875 results about "Diagnostic accuracy" patented technology

The invention provides a wind power booster station equipment fault prediction and diagnosis method and system, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: constructing an equipment topological relation through a knowledge graph, employing a double-flow heterogeneous graph neural network to extract space-time cooperation features, generating a candidate path based on multi-hop reasoning, extracting a key evidence chain, and calculating a credibility score. And combining multi-scale fault feature reconstruction and Tsallis entropy calculation to obtain a diagnosis result. According to the invention, the fault root cause can be accurately identified, the diagnosis accuracy is improved, the false alarm rate is reduced, and decision support is provided for wind power plant equipment maintenance.

The invention relates to the technical field of medical data processing systems, in particular to an artificial intelligence-driven medical diagnosis and treatment data processing method and system. The method comprises the steps that a multi-modal medical data acquisition module acquires and processes multi-source heterogeneous medical data of a patient, and a standardized data set is generated; a medical feature depth extraction module performs multi-dimensional feature extraction on the data set, and constructs a dynamic evolution feature matrix; a multi-dimensional health state space construction module constructs a patient health state multi-dimensional space according to the matrix and determines a key medical early warning index set; the real-time medical data fusion module maps real-time data to the space to generate real-time health risk factors; and the medical risk prediction and decision-making module establishes a personalized model, outputs a disease occurrence probability and generates personalized treatment suggestions. The system solves the problems that medical data processing is difficult, diagnosis analysis is not comprehensive, and a treatment scheme lacks personality, and diagnosis accuracy and treatment pertinence are improved.

The invention discloses a multi-modal dynamic fusion and incremental learning fault diagnosis method for deep vertical shaft equipment, which belongs to the technical field of industrial equipment fault diagnosis, and comprises the following four steps of: constructing a pre-training large model to perform feature extraction, and relying on a multi-layer Transformer encoder and a dual loss function, establishing a multi-modal dynamic fusion and incremental learning fault diagnosis model; mining cross-modal universal fault features from vibration, temperature and current multi-modal time sequence data; according to the method, multi-modal features are fused, multi-modal association is constructed, modal weights are dynamically adjusted through a modal gating unit and a time delay compensation attention mechanism to adapt to signal quality changes, and meanwhile time sequence deviation is corrected to achieve accurate association; incremental learning is realized by using a decoupling projection layer, and a lightweight projection module is designed for a newly added fault task to suppress disastrous forgetting; network training is optimized, pre-training loss, incremental learning loss and attention regularization loss are integrated through a multi-objective loss function, and model stability and diagnosis precision are improved. The method has the advantage that the model stability and the diagnosis precision are improved.

The invention discloses a power distribution equipment remote diagnosis method based on edge computing, and particularly relates to the technical field of intelligent monitoring of power equipment, and the method comprises the steps: an edge computing node collects the operation state data of the power distribution equipment in real time; performing diagnosis analysis locally at the node to generate a preliminary diagnosis result and key data; uploading the structured data to a cloud according to a preset strategy, and checking the integrity; and the cloud platform performs association analysis on the multi-node data to identify common anomalies, dynamically optimizes a diagnosis algorithm, automatically triggers alarms and work order distribution in a grading manner, and realizes rapid closed-loop processing in combination with the positions and skills of operation and maintenance personnel. Through cooperation of the edge and the cloud, communication bandwidth occupation is reduced, diagnosis accuracy and real-time performance are improved, fault response time is shortened, and the method is suitable for line-level monitoring and operation and maintenance management of the power distribution network.

The invention discloses a bearing cross-domain fault diagnosis system and method based on a meta-learning domain adversarial graph convolutional network, and particularly relates to the technical field of mechanical fault diagnosis. Multi-source bearing vibration signals are integrated, and a cross-domain graph structure data set including node features and an adjacent matrix is constructed; performing adversarial training through a feature extractor and a domain classifier of the domain adversarial graph convolutional network, and combining a gradient inversion layer to extract domain invariant features; carrying out internal circulation task adaptation and external circulation element parameter updating by utilizing a element learning framework, and optimizing network parameters; and finally carrying out fault diagnosis on the target domain signal. And the total loss function of the system fuses task classification loss, domain adversarial loss and a graph structure regularization item, so that the cross-domain diagnosis precision is improved. The method effectively solves the problem of model generalization caused by domain difference, is suitable for bearing fault diagnosis scenes with few samples and multiple working conditions, and has the advantages of high robustness and high diagnosis precision.

The invention relates to an aeration fan predictive maintenance method, system and equipment based on multi-modal perception and adaptive learning and a medium. The method comprises the following steps: generating a time sequence data set through synchronous acquisition and combined noise reduction processing of a sensor group; generating a multi-dimensional feature vector through time-frequency feature spectrum characterization and interpretability contribution analysis in combination with dynamic weight distribution coupled by environmental factors; on the basis of the multi-dimensional feature vectors, real-time anomaly detection is carried out at the edge end through a lightweight model, and abnormal data fragments are uploaded to the cloud end; and performing cross-sensor bidirectional reasoning on abnormal data fragments through a reasoning model deployed at the cloud, reconstructing a sensor topological graph, intelligently triggering elastic incremental learning, cooperatively processing equipment degradation trend analysis, multi-source evidence fusion and space calibration, and outputting a life prediction result and a fault thermodynamic diagram. According to the method, the core pain points of high early fault omission ratio, insufficient model robustness and the like are solved, and cost reduction, efficiency improvement and equipment life prolonging are realized while the diagnosis precision is maintained.

The invention relates to the technical field of automatic deployment, in particular to a cloud code deployment system which comprises a resource monitoring module, a task scheduling module, a path construction module, a log analysis module and a container repair module. According to the method, by collecting the node load and dynamically collecting the node load, the bandwidth and the storage capacity, accurate comparison of the resource request and the surplus is achieved, the high-matching-degree task node relation is established, the resource allocation scientificity is improved, the scheduling priority and the mirror image dependency sequence are extracted, and the deployment sequence is optimized in combination with conflict task sorting; the scheduling adaptability in a multi-task environment is enhanced, the port and environment configuration is compared in a path construction link, the deployment compatibility is improved, the dependency relationship between deployment log analysis and fusion timestamp and exception identifier verification is improved, the diagnosis precision is improved, re-deployment is carried out based on state and frequency linkage in an exception processing stage, accurate node positioning is replaced, a closed-loop mechanism is formed, and the reliability of the system is improved. And the system stability and the self-adjusting capability are enhanced.

The invention relates to the technical field of hidden danger intelligent diagnosis, and discloses a hidden danger intelligent diagnosis method based on multi-modal feature fusion. The method comprises the following steps: firstly, synchronously acquiring a visual image, a voiceprint signal and vibration data of equipment, and constructing a multi-modal original data set; eliminating the time deviation of the multi-source data through space-time alignment processing to obtain a synchronous multi-modal data set; then respectively extracting a feature sequence of each modal, and establishing an association relationship between features by adopting a cross-modal association coding technology; and finally, inputting the fused multi-modal features into a hidden danger discrimination model, and outputting a diagnosis result and a disposal suggestion. According to the method, collaborative analysis of vision, voiceprint and vibration data is innovatively realized, the limitation of traditional single-mode detection is effectively solved, the diagnosis accuracy of equipment composite faults is improved, and a new technical means is provided for intelligent operation and maintenance of industrial equipment.

The invention provides a self-adaptive bearing fault diagnosis method based on multi-base wavelet fusion. The objective of the invention is to solve the problems of noise reduction, insufficient feature extraction and low diagnosis precision under noise conditions. A Kaisixi University bearing public data set is used as original data, and Gaussian noise with different SNRs is superposed to simulate various noise intensities. And uniformly carrying out length alignment, down-sampling, equal-length segmentation, division and normalization preprocessing. Then, wavelet bases such as sym4, db4, coif5 and the like are adopted for parallel multi-scale decomposition and reconstruction; and adaptively determining the number of decomposition layers and a threshold strategy according to the noise level, and generating a de-noising branch. And performing weighted fusion on the denoising results of the branches, and performing iterative denoising on the residual error. Signals subjected to noise reduction processing are sent to a double-branch convolution-cycle-attention network, a convolution layer extracts features, an LSTM and a self-attention module capture time sequence changes, and accurate recognition of various bearing faults is achieved. The training adopts a segmented attenuation learning rate and an early stop strategy, and the robustness and generalization ability of different SNR working conditions are improved.

The invention discloses a power grid intelligent auxiliary inspection system based on an unmanned aerial vehicle, and relates to the technical field of power grid monitoring. Comprising a risk prediction module, a task planning module, a navigation positioning module, an environment perception module, a data acquisition module, an anomaly detection module, a defect identification module, a sound wave diagnosis module, a monitoring adjustment module, an identification optimization module, a collaborative traceability module and an edge decision module. According to the method, the inspection coverage rate and accuracy can be improved, autonomous risk avoiding and dynamic task rearrangement of the unmanned aerial vehicle are realized, manual intervention and inspection risks are remarkably reduced, the adaptability to different defect types and scenes and the diagnosis accuracy are improved, and the initiative and economical efficiency of power grid operation and maintenance are remarkably improved.

The invention discloses a multi-modal power grid fault diagnosis method and system based on a causal event atlas, and belongs to the technical field of intelligent operation and maintenance of power systems. The method comprises the following steps: preprocessing historical fault case data of power grid equipment, and constructing a causal event atlas database; when a fault diagnosis request is received, analyzing the fault diagnosis request by the planning agent, generating an initial fault hypothesis set in combination with power field knowledge, endowing a corresponding credibility score to the fault hypothesis, retrieving the cause subgraph as evidence in an iterative loop, and updating the credibility score of the fault hypothesis by the reasoning agent; and generating a multi-modal diagnosis report after the termination condition is met. According to the method, the problems of insufficient causal modeling and poor interpretability of a traditional method are solved, and the diagnosis accuracy, efficiency and user credibility are remarkably improved.

The present invention belongs to the technical field of signal processing, and particularly relates to a multi-source data fusion fault diagnosis method and system based on the DS evidence theory. The method of the present invention comprises: first, collecting multi-source information of a mechanical device, wherein the multi-source information includes vibration data, image data and sound data; respectively calculating corresponding feature indicators; then, using a PCA algorithm to perform dimensionality reduction processing on the indicators; using a Bayesian fault diagnosis model to perform fault diagnosis, so as to obtain a diagnosis result from each sensor; and then, using the DS evidence theory to perform fault fusion diagnosis. The present invention can comprehensively consider the characteristics of various types of data, extract more effective fault indicators, achieve more comprehensive fault identification, and fuse diagnosis results from a plurality of sensors, thereby ensuring the accuracy of final diagnosis. The present invention is suitable for fault diagnosis of various complex mechanical systems.

The invention discloses a hydraulic engineering motor set fault early warning system, and relates to the technical field of hydraulic engineering equipment monitoring, and the hydraulic engineering motor set fault early warning system comprises the following steps: S1, collecting and integrating mechanical state data, including vibration, temperature and oil pollution monitoring; s2, electrical parameter monitoring and complementary analysis are carried out, and mechanical data are associated to identify faults such as short circuit; s3, hydraulic monitoring and cooperative early warning are carried out, pressure flow abnormity is detected, and mechanical and electrical data are linked; s4, multi-source data fusion and fault classification are carried out, and dynamic weight distribution is carried out to improve the diagnosis accuracy; s5, dynamic threshold optimization and cloud decision making are carried out, and the threshold is adjusted according to working conditions to reduce the false alarm rate; s6, closed-loop maintenance and intelligent pushing are carried out, faults are positioned, maintenance suggestions are pushed, and closed-loop verification is carried out; the method has the beneficial effects that accurate fault classification, cross-dimension collaborative early warning and closed-loop maintenance decision making are realized, and the operation reliability and the maintenance efficiency of the unit are remarkably improved.

The invention provides a septal tumor accurate diagnosis system based on artificial intelligence, a multi-scale attention fusion module receives multi-modal data, focuses a septal region, dynamically adjusts the weight and outputs a key feature vector, an adversarial self-supervision pre-training module digs tumor morphological features based on the vector, robustness and generalization ability are enhanced, and the accuracy of diagnosis is improved. And the causal intervention diagnosis decision-making module constructs a decision-making tree by using enhanced features to perform preliminary diagnosis and transmits causal information to the meta-learning enhanced diagnosis module, and the meta-learning enhanced diagnosis module adjusts parameters by using a small number of samples for rare subtypes, outputs a final diagnosis result and feeds back the final diagnosis result. According to the system, multi-mode and dynamic attention, cooperative confrontation self-supervision pre-training and causal reasoning are fused, rare tumor diagnosis is optimized through meta-learning, a closed-loop feedback and shared knowledge graph is formed, and the diagnosis precision, interpretability and rare case diagnosis capacity are integrally improved.

The invention relates to the technical field of transformer fault diagnosis, in particular to a transformer fault diagnosis method and system based on generative acoustic large model enhancement, and the method comprises the steps: constructing a generative acoustic large model, generating specified virtual reference Mel spectrum feature data through an acoustic semantic prompt, and carrying out the recognition of the Mel spectrum feature data; configuring a condition self-encoding model to perform feature reconstruction based on the diagnosis condition label and the current reference Mel spectrum feature, and calculating a reconstruction feature error metric value of the reference Mel spectrum feature and the corresponding reconstruction Mel spectrum feature; comparing the calculated reconstructed feature error metric value with a fault diagnosis error metric threshold value corresponding to the diagnosis condition label; and based on the time sequence characteristics of the reference Mel spectrum features, comparing the fault classification statistical information obtained by statistics with the fault diagnosis statistical judgment conditions to determine the fault diagnosis state of the transformer to be subjected to fault diagnosis. The transformer fault diagnosis accuracy can be improved, and the fault diagnosis cost is greatly reduced.

The invention belongs to the technical field of intelligent operation and maintenance and automatic inspection of power equipment, and particularly discloses a power transformation and distribution station room inspection method and device based on multi-mode visual perception. The method comprises the following steps: controlling the intelligent inspection robot to move and synchronously acquiring visible light, infrared thermal imaging, partial discharge ultrasound and other multi-mode sensing data streams; generating an ultrasonic-guided enhanced infrared image, a power equipment structure map and an ambient light field distribution map through deep fusion in combination with ambient illumination parameters; and on the basis of the deeply fused information, generating a refined health state level of the power equipment, and finally automatically generating an inspection report conforming to the regulation. According to the invention, through deep cooperation and mutual verification of the multi-modal data, the perception robustness and diagnosis accuracy of early faults and abnormal states of equipment in a complex environment are improved, intelligent inspection from later judgment to beforehand prediction is realized, and the power supply safety of a power transformation and distribution station room is guaranteed.

The invention discloses an automobile air flow sensor fault diagnosis method and system, and relates to the technical field of intelligent fault diagnos.The method comprises the steps that driving behavior parameters are collected in real time, and driving style feature vectors are obtained through classification of a driving behavior recognition model; theoretical air inflow is calculated based on an engine three-dimensional fluid dynamic model, and dynamic correction is carried out in combination with driving style characteristics to generate a corrected theoretical flow signal; and finally, the actually measured signal of the air flow sensor and the corrected theoretical signal are compared and analyzed, and accurate diagnosis of the fault of the air flow sensor is realized. The technical problems that an existing air flow sensor fault diagnosis method cannot depend on a static threshold value, is difficult to adapt to driving styles and working condition changes, is insufficient in accuracy and is prone to false report and missing report are solved, self-adaptive correction of the theoretical air inflow is achieved by combining driving behavior recognition and dynamic correction, and therefore the diagnosis accuracy is improved, and the fault diagnosis efficiency is improved. And false alarm and missing alarm are reduced.

The invention provides an edge computing remote medical task offloading method based on an improved PPO algorithm, and the method comprises the steps: dynamically selecting an optimal offloading mode through a PPO-MED intelligent decision module according to the characteristics of a medical task and a system resource state, and enabling the offloading mode to comprise a local computing offloading mode (LCO), an edge server offloading mode (ESO) and an edge intelligent preprocessing offloading mode (EIPO); a PPO-MED algorithm models a medical task unloading problem into a Markov decision process, and an unloading strategy is optimized through deep reinforcement learning. The improved PPO-MED algorithm realizes intelligent decision, can dynamically select the optimal unloading strategy according to the real-time system state, task urgency and resource availability, significantly improves the adaptive capacity and service quality of the system in a complex environment, compresses the original medical data into a plurality of orders of magnitude, and improves the reliability of the system. On the premise that the diagnosis accuracy is guaranteed, the transmission delay is remarkably reduced, and the method is particularly suitable for remote diagnosis application of basic medical institutions under the limited bandwidth condition.

The invention discloses a vibration signal processing method based on self-adaptive wavelet packet and deep learning fusion, and belongs to the field of sewage plant equipment fault diagnosis. The method aims at solving the problems that traditional signal processing is poor in flexibility, the non-stationary signal processing capacity is weak, the deep learning data requirement is large, and the high-frequency weak feature capturing capacity is limited. According to the method, the high-frequency acceleration sensor is adopted, the vibration signals of the sewage plant equipment are accurately collected, the self-adaptive wavelet packet decomposition technology is applied, the primary function is dynamically selected, the number of decomposition layers is optimized, self-adaptive threshold noise reduction is achieved, and the signal processing quality is improved. Meanwhile, in combination with a one-dimensional convolutional neural network and a bidirectional LSTM model, local and global features of the signal are extracted respectively, and pre-processed data are formed through gating weighted fusion. According to the method, the signal-to-noise ratio and the weak fault detection rate are remarkably improved, feature redundancy and data requirements are reduced, the calculation efficiency and diagnosis accuracy are improved, the method is suitable for sewage plant equipment fault diagnosis, and the industrial applicability is enhanced.

The invention relates to the technical field of power equipment fault detection, in particular to a power equipment fault diagnosis method and system based on a large power model, and the method comprises the steps: converting the multi-modal data of a power cable in operation into a feature vector, and inputting the feature vector into a pre-trained fault diagnosis model to obtain a preliminary diagnosis result and confidence; if the confidence coefficient is not lower than the threshold value, the preliminary diagnosis result is reserved; and if the confidence coefficient is lower than a threshold value, taking the feature vector as a current potential fault feature vector to perform secondary discrimination, namely obtaining a significance index by calculating the similarity and volatility of the current potential fault feature vector and a historical potential fault feature vector, analyzing a time change trend to obtain a cumulative trend index, and performing secondary discrimination on the cumulative trend index. And comprehensively determining a potential fault index through the significance index and the cumulative trend index, and determining a final diagnosis result according to the potential fault index. According to the scheme, the diagnosis accuracy of the fault diagnosis model on low-confidence potential faults and unknown faults is improved.

The invention discloses a relay protection device wave recording file analysis method based on multi-dimensional feature extraction and intelligent analysis, and belongs to the field of power system relay protection. According to the method, the wave recording files of different manufacturers can be identified, the data integration degree is high, the time domain, frequency domain and space correlation characteristics of the wave recording files are extracted for analysis, the characteristics of the wave recording files can be comprehensively reflected, a Transform model is constructed for fault diagnosis, historical fault cases are used for auxiliary diagnosis, the accuracy is high, the real-time performance is good, and the requirements of a smart power grid are met. The problems of low accuracy and low real-time performance during power system fault diagnosis in the prior art are solved.

The invention discloses an electric vehicle multi-source fault diagnosis and early warning method based on a decision tree model, and belongs to the field of intelligent diagnosis and operation and maintenance of new energy vehicles. According to the method, various data sources generated in the running process of the electric vehicle are comprehensively utilized, and the data sources comprise battery management system (BMS) alarm data, running state label-free data and battery monomer cycle life data. Through feature engineering construction and mutual information analysis, feature variables strongly related to faults are extracted, an up-down sampling strategy is adopted to optimize data balance, and decision tree classification models are constructed for various typical faults respectively. After the model is trained, the model is deployed to a cloud platform, and online state monitoring, real-time fault early warning and alarm reason tracing are realized. The method has the advantages of being high in interpretability, high in diagnosis accuracy, convenient to deploy and the like, is suitable for an electric vehicle full-life-cycle health management system, and can remarkably improve operation safety and maintenance efficiency.

The invention relates to a GIS (Gas Insulated Switchgear) equipment mechanical defect diagnosis method based on a Gramb angle field and a dual-channel PCNN-Attention neural network, and belongs to the technical field of gas insulated switchgear mechanical vibration defect diagnosis. The method solves the problems that traditional diagnosis depends on artificial feature extraction, so that subjectivity is high, information mining is insufficient, and defect severity evaluation is missing. According to the technical scheme, the method comprises the steps that a one-dimensional vibration signal is converted into a GASF two-dimensional image and a GADF two-dimensional image through a GASF field so as to completely reserve time sequence topological features; carrying out data enhancement by adopting an image geometric transformation technology so as to improve the generalization ability of the model; and a dual-channel PCNN-Attention model is constructed, and synchronous intelligent identification of defect types and severity is realized through parallel feature extraction and dynamic weight optimization of an attention mechanism. According to the method, the diagnosis accuracy, reliability and adaptive capacity are improved, and support is provided for equipment state operation and maintenance.

The invention discloses a depression multi-mode detection method and device based on a state space model. The method comprises the following steps: reading a face video sequence and an audio sequence of a detected person as input data; extracting spatial features to form a video spatial feature vector and an audio spatial feature vector; a state space model based on a Mama framework is adopted to learn the time sequence features, and a video time sequence feature vector and an audio time sequence feature vector are obtained; a multi-modal fusion module is used for carrying out conjoint analysis on the spatial features and the time sequence features, multi-modal spatial-temporal feature vectors are generated through splicing, and then dynamic weighted feature fusion is achieved through a self-attention mechanism to obtain fusion features; and the output module generates a final depression detection result through multi-layer full-connection network processing based on the fusion features. According to the method, the problems of insufficient diagnosis accuracy and robustness caused by information loss, insufficient multi-mode integration and poor real scene adaptability when long sequence data is processed by the existing depression detection method are solved.

The invention provides an intelligent and rapid pre-examination and diagnosis method and system for tree health, and relates to the technical field of tree management. The method comprises the following steps: constructing a liquid flow database of a standard tree of a target tree species, carrying out model training on an established liquid flow calculation model by utilizing the liquid flow database, then collecting liquid flow data of a to-be-detected target tree in a preset detection time and meteorological and soil information of a growth environment, inputting an optimal liquid flow calculation model, and calculating a theoretical liquid flow value of the target tree, comparing with an actually monitored liquid flow value, calculating a liquid flow deviation degree, grading according to a daily maximum liquid flow deviation degree, and comprehensively evaluating a tree health grade in combination with phenotype information to obtain a diagnosis result; the rapid and intelligent pre-examination diagnosis of the tree health is realized, and the intervention timeliness is improved; and multi-dimensional data fusion is adopted, so that the diagnosis accuracy is improved, and automatic monitoring and real-time early warning are realized.

The invention relates to the technical field of hydraulic system control, and discloses a fault diagnosis method and device for a hydraulic system of fully-mechanized mining withdrawing triangular area supporting equipment. The method comprises the following steps: acquiring real-time operation data of each oil cylinder through a hydraulic system sensor, extracting differences of motion states of the oil cylinders by adopting a time sequence analysis and principal component analysis method, and calculating quantitative indexes of inertia force distribution and non-uniform force distribution of the oil cylinders. And when the quantitative index exceeds a preset threshold value, identifying the abnormal oil cylinder by adopting a clustering analysis method, analyzing the correlation between the positioning deviation and the abrasion of the sealing element through a linear regression model, and calculating the occurrence probability of an early fault signal. The fault prediction grading result is generated based on the probability distribution, the oil cylinder control parameters are adjusted through the decision tree algorithm, the oil cylinder operation parameters are updated, dynamic adjustment of the hydraulic system and optimization of the supporting structure are achieved, the fault diagnosis precision of the hydraulic system is improved, and the stability and reliability of the supporting structure are ensured through real-time compensation control.

The invention relates to a rolling bearing fault diagnosis method and device based on a deep convolutional neural network, and the method comprises the steps: obtaining vibration signals of a rolling bearing under a plurality of working conditions, adding labels, and dividing the vibration signals into a training set and a test set; performing training and diagnosis evaluation on the deep convolutional neural network by using the training set and the test set to obtain a diagnosis model; the deep convolutional neural network comprises a plurality of network units which are connected in sequence, and each network unit comprises a convolution module, an efficient time channel attention module and a maximum pooling layer which are connected in sequence; the high-efficiency time channel attention module takes the output of the convolution module as the input, adopts a parallel channel attention module and a time attention module to adaptively distribute attention weights on each time slice and channel dimension, and performs weighted fusion on an output attention map and input features to obtain a time channel attention map; and important feature information is screened out and dimensionality reduction is carried out. According to the invention, the discrimination capability and diagnosis precision of the diagnosis model are improved.

The invention discloses an intelligent endoscope system with a multi-mode imaging function, and relates to the technical field of medical equipment, and the intelligent endoscope system comprises an imaging module which comprises a white light imaging subunit, a fluorescence imaging subunit and an infrared thermal imaging subunit; the data preprocessing module comprises noise suppression, SIFT registration and normalization; the image fusion module is used for decomposing four layers in a Laplacian pyramid, and reconstructing after feature weighting; the intelligent analysis module is used for extracting multiple types of features, performing CNN-Transform model reasoning and performing historical case verification; the display control module is used for 4K display, AR labeling and touch operation; and the data storage module is used for AES-256 encryption, DICOM conversion and three-level permission. The diagnosis accuracy is improved through multi-modal fusion, lesions are analyzed and efficiently recognized, operation risks are reduced through operation navigation, safety is guaranteed through data double backup, the system is suitable for multi-cavity diagnosis and treatment, and clinical precise diagnosis and safe operation are assisted.

Methods and systems for automated electrocardiogram (ECG) analysis using neural networks, enhancing the accuracy of beat-by-beat cardiac monitoring. The system utilizes a Generative Adversarial Network (GAN) and beat classifiers to analyze ECG data and detect conditions various beast properties of an ECG at a discrete level. Additional neural networks may be trained to detect beat based conditions such as premature atrial contractions (PACs) and premature ventricular contractions (PVCs). The GAN generates realistic ECG beats, while classifiers detect abnormalities. Additional transformers may be trained to detect rhythm based conditions such as AFib and Aflutter. Methods and Systems support real-time cardiac health insights and integrates with ECG devices for continuous monitoring, offering a robust solution for improving diagnostic accuracy.

The invention discloses a ship propulsion shafting fault diagnosis method based on multi-modal attention fusion, and relates to the technical field of ship fault diagnosis. According to the method, multi-modal data such as vibration parameters, lubricating oil parameters and cooling water parameters of key parts of a ship propulsion shafting are synchronously collected and preprocessed; a modal specific feature extraction strategy is adopted, a multi-modal attention fusion mechanism including intra-modal self-attention, inter-modal cross attention and adaptive dynamic weight distribution is designed, and heterogeneous modal features are effectively integrated. Compared with a single-mode method and a traditional feature splicing method, the method has the advantage that the diagnosis accuracy is remarkably improved. The method has high robustness, and can still maintain high diagnosis accuracy even under the condition that part of sensors fail.