852results about How to "Improve diagnostic accuracy" 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 relates to the field of vehicle data analysis, in particular to an automobile data analysis method and system based on an intelligent diagnostic instrument. The method comprises the following steps: acquiring vehicle multi-modal monitoring data based on an intelligent diagnostic instrument; operation power change calculation is conducted on the vehicle multi-mode monitoring data, time sequence state feature mining is conducted, and vehicle time sequence state features are obtained; carrying out multi-frequency noise reduction optimization on the vehicle multi-modal monitoring data, and carrying out normalized periodic evolution mining so as to generate normalized vibration periodic evolution characteristics; potential feature association deep mining is carried out on the vehicle time sequence state features and the normalized vibration period evolution features, then association graph modeling is carried out, and a vehicle multi-modal feature knowledge graph is constructed; and abnormal parameter deviation calculation and fault positioning traceability are carried out on the real-time operation parameters of the vehicle based on the vehicle multi-modal feature knowledge graph, so that a potential abnormal fault positioning point is obtained. According to the invention, efficient and accurate vehicle fault risk prediction analysis is realized.

The invention relates to the technical field of monitoring and early warning of power equipment, in particular to a monitoring and early warning method and system for substation power equipment. Comprising a data acquisition module, an intelligent diagnosis module, a risk assessment module, a collaborative early warning module and an interaction control module. The data acquisition module acquires equipment operation data including current, voltage, temperature and humidity and vibration signals. And the intelligent diagnosis module identifies equipment abnormity through the generative adversarial network and the graph neural network. The risk assessment module generates a risk assessment curve based on a fractional order differential model and a reinforcement learning algorithm. And the collaborative early warning module generates a graded early warning scheme by utilizing edge calculation. And the interaction control module displays a state and optimizes a control strategy through a human-computer interface. The intelligent monitoring and management level of the power equipment of the transformer substation is improved, the service life of the equipment is prolonged, and the operation and maintenance cost is reduced.

The invention provides a main helium fan fault diagnosis system and method based on deep learning, and belongs to the technical field of main helium fan fault diagnosis, and the method comprises the steps: fusing vibration, current, temperature and other multi-source sensor data, and carrying out the fault mode recognition and root positioning through an attention mechanism convolutional neural network; meanwhile, a health index prediction model and a dynamic early warning threshold value are introduced, and multi-level real-time early warning is achieved; in addition, through online learning and transfer learning technologies, the diagnosis model is endowed with sustainable evolution and rapid deployment capabilities. According to the method, fault diagnosis, trend prediction, early warning decision and adaptive learning are integrated, a new thought is provided for intelligent operation and maintenance of the main helium fan, and the method has important value in the aspect of improving safety, reliability and economical efficiency of a nuclear power unit.

The invention provides a tumor area double-view detection method and system based on image modalities and views, and relates to the field of medical image analys.The method comprises the steps that multi-modal image data are collected from the body of a patient, and image data of different view angles at the same position are obtained according to time frames; extracting multi-modal features from the image data of different modalities and different view angles, and performing weighted fusion on the extracted multi-modal features to form a fused feature map; inputting the fused feature map to train and predict the 3D-CNN network so as to identify and position a tumor region, and marking the tumor region by using a mask output by the segmentation model; calculating the pixel number of the tumor area in each layer of image according to the marked mask, and multiplying the pixel number by the volume of each pixel to obtain the volume of the tumor area; and the segmentation results of each layer of image are superposed, and a three-dimensional model of a tumor region is generated by using 3D reconstruction, so that the limitation of single-mode and single-view image data in tumor detection is overcome.

The invention provides a remote monitoring and fault diagnosis method for a distributed photovoltaic power generation system, and the method comprises the steps: obtaining the real-time power generation data of a polycrystalline silicon photovoltaic module and a monocrystalline silicon photovoltaic module in the distributed photovoltaic power generation system, and extracting key parameters reflecting the power generation characteristics of different types of photovoltaic modules from the real-time power generation data, establishing a plurality of power generation characteristic models comprising a photovoltaic module layout mode and an array connection topology, wherein the power generation characteristic models are used for describing current and voltage characteristics of the photovoltaic modules under different working conditions; the method comprises the following steps: continuously tracking subsequent power generation current, voltage and power of an abnormal photovoltaic module, optimizing an abnormality diagnosis model, applying the optimized model to other distributed photovoltaic power stations adopting the same photovoltaic module model and array connection topology, and determining model parameters in combination with influences of different geographic positions and environmental conditions.

The invention provides a new energy automobile charging pile fault diagnosis method and system. The method comprises the steps of obtaining charging behavior data of a to-be-detected new energy automobile charging pile to construct a feature matrix; when the new energy automobile charging pile breaks down, environment sensing data are collected, and a multi-modal feature matrix is constructed in combination with the abnormal feature vectors and the thermal distribution features; and constructing a fault classification model according to the dynamic fusion features, obtaining a predicted fault classification result of the new energy vehicle charging pile, recording the predicted fault classification result and a risk level for each risk assessment result, synchronizing a real-time response action to an equipment log, and dynamically updating the weight of the fault classification model and a risk assessment threshold for optimization. According to the method, deep fusion of dynamic behaviors and environment perception is realized, higher diagnosis precision, higher adaptability and more intelligent response capability are realized, and the method can be widely applied to the field of operation maintenance and safety guarantee of the new energy automobile charging pile.

The invention discloses an intelligent early warning diagnosis method based on an intelligent power plant, and the method comprises the steps: detecting an abnormality through employing an unsupervised learning method, and correcting a detection result in combination with a heuristic rule; adjusting the length of a dynamic time window according to the running state of the equipment, ensuring the requirement of data trend analysis, carrying out smoothing processing on trend data captured in the adjusted time window, and extracting fault trend characteristics; and performing abnormal distribution analysis on the fault trend characteristics, performing layered early warning on the faults according to abnormal levels, constructing a step-by-step troubleshooting model based on a Bayesian network, and analyzing fault causes step by step. According to the intelligent early warning diagnosis method, the influence of equipment faults on a power plant system can be effectively reduced, and the overall safety and stability of a power plant are improved.

The invention relates to the technical field of mechanical fault diagnosis, in particular to a mechanical fault diagnosis method and system based on a pulse neural network. The method comprises the following steps: constructing a fault diagnosis network, wherein the fault diagnosis network comprises an improved gating attention coding module and a pulse residual network; the method comprises the following steps: replacing a common convolution layer of a gating attention coding module with a one-dimensional sparse convolution layer, and replacing a two-dimensional batch normalization layer with a one-dimensional batch normalization layer to obtain an improved gating attention coding module; setting a pulse neuron in the fault diagnosis network as a PSN neuron, and embedding a time step shrinkage layer in the pulse residual network; and inputting the mechanical vibration signal of the current sampling period into a fault diagnosis network, and outputting a predicted fault category label of the current sampling period. According to the invention, high-efficiency and high-precision mechanical fault intelligent diagnosis is realized.

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 discloses a multi-mode breast ultrasonic diagnosis system based on a dynamic feature fusion network, belongs to the field of medical image processing, and particularly relates to the multi-mode breast ultrasonic diagnosis system based on the dynamic feature fusion network. The invention aims to solve the problem that the existing method is low in breast ultrasonic recognition accuracy. The system comprises a multi-modal data preprocessing main module used for preprocessing a collected multi-modal mammary gland ultrasound image to obtain a preprocessed multi-modal mammary gland ultrasound image; the multi-modal convolutional neural network main module is used for building a dynamic feature fusion network model; the model training main module is used for training an established dynamic feature fusion network model by using the preprocessed multi-modal breast ultrasound image to obtain a trained dynamic feature fusion network model; and the model test main module is used for loading the trained dynamic feature fusion network model, classifying a to-be-diagnosed multi-modal breast ultrasound image, and identifying benign and malignant breast tumors in the multi-modal ultrasound image.

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 relates to the technical field of photovoltaic system monitoring, in particular to a photovoltaic power station fault supervision system based on a neural network model, and the system comprises a data collection module which is used for collecting string current data and voltage data; the IV analysis module is used for obtaining a fault diagnosis result; the digital power station modeling module is used for identifying and numbering the coordinates of the photovoltaic string; an inspection route planning module; the unmanned aerial vehicle inspection module is used for performing inspection control on the unmanned aerial vehicle to obtain inspection data; an inspection data uploading module; the CV analysis module is used for performing defect identification on the inspection data to obtain a defect identification result; the fusion inspection analysis module is used for outputting a fusion diagnosis result and also used for selecting a final fusion diagnosis result; the visual presentation module is used for carrying out visual presentation; the report module is used for generating a report; and the navigation defect elimination module is used for carrying out navigation defect elimination. According to the invention, the automation level and inspection efficiency of fault diagnosis are improved.

The invention relates to the technical field of medical systems, in particular to an intelligent gastrointestinal postoperative intestinal obstruction diagnosis and intervention decision-making system and method, and the system comprises a data preprocessing module, a multi-modal data fusion module, a gastrointestinal function recovery model construction module, a self-adaptive progressive reinforcement learning optimization module AHRL and a diagnosis and intervention recommendation module. The self-adaptive progressive reinforcement learning optimization module optimizes an intervention strategy layer by layer through a five-layer progressive algorithm from the steps of dynamic adjustment of feature weights, recursive state propagation, layered accumulation optimization, symptom similarity matrix generation, progressive weight reverse feedback and the like, deep fusion of key symptoms by multi-modal data is achieved, and the accuracy of the key symptoms is improved. And the intervention scheme is updated in real time according to the patient state change. In the intervention decision recommendation module, the system generates personalized diagnosis suggestions based on the optimized strategy, carries out real-time risk assessment, and provides continuous and dynamic intervention support for the patient.

The invention discloses a multi-source modal data fusion-oriented charging pile fault diagnosis method and system. The method comprises the following steps of: acquiring message stream data, audio data and infrared sensing data associated with a charging equipment fault through a sensor system; and based on the characteristics of each modal data, fault characterization and identification are carried out by adopting a random forest, a convolutional neural network and a YOLO-v8 target detection model. According to the marked fault data, a mapping relation among multi-source data is established, and a Dempster-Shafer multi-source fusion theory is utilized to aggregate a multi-modal fault judgment result, so that the equipment fault is accurately diagnosed. According to the method, the accuracy of fault diagnosis and diversified presentation of fault types are effectively improved, the repair rate is reduced, and the method has a good engineering application prospect.

The invention discloses a craniocerebral injury degree diagnosis auxiliary system based on a medical image, and relates to the technical field of medical treatment, a primary judgment module can accurately judge whether a patient has the risk of interstitial encephaledema or not by analyzing the accumulation degree of interstitial fluid in white matter and quantitatively calculating a penetration anomaly coefficient Syxs, the ability of early risk screening is provided, and the risk of interstitial encephaledema can be accurately judged. Potential problems can be recognized in time before disease deterioration, a clinician is prompted to intervene rapidly, and therefore the prognosis effect of a patient is remarkably improved. When the risk of interstitial encephaledema exists, the type diagnosis module further distinguishes lesion types (such as brain contusion or other injuries) by extracting related distinguishing features in a multi-modal feature set, calculates a locking coefficient Sdxs, and provides a scientific quantitative basis for diagnosis of lesion types based on specific features of the lesions. According to the accurate type diagnosis method, diagnosis errors can be reduced, and reliable support is provided for clinical decision making.

The invention provides a valve leakage diagnosis method and system based on an improved Hereum algorithm optimized SVM (Support Vector Machine), and relates to the technical field of valve leakage diagnosis. Decomposing the collected original signal to obtain a limited number of IMF components; calculating a fuzzy entropy, and constructing a feature vector; taking the feature vectors as data samples, and dividing the data samples into a training set, a verification set and a test set; using the training set to train the SVM model, and according to the classification error rate of the verification set on the trained SVM model, performing iterative optimization on the parameters of the SVM model based on an improved He-Ma algorithm to obtain an optimized SVM model; inputting the test set into the optimized SVM model to obtain a valve leakage diagnosis model; and acquiring a new acoustic emission signal, and inputting the new acoustic emission signal into the valve leakage diagnosis model to obtain a diagnosis result. The method not only can improve the classification accuracy and generalization ability of the diagnosis model, but also can enhance the stability of the model under different working conditions and environmental noise.

The invention provides a rolling bearing fault diagnosis method driven by simulation data, and the method comprises the steps: constructing a five-degree-of-freedom dynamical model of a rolling bearing according to the failure forms of different fault types of the bearing, so as to represent the dynamic response of a bearing system in a virtual space; the method comprises the following steps: collecting vibration signals under healthy and different fault conditions in a real environment through a bearing fault simulation experiment table, and establishing a data set; the feature distribution difference between the simulation data and the measurement data is considered, and four optimization targets are constructed and are respectively used for difference feature learning and domain invariant feature learning; a Transform model is used as a feature extractor, and fault diagnosis is carried out under a cross-domain condition in combination with a mixed loss function. Compared with the prior art, the model established by the invention can perform effective diagnosis under cross-domain and sparse training data conditions, solves the problems of data feature distribution difference and sparse target domain samples in engineering practice, and has certain robustness.

The invention discloses a rolling bearing fault diagnosis method based on a double fusion attention mechanism, and the method comprises the steps: collecting a vibration signal, processing the vibration signal to form a two-dimensional time-frequency diagram, and dividing the formed two-dimensional time-frequency diagram into a data set; dual fusion attention based on channel attention and space attention is provided by improving a traditional mixed attention mechanism; constructing a dual fusion attention-multi-scale feature network fault diagnosis model based on dual fusion attention, and training the constructed dual fusion attention-multi-scale feature network fault diagnosis model through a data set; and finally, inputting a rolling bearing signal under a strong noise background into the trained double fusion attention-multi-scale feature network fault diagnosis model to obtain a fault diagnosis result. According to the invention, the problem of low identification accuracy in a complex background in the existing monitoring and diagnosis of the rolling bearing of the machinery is solved.

The invention discloses an air conditioner fault diagnosis method and system based on multi-intelligent-field large model cooperation, and the method comprises the steps: inputting the equipment information and operation parameters of an air conditioner in a multi-mode form of voice, text or image, and converting the operation state information into text representation in a unified manner through a multi-mode conversion model; extracting the information of the text through the diagnosis agent and predicting the type of a potential fault; and finally, obtaining a corresponding fault reason and a disposal suggestion by utilizing the disposal agent. According to the method, callable professional tools in the field of air conditioner system fault diagnosis are introduced, and professional knowledge in the field of air conditioner fault diagnosis is provided for the disposal agent through retrieval enhancement, so that the problem that a general large model is insufficient in professionality in application in the field of air conditioner systems is solved, and accurate and reliable air conditioner system fault diagnosis is realized.

The invention discloses an AE-GCN-based unmanned aerial vehicle system fault diagnosis method and equipment, belongs to the technical field of unmanned aerial vehicle fault diagnosis, and is used for solving the technical problems that an existing unmanned aerial vehicle fault diagnosis method depends on expert experience and feature engineering and has the defects of low efficiency, poor generalization ability and the like. The method comprises the following steps: constructing a corresponding structure topological graph based on a topological structure of a target unmanned aerial vehicle, and obtaining a topological matrix; performing node feature extraction on the structure topological graph to obtain a node feature vector; inputting the topological matrix and the node feature vector into a graph convolution neural network for graph convolution operation to obtain a node state feature vector; inputting the data into an attention mechanism module to obtain attention features, and mapping the attention features into node state updating parameters; comparing the node state updating parameter with the node state historical parameter, and positioning a fault node and a fault type; and predicting a fault propagation path based on other nodes directly connected with the fault node in the structural topological graph.

The invention discloses a medical ultrasonic image intelligent processing method and system, and relates to the technical field of medical image.According to the main scheme, normal feature coordinates of different organs and tissues are calculated by obtaining normal ultrasonic image data, to-be-processed image data and historical medical record information of a patient; extracting coordinates of a to-be-detected image and comparing the coordinates with normal coordinates for preliminary judgment; performing secondary judgment by combining ovality and circularity; setting a lesion gray threshold to judge whether a lesion exists or not; calculating the size of a lesion area when the lesion exists; according to the method and the system, the comprehensive blood flow evaluation index is calculated in combination with color Doppler blood flow imaging data, the disease evaluation value is calculated according to known medical record data and the like, the disease level is judged, the diagnosis report is automatically generated, the accuracy and the efficiency of medical ultrasonic diagnosis are improved, and powerful support is provided for clinical diagnosis.

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 provides an electric power system main equipment defect diagnosis method and system based on a large language model, and relates to the technical field of electric power system operation and maintenance and intelligent diagnosis, and the method comprises the steps: collecting defect description information of electric power system main equipment; preliminarily understanding the defect description information by using a pre-constructed natural language representation model, obtaining corresponding embedded representation, executing a defect level classification task and a defect basis classification task, and outputting a defect level classification result and a defect basis classification result; and summarizing by utilizing a large language model, fusing the summarized classification information into a prompt text, exciting the reasoning ability of the large language model through a prompt project, and finally obtaining a diagnosis basis and a diagnosis result corresponding to the defect description information. According to the method, excellent results are obtained in defect level classification tasks and question and answer tasks, and high-quality classification evidences and guidance information can be generated.

The invention discloses an indirect ELISA kit for distinguishing porcine actinobacillus pleuropneumoniae wild virus infection and vaccine immunity and application, and belongs to the technical field of biology. According to the indirect ELISA kit, screened ApxIVA truncated protein serves as a coating antigen, the amino acid sequence of the ApxIVA truncated protein is shown in SEQ ID NO.3, the ApxIVA truncated protein can be subjected to soluble expression in escherichia coli, only one His tag and one S tag are carried, and the structure and the function of the ApxIVA truncated protein are close to those of natural ApxIVA. The pxIVA truncated protein is used as an antigen, an indirect ELISA method and kit for distinguishing APP wild strain infection and gene deletion vaccine immunity are established, the kit has the advantages of high specificity, low background value, low cost and short detection time, and the kit has the advantages of improving animal husbandry APP diagnosis accuracy and improving animal husbandry APP diagnosis accuracy. And the method has important significance in the aspects of optimizing prevention and control measures and guaranteeing animal health and production benefits.

The invention relates to the technical field of artificial intelligence, and provides an autism multi-source fusion diagnosis method, device and equipment and a storage medium, and the method comprises the steps: obtaining multi-modal data of a to-be-diagnosed object; a virtual reality device is worn on the head of the to-be-diagnosed object, the virtual reality device is used for playing an interaction task to the to-be-diagnosed object, and the multi-modal data is collected in the playing process of the interaction task; the multi-modal data comprises behavior data, electroencephalogram data, expression data and eye movement data; and inputting the multi-modal data into a pre-trained multi-modal diagnosis model, performing single-modal feature extraction on the multi-modal data by using the multi-modal diagnosis model, and performing feature fusion diagnosis on the extracted single-modal features to obtain an autism diagnosis result of the to-be-diagnosed object. Through feature fusion and analysis diagnosis of multi-modal data, feature complementation of different modals can be realized, and the accuracy and credibility of autism diagnosis results are improved.

The invention relates to the technical field of container damage detection, in particular to a container damage detection method and system based on deep learning. Comprising the following steps: establishing a plurality of container categories, and establishing an image analysis model according to all container categories; setting an image acquisition strategy of the to-be-detected container, and obtaining an image data packet of the to-be-detected container according to the image acquisition strategy; generating a preprocessing result of the image data packet according to the image analysis model; generating a primary detection strategy of the to-be-detected container according to the preprocessing result, and generating a damage risk value of the to-be-detected container; a plurality of container categories are constructed based on different container equipment parameters, a sub-image model of each container category is constructed by using a deep learning technology so as to adapt to the damage conditions of different types of containers in the actual production process of different ports, preliminary damage detection is carried out by collecting image data of a to-be-detected container, and the detection accuracy of the to-be-detected container is improved. Container damage detection efficiency is improved, and detection operation and maintenance cost is reduced.

The invention discloses an intelligent operation and maintenance fault diagnosis system based on a communication network, which belongs to the technical field of communication networks and comprises a data acquisition preprocessing module, a network dynamic evolution module, a quantum auxiliary diagnosis module, a fault prediction evaluation module, a fault decision module, a fault operation and maintenance module and a fault feedback module. By integrating the data acquisition and preprocessing module, the network dynamic evolution module and the quantum auxiliary diagnosis module, the system can acquire and process the state information of a communication network in real time, and quickly position and accurately identify a fault scene in combination with the high efficiency of quantum calculation, so that the fault diagnosis time is remarkably shortened, the diagnosis accuracy is improved, and the fault diagnosis efficiency is improved. Through the fault prediction and evaluation module in combination with the results of network dynamic evolution and quantum-assisted diagnosis, prediction of a plurality of future network states can be generated, different fault scenes are simulated for evaluation, potential risks are effectively predicted, and meanwhile, through risk evaluation and sorting, operation and maintenance personnel are helped to preferentially process high-risk scenes, and the operation and maintenance efficiency is improved.

The invention provides a customized microscopic examination case grading retrieval system based on multi-modal learning, which is characterized in that a CLIP model is utilized to process a retrieval demand input in an image form, and after the retrieval demand is fused with a retrieval demand input in a text form, feature representation information of the retrieval demand is extracted; comparing with the feature representation of the image in the historical data after CLIP coding to obtain a historical image closest to the retrieval demand after fusion; according to the characteristic that medical images have many fine granularity differences, an existing feature matching mode is improved, hierarchical retrieval design for historical data feature representation is introduced, and the accuracy of customized retrieval is improved. According to the method, in combination with microscopic examination images of patients and specific requirements in diagnosis of doctors, historical related case images and diagnosis information are retrieved and presented, the doctors are assisted in making judgment, and the diagnosis accuracy is improved; the method is particularly beneficial to diagnosis of disputed cases, rare cases and the like in microscopic examination, and reliable basis and reference are provided for diagnosis and treatment of doctors.

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.