation time at the lesion site, and is easier to modify and functionalize than small molecule contrast agents. It can be widely used in the field of biomedical technology.

The invention relates to the technical field of medical imaging, and discloses a lower limb vein 3D imaging method, which comprises the following steps: identifying an imaging range of a lower limb vein patient, and collecting a lower limb slice image of the lower limb vein patient; analyzing the pixel distribution condition of the tissue type, and identifying a venous blood pixel intensity factor of the patient with the lower limb vein; constructing a pixel integral and an image of the corrected image, performing image segmentation processing on the corrected image to obtain a segmented image, and determining a vein blood vessel of the patient with the lower limb vein; analyzing the influence degree of the feature type on the lower limb vein patient, and performing dimension reduction processing on the vein features to obtain key vein features; and according to the key vein features, the pixel integral and the image, constructing a lower limb vein 3D image of the lower limb vein patient, analyzing an imaging effect of the lower limb vein 3D image on a vein blood vessel, and based on the imaging effect, outputting a 3D imaging result of the lower limb vein patient. The three-dimensional structure and the spatial relation of the lower limb veins can be visually displayed, and the accuracy of diagnosis results is improved.

A residual-knowledge-data jointly driven fault composite diagnosis method and system, in which the first step is to construct a probability graph model, including determining the structure and parameters of the probability graph model, and the second step is to apply the probability graph model online, calculate the online real-time monitoring data, and input the residual data of the features in the residual nodes, the direct monitoring data of the features in the data nodes, and the state of the features in the knowledge nodes into the probability graph model to obtain the posterior probability of each state of the diagnosis layer node, and output the state with the maximum posterior probability in the diagnosis layer as the fault diagnosis result; the system includes a memory and a processor, and the processor executes a computer program to implement the above method. The present invention integrates the advantages of the three types of methods, namely, residual-driven, knowledge-driven, and data-driven, into the same diagnostic system, realizes the complementary advantages of each other, and obtains better diagnostic performance.

The invention discloses an aero-engine fault diagnosis method based on a conditional physical perception diffusion model, and the method is characterized in that the method comprises the steps: obtaining a working condition monitoring parameter and a performance monitoring parameter in the operation process of an aero-engine; based on the thermodynamic system characteristics of the aero-engine, constructing a physical constraint system; a conditional physical perception diffusion model is constructed, noise addition and physical violation term injection are carried out in a physical perception forward process, and noise removal and physical violation term correction are respectively carried out through a noise prediction branch and a physical correction branch of a double-branch network by taking a fault type label as a condition in a reverse process. The double-branch network completes pre-training based on a physical constraint system; training the conditional physical perception diffusion model; based on the working condition monitoring parameters and the performance monitoring parameters, physical perception features are extracted through middle layer representation of the condition physical perception diffusion model, and fault diagnosis of the aero-engine is achieved through a fault diagnosis classifier.

The invention relates to the technical field of neural image analysis, and particularly provides a brain function connection intelligent screening method and system for autism spectrum disorders, and the method comprises the steps: firstly obtaining resting state functional magnetic resonance imaging data and phenotype information thereof, and extracting a blood oxygen level dependence value time sequence of each brain region after preprocessing; then constructing a multi-scale brain network comprising a low-order function connection matrix and at least one high-order function connection matrix; the matrix is converted into a brain function connection graph containing sub-graphs of different scales through threshold sparsification; meanwhile, phenotype embedding features are extracted from phenotype information; the graph data and the phenotypic features are input into a multi-channel neural network model for parallel processing and fusion, and joint feature representation is obtained; and finally, outputting an auxiliary diagnosis result of the autism spectrum disorder through the classifier. According to the method, by fusing the multi-scale brain function connection information and the individual phenotype features, the accuracy of autism classification diagnosis and the generalization ability of the model are effectively improved.

The present application relates to the field of agricultural big data and fertilizer supply chain management, and particularly to a selenium-rich bio-organic fertilizer supply chain collaborative management system based on big data, comprising: a source evaluation step: a management center calls material attributes, a raw material evaluation unit analyzes source quality fluctuations to distinguish batches; an efficiency drift analysis step: in response to abnormal control signals, a conversion analysis unit performs efficiency drift evaluation feedback analysis; a supply-demand collaborative matching step: in response to regular control signals, a collaborative demand unit obtains a collaborative demand coefficient, and a matching division unit performs quantitative matching analysis of control decision; a control execution step: compare control evaluation indexes to generate control signals; the present application constructs a source fluctuation perception and supply-demand collaborative matching closed loop, solves the spatiotemporal mismatch contradiction of raw material fluctuations and soil adsorption specificity, and improves the overall efficiency of the supply chain.

The invention relates to the technical field of remote control CT machines. The invention relates to a remote control CT machine detection method and device, computer equipment and a storage medium, and the remote control CT machine detection method comprises the steps: obtaining patient information, generating CT machine working information according to the patient information, obtaining remote control CT machine working information from the CT machine working information, obtaining CT image information from the remote control CT machine working information, and sending the CT image information to a computer. Generating an analysis and processing CT image instruction according to the CT image information, generating a CT image sending instruction according to the analysis and processing CT image instruction, generating hierarchical diagnosis information according to the CT image sending instruction, obtaining an illness state diagnosis result from the hierarchical diagnosis information, and generating diagnosis result storage information according to the illness state diagnosis result. The method has the effect of improving the uniformity of the CT machine in CT image processing, illness state recognition and patient information storage.

The invention discloses an evaporator control method and a control system thereof, and relates to the technical field of evaporation process control. According to the method, a material characteristic coefficient, a thermal efficiency coefficient and a heat transfer performance coefficient are obtained, and the thermal working condition adaptation degree is obtained by combining the pressure of an evaporation chamber and the vapor phase temperature; based on the current feeding flow, the adaptation degree and the heat transfer performance coefficient, the optimized feeding flow is obtained through a flow optimization model. According to the method, through multi-model collaborative analysis, dynamic optimization adjustment of the feeding flow of the evaporator is achieved, the operation stability, the thermal working condition adaptability and the overall heat transfer efficiency of the evaporation process are effectively improved, and the self-adaptive control capacity of an evaporation system is remarkably improved.