1858 results about "Diagnostic model" patented technology

Disclosed in the present invention are an adaptive deep transfer fault diagnosis method and system, an apparatus and a medium. The method comprises the following steps: S1: collecting vibration acceleration signals of industrial equipment under different working conditions, and dividing same into a source domain data set and a target domain data set; S2: building a self-tuning universal domain adaptive fault diagnosis model, which comprises a shared feature extractor, a known classifier and a plurality of unknown classifiers; S3: separately calculating a classification loss of known faults of the source domain, a discriminative loss of the plurality of unknown classifiers, a target domain soft consistency regularization loss and an information entropy maximization loss; S4: introducing a dynamic weighting strategy based on model uncertainty assessment to optimize the model parameters; and S5: using the model for diagnosis. The present invention can fully mine valid information in data, can establish reliable class decision boundaries, and in addition, uses the self-tuning dynamic update strategy to adjust weightings corresponding to different loss functions, thus allowing for quick generalization of the model to different industrial diagnosis scenarios.

The invention provides a power plant system equipment fault diagnosis method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a continuous operation state data flow of key equipment of a power plant, then carrying out the feature extraction of the continuous operation state data flow, and generating a fusion feature set reflecting the equipment operation state coupling relation; calling a pre-trained fault dynamic diagnosis model to perform state evolution analysis processing on the fusion feature set to generate a diagnosis intermediate result which comprises a fault potential node and a fault evolution path, and performing fault attribute analysis processing based on the diagnosis intermediate result to obtain a fault attribute analysis result; according to the method, fault diagnosis information including fault trigger conditions, fault development trends and fault influence ranges is generated, and finally, an equipment diagnosis report including early warning strategies and maintenance priorities is generated according to the fault diagnosis information and is transmitted to a power plant intelligent operation and maintenance terminal, so that the accuracy and timeliness of power plant equipment fault diagnosis can be improved, and the power plant equipment fault diagnosis efficiency is improved. Safe and stable operation of a power plant is ensured.

The invention relates to the technical field of industrial intelligent monitoring, in particular to a remote online monitoring method and system based on machine vision and artificial intelligence, and the method comprises the following steps: synchronously collecting a visible light video stream, infrared thermal imaging data and three-dimensional vibration spectrum data through an edge calculation node, and forming a multi-modal sensing data set; performing cross-domain feature alignment processing on the data set to generate a space-time synchronous composite feature matrix; inputting the composite feature matrix into a cascaded deep learning model, and outputting a three-dimensional diagnosis vector which comprises an equipment health state, an abnormal region and a fault probability; and matching the three-dimensional diagnosis vector with a historical reference vector through a DTW and cosine similarity combination algorithm to generate a deviation index, and outputting a self-adaptive alarm threshold and a maintenance suggestion scheme according to the deviation index. The method has the advantages of high multi-modal fusion degree, light diagnosis model and intelligent and adjustable alarm mechanism, and is suitable for real-time remote operation and maintenance in a high-risk industrial scene.

The invention relates to a railway traction substation state monitoring method and system, equipment and a medium. The monitoring method comprises the following steps: acquiring real-time monitoring data of the equipment in a railway traction substation; performing data preprocessing on the real-time monitoring data to obtain a preprocessed monitoring data set, performing protocol identification, and converting heterogeneous data in the monitoring data set into structured data according to a preset protocol template library; based on the structured data, time-frequency domain characteristic parameters of the equipment are extracted, and a multi-dimensional characteristic matrix is constructed; inputting the multi-dimensional feature matrix into a pre-trained hybrid diagnosis model, and generating an equipment health degree score and a fault probability value; according to the health degree score and the fault probability value, generating an early warning instruction in combination with a dynamic threshold algorithm; and generating a priority maintenance strategy through a maintenance strategy optimization model based on the early warning instruction and the equipment maintenance resource constraint condition. According to the invention, accurate perception and intelligent decision making of the equipment state are realized in a multi-source heterogeneous data environment.

The invention relates to the technical field of fault analysis, and particularly provides a monitoring fault analysis method fused with a multi-modal knowledge base, which comprises the following steps: collecting original data of a monitoring fault log, and preprocessing and storing the original data; performing data cleaning and feature extraction on the obtained original data of the monitoring fault log; constructing a searchable knowledge base based on the cleaned data; when the system triggers an alarm, mixed retrieval is executed through a dynamic routing mechanism; aggregating the plurality of retrieval results to generate an executable repair scheme; iteratively optimizing the decision process through manual feedback; and continuously optimizing the knowledge base and the diagnosis model to form a closed loop iteration mechanism. According to the scheme, the accuracy and response efficiency of fault diagnosis are improved.

The invention belongs to the technical field of artificial intelligence, and discloses a computer network fault detection system based on an artificial intelligence technology, which comprises a data acquisition module, a feature extraction and preprocessing module and a fault positioning and repairing module. According to the invention, the detection link is accurate and comprehensive, the data acquisition module performs multi-source fusion to obtain rich materials, the feature extraction and preprocessing module generates multi-dimensional vectors to capture complex features, and multi-level fault identification performs comprehensive troubleshooting and reduces misjudgment; the diagnosis process is intelligent and efficient, the hybrid fault diagnosis model fuses supervised and unsupervised learning, processes known faults and detects unknown anomalies, and supervised learning branches are optimized to improve performance; the repair work is timely and reliable, the automatic repair scheme generation method has strategy library matching, dynamic adjustment and rollback mechanisms, quick response and flexible repair can be achieved, and the subsequent diagnosis accuracy is improved through repair verification multi-dimensional evaluation and closed-loop feedback.

The invention relates to the field of mechanical equipment health management, in particular to a large sliding bearing fault detection and evaluation method, device and system. Comprising the following steps: collecting multi-source sensing data, and constructing a comprehensive data set; constructing a state space model based on a sliding bearing physical mechanism; the multi-source sensing data and the state space model are fused through Bayesian filtering, and hidden state parameter posterior distribution is dynamically estimated; generating a virtual fault sample by using a generative adversarial network in combination with a physical rule base; designing a Bayesian space-time sequence diagnosis model based on an attention mechanism, and generating fusion health state features; processing and fusing the health state features by using a degradation process model, and predicting the remaining service life of the bearing; and based on the health state, the fault probability and the remaining service life, setting multi-stage early warning threshold values, and triggering intelligent early warning. According to the method, the defect that a single model is insufficient in adaptability and generalization ability under complex working conditions is overcome, and the accuracy and reliability of fault detection are remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a fault diagnosis and recovery verification method and device, equipment and a medium. Inputting the candidate event and the context state into an intelligent diagnosis model to obtain a diagnosis result and a fault type, selecting a recovery path in a recovery strategy library based on the diagnosis result, generating a recovery execution instruction, creating a verification experiment based on the instruction, and injecting drill data to obtain verification data, and forming a verification conclusion according to the verification data, associating a recovery execution instruction, and issuing the instruction in the execution environment to complete recovery operation. According to the method, through multi-source monitoring data processing, intelligent diagnosis model reasoning, strategy library path selection, verification experiment verification and execution environment automatic recovery, a full-link automatic process from detection, diagnosis, recovery to verification is constructed, and accurate diagnosis and dynamic recovery in a complex environment are achieved.

The invention discloses a wind turbine generator data analysis and fault diagnosis method and system based on big data and artificial intelligence. According to the method, a blade image, a vibration signal, audio data and operation parameters are synchronously acquired through an unmanned aerial vehicle multi-mode sensor and a ground monitoring system, and a multi-source heterogeneous data set is constructed; after the data is classified and preprocessed, image features, vibration time-frequency domain features and operation parameter key value pairs are extracted respectively; dimensionality reduction is carried out by using an auto-encoder, feature-level space-time alignment is realized through an improved DTW algorithm, and a multi-dimensional fault feature matrix is generated; a hierarchical diagnosis model including a GRU auto-encoder, an MLP network and an attention mechanism CNN is constructed, and training is carried out by taking minimization of sub-model deviation as an optimization target; and finally, fusing multi-source features to realize fault classification, and generating a visual diagnosis report. According to the method, efficient fusion and accurate diagnosis of multi-source heterogeneous data are realized, and the accuracy and the real-time performance of fault detection of the wind turbine generator are remarkably improved.

The invention discloses a defect diagnosis method and system based on multi-modal data cooperative training, and belongs to the technical field of defect diagnosis, and the method specifically comprises the steps: constructing a multi-modal cooperative diagnosis model comprising a feature extraction sub-network and a cross-modal attention module; after multi-modal data is collected and preprocessed, initial features are obtained through the feature extraction sub-network, attention weights are generated through the cross-modal attention module, weighted multi-modal features are obtained, multi-scale fusion features are obtained through the multi-scale feature extraction sub-network, and a preliminary diagnosis result is given through cascade processing. Meanwhile, the data integrity is detected, and a modal missing scene is coped with through cascade collaborative diagnosis; and finally, comparing the two types of diagnosis results with a defect labeling sample to obtain a multi-modal collaborative diagnosis model after training optimization, inputting to-be-diagnosed sample data, outputting a final diagnosis result and updating the defect labeling sample, and realizing efficient and accurate defect diagnosis.

The invention relates to the field of equipment diagnosis, and particularly discloses an equipment predictive maintenance method based on deep learning, and the method comprises the steps: carrying out the time domain amplitude normalization and frequency domain weighted normalization of training data, and carrying out the dual-channel feature fusion, so as to obtain a fusion feature vector; performing feature extraction on the fused feature vector by using multiple groups of self-adaptive wavelet kernels to obtain a self-adaptive time-frequency feature vector; constructing a weight population through statistical characteristics, and screening the optimal initial weight of the initial diagnosis model; carrying out multi-scale depth feature calculation, time-frequency domain attention feature fusion, fault prototype comparative learning and a pre-constructed total loss function on the adaptive time-frequency feature vector, and carrying out iterative updating on the initial diagnosis model to obtain a diagnosis model; and the equipment is diagnosed through the diagnosis model. Multi-scale feature fusion and a double-path attention mechanism can cooperatively capture short-time impact and a long-period mode, and the limitation of a traditional method in diversified fault scenes is overcome.

The invention relates to the technical field of digital manufacturing, and discloses a digital twin middle station and self-healing decision system for oil and gas equipment management, which comprises a multi-source sensing terminal, a twin optimization module, a fault topology diagnosis module and an early warning execution terminal, a multi-modal synchronous fusion mechanism is constructed, and when the state of oil and gas equipment is monitored, a heterogeneous data alignment standard is formulated, and a dynamic frequency normalization rule is set for different equipment types, so that the definiteness of multi-source sensing data fusion is ensured; meanwhile, vibration, temperature, pressure and sound wave data are synchronously collected in real time, millisecond-level detection can be achieved, the data time difference problem can be eliminated, the accuracy of equipment state feature extraction is guaranteed, and the feature fusion error is further reduced; and generating a self-adaptive correction coefficient in real time by calculating the dynamic deviation of the physical model and the data model.

The invention relates to the technical field of fault diagnosis of power electronic equipment, and particularly discloses a fault analysis and diagnosis method and system for a mining high-voltage frequency converter, and the method comprises the steps: collecting the skin effect depth and parasitic parameter drift distance in real time under a high-frequency working condition through a multi-physics field sensor array; constructing three-dimensional current density distribution of the conductor based on a non-Euclidean space mesh generation technology, and dynamically correcting the equivalent resistivity of the conductor in combination with a metamaterial database to calculate a dynamic eddy current loss characteristic value; optimizing and solving the parasitic parameter time-varying evolution equation by using a quantum annealing algorithm to obtain a voltage peak sensitivity characteristic value; inputting the characteristic value into a pre-trained deep residual shrinkage network diagnosis model for multi-modal fusion analysis, and outputting a fault risk assessment result; according to the method, the problem of parasitic parameter time-varying characteristic modeling in the vibration environment is innovatively solved, and early warning of hidden faults is achieved.

The invention discloses an injection molding process fault diagnosis model training method and system based on a large language model and a fault diagnosis method. The model training method comprises the following steps: collecting and cleaning process parameters under the fault working condition of the injection molding machine, converting the process parameters into a natural language text, combining the natural language text with a fault label to construct a textualized data set, and dividing the textualized data set into a training set and a verification set according to a proportion; and in combination with the text data dimension and the fault category number, loading the pre-trained large language model and configuring a diagnosis model structure in a quantitative mode. And inputting the training set into a model to extract semantic features, processing the semantic features by a feature conversion module to generate a high-order feature vector, and inputting a classification head to output a fault category probability. And back propagation is carried out by using a loss function, and model parameters are efficiently and finely tuned in combination with low-rank adaptation and a layered freezing strategy. And repeating training until the performance reaches the standard, and outputting a final diagnosis model. The method is efficient in training, and can effectively reduce the maintenance and use cost of the model.

The invention discloses a boiler fault self-diagnosis method and related device, and the method comprises the steps: S1, collecting boiler operation parameters, generating virtual data in combination with a digital twinborn model, and constructing a multi-modal monitoring data set; s2, carrying out preprocessing and anomaly detection on the data set by utilizing an edge computing node, and obtaining a preliminary anomaly signal and a feature vector; s3, uploading the abnormal signal and the feature vector to a cloud end, and performing simulation verification through a digital twin engine; s4, inputting the feature vector and a verification result into a hybrid enhancement diagnosis model, and outputting a fault type and a probability; s5, reasoning a fault source and a propagation path in combination with the knowledge graph according to the fault type and the probability; and S6, generating a maintenance scheme based on the fault information, and optimizing the diagnosis model by using operation and maintenance feedback. According to the method, the boiler operation parameters are collected and combined with the digital twinborn model to generate the virtual sensor data, the multi-modal monitoring data set is constructed, synchronous monitoring of multiple data is achieved, and one-sidedness of single-parameter monitoring is avoided.

The invention relates to the technical field of power transmission and distribution production, and discloses a data integration and multi-modal diagnosis method based on a power transmission and distribution scene, and the method comprises the following steps: S1, enhanced integration of multi-source heterogeneous data: collecting time sequence monitoring data, text procedures and image data of power transmission and distribution equipment, constructing an equipment topological correlation graph through a graph attention neural network, and carrying out the enhanced integration of the multi-source heterogeneous data; node feature embedding is optimized through self-supervised comparative learning, an adversarial variational auto-encoder is designed for edge data, and an enhanced sample is generated in combination with physical constraints of equipment. According to the data integration and multi-modal diagnosis method based on the power transmission and distribution scene, the field adaptability and reliability of a diagnosis result are improved while the model fine tuning cost is reduced, and the knowledge migration problem of a general model in the power transmission and distribution scene is solved; the introduction of a dynamic knowledge graph and a multi-dimensional evaluation system realizes the real-time integration of new regulation knowledge and the comprehensive evaluation of model performance, and ensures the sustainable evolution ability and decision transparency of the diagnosis model.

The invention provides a disease screening system based on a large model. The disease screening system is used for solving the technical problem that an existing disease screening system is intelligently used for single special disease screening. The system comprises a scheduling model and a plurality of AI auxiliary diagnosis models, the scheduling model is connected with the plurality of AI auxiliary diagnosis models, and the scheduling model is connected with a big data disease library. According to the method, a high-level scheduling model is utilized, multiple single AI auxiliary diagnosis models are managed in a centralized mode, automatic calling of a multi-disease AI system is achieved, automatic structured report generation is achieved through a large language model, historical medical history is combined, progress is predicted, treatment suggestions and reference cases are automatically given, an existing clinician film reading workflow is fitted, and the efficiency is improved. The whole process of actual diagnosis decision making of doctors is greatly fitted, and the working efficiency of the doctors can be improved to the maximum extent.

The invention provides a small sample tympanic membrane image recognition method based on meta-prompt and knowledge driving, and the method comprises the steps: inputting a small sample training set into an initial multi-mode pre-training model, obtaining a prediction category, comparing the prediction category with a real category, and screening misclassification samples in combination with confidence to construct a meta-task set; and inputting the meta-task set into the primary diagnosis model, and outputting a primary diagnosis report. And then, optimizing the medical description text sample based on the preliminary diagnosis report by utilizing a knowledge refining model, and replacing the original text sample, so as to obtain an updated sample. And finally, iteratively training the initial multi-modal pre-training model by using the updated sample until a termination condition is met. According to the method, a closed-loop optimization system composed of a primary diagnosis model and a knowledge refining model is constructed. Under the condition of small samples, the system dynamically optimizes the visual-semantic understanding ability of the model by using error samples generated by the model, and the accuracy of small sample tympanic membrane image recognition is effectively improved.

The invention discloses a GIS (Gas Insulated Switchgear) tiny defect detection method and system based on X-ray multi-effect fusion, and the method comprises the following steps: firstly, exciting a latent defect in a GIS to generate partial discharge by using an X-ray photoionization effect, detecting a discharge signal by using an ultrahigh frequency method and an ultrasonic method, and after the discharge signal is processed by a noise reduction model, carrying out noise reduction on the discharge signal; and respectively obtaining an ultrahigh-frequency discharge phase spectrogram and an ultrasonic discharge phase spectrogram. Then, the imaging plate is placed on the back face of the GIS, digital DR imaging is carried out through X rays, and an X-ray image is obtained; the ultrahigh frequency discharge phase spectrogram and the ultrasonic discharge phase spectrogram are fused after being denoised, an X-ray image is combined to establish a GIS health state diagnosis model, and reliable defect detection is carried out on the GIS according to the evaluation score of the diagnosis model. According to the method, multiple effects of X-rays are fused, so that smaller GIS latent defects can be found, the detection sensitivity is remarkably improved, and the problem that the traditional method is difficult to identify the tiny latent defects is solved.

The invention relates to the technical field of equipment intelligent diagnosis, in particular to a generator digital twinning diagnosis method based on temperature field online simulation, which comprises the following steps: respectively constructing a three-dimensional virtual model, a temperature field simulation model and an intelligent agent diagnosis model; the digital twin is in data connection with the physical entity; data alignment is achieved through a unified space-time coordinate system, a mapping relation is established between a vertex of the three-dimensional virtual model and a grid node in the temperature field simulation model, and a fault probability matrix output by the intelligent agent diagnosis model is matched to a three-dimensional grid of the three-dimensional virtual model through spatial interpolation; according to a fault probability output by the intelligent agent diagnosis model, correcting a boundary condition of the temperature field simulation model, triggering local grid adaptive encryption of the temperature field simulation model, and marking a fault position in real time through a three-dimensional virtual model; and the simulation result of the temperature field simulation model verifies the fault probability output by the intelligent agent diagnosis model. Through the method, the fault identification accuracy can be effectively improved.

The invention relates to the technical field of wind power generation. The invention provides a wind field unit comparison type fault diagnosis method, system and device and a storage medium. The method comprises the following steps: synchronously acquiring operation and corresponding environment data of units of the same type in the same wind field, and establishing a multi-dimensional data set; building a normal working condition multi-dimensional parameter reference model by using clustering analysis and updating in real time; performing multi-scale comparison on a target and a reference unit, and extracting feature fusion to generate a comprehensive health index; establishing a double-layer diagnosis model, constructing a virtual unit based on digital twinning in the first layer, comparing residual errors and positioning a potential fault source, detecting outliers by using an improved isolated forest algorithm in the second layer, determining a fault propagation path in combination with an association rule, and establishing a fault mode knowledge base; and triggering an early warning mechanism according to the comprehensive health index and the fault mode classification. The problems that a traditional operation and maintenance mode is difficult in fault prediction, high in cost and lack of a reliable evaluation system, state monitoring is limited to a single parameter, multi-unit comparative analysis means are insufficient, and fault diagnosis is lagged are solved.

The invention discloses a distribution transformer monitoring system and method, and relates to the technical field of electric power intelligent monitoring, and the method comprises the steps: obtaining the three-dimensional temperature gradient field distribution and mechanical stress distribution of a distribution transformer based on a standardized multi-source data set, and outputting a comprehensive field matrix through a field coupling analysis method; performing spatial analysis on the comprehensive field matrix by adopting spatial hot spot analysis, identifying and marking temperature and mechanical stress abnormal areas, performing spatial aggregation and noise filtering through an OPTICS density clustering algorithm, and outputting abnormal feature vectors; inputting the abnormal feature vector into a pre-trained intelligent diagnosis model, outputting a comprehensive fault risk score and performing preliminary diagnosis; searching fault data in a historical case library based on the preliminary diagnosis result for comparison verification, and optimizing the preliminary diagnosis result through a machine learning algorithm to generate a final diagnosis result; according to the method, a field coupling analysis method is adopted to establish a heat-force bidirectional action model, and the spatial distribution characteristics of the temperature gradient and the stress tensor are accurately reflected.

The invention discloses a medical image intelligent diagnosis method and system based on deep learning, and the method comprises the steps: obtaining multi-modal medical image data through a medical image collection device, carrying out the data preprocessing, and forming a standardized image data set; constructing a deep learning diagnosis model based on multi-scale feature fusion, inputting the standardized image data set into the deep learning diagnosis model for transfer learning training, and optimizing model parameters by adopting a dynamic weight adjustment strategy; verifying the trained deep learning diagnosis model through an integrated learning framework, generating a diagnosis confidence score, and performing probability calibration on a diagnosis result in combination with a Bayesian optimization algorithm to obtain an optimized diagnosis model; and inputting medical image data to be diagnosed, and outputting a pathological classification result. The problems that in the prior art, multi-modal medical image data processing is insufficient, model optimization strategies are insufficient, and diagnosis confidence coefficient calibration methods are insufficient are solved.

The invention relates to the technical field of equipment fault intelligent diagnosis, in particular to an equipment fault intelligent diagnosis method and system based on a Modbus protocol. The method comprises the following steps: acquiring real-time operation data from target industrial equipment through a Modbus protocol, dynamically adjusting an initial sampling frequency based on an equipment operation state, and performing multiple verification and compensation correction on the acquired data to obtain a stable data stream; performing multi-scale decomposition and feature enhancement processing on the stable data stream, extracting a time-frequency domain mixed feature set, and constructing a feature evolution trajectory; inputting the feature evolution trajectory into a double-branch diagnosis model integrating equipment state prediction and fault classification, and outputting an equipment health degree score and fault type probability distribution; and constructing a dynamic fault threshold curved surface, carrying out multi-dimensional fusion decision by combining the equipment health degree score and the fault type probability distribution, and generating a graded fault early warning and maintenance strategy. According to the invention, the accuracy, timeliness and adaptability of industrial equipment fault diagnosis can be greatly improved.

The invention relates to the technical field of equipment health monitoring. The method comprises the following steps: constructing a modal drift factor reflecting a modal time sequence change trend based on a multi-modal input feature set so as to obtain a static feature and a dynamic feature, the method comprises the steps of obtaining a modal availability evolution sequence, inputting the modal availability evolution sequence into a pre-trained modal availability prediction model to obtain a data modal type, obtaining feature distribution condition information based on the data modal type, constructing a modal credible distribution diagram according to the feature distribution condition information, and determining a fault based on the modal credible distribution diagram and a preset fault determination rule. And constructing a fusion feature representation vector, inputting the fusion feature representation vector into the target diagnosis model, executing feature correlation analysis and trend recognition processing, and outputting a health state judgment result of the power transformation equipment. The method has the effect of improving the health state intelligent monitoring capability of the power transformation equipment.

The invention discloses a hydroelectric generating set operation fault data enhancement method and device, and the method comprises the steps: firstly collecting a set vibration signal, selecting a time-frequency transformation method to convert a one-dimensional vibration signal into a two-dimensional time-frequency image, enhancing the feature dimension of the signal, constructing a diffusion feature migration model, gradually disturbing the data distribution to Gaussian noise through forward diffusion, and carrying out the recognition of the Gaussian noise. The method comprises the following steps of: performing inverse denoising to generate simulation data highly similar to a real fault sample, realizing relevance learning and migration sharing of fault features among different working conditions in combination with an adversarial feature migration architecture, and finally evaluating an enhancement effect by calculating similarity among samples, and inputting enhanced data into a fault diagnosis model to verify precision improvement. Through the combination of time-frequency transformation and a diffusion model, sample scarcity and working condition barriers are broken through, a remarkable effect is shown in the aspects of expanding the fault sample scale and enriching the sample dimension, the similarity of generated data and a real sample is improved, the diagnosis precision is improved, and the model generalization ability is remarkably enhanced.

The invention relates to the technical field of aerocar mode switching, and discloses a distributed driving aerocar self-adaptive fault-tolerant control method for actuator multimode failure, which comprises the following steps: constructing a unified six-degree-of-freedom dual-mode state space model; residual signals are generated based on extended Kalman filtering and a sliding-mode observer, and fault types and positions are positioned in real time through a lightweight classifier; the method comprises the following steps: extracting residual time-frequency features, identifying hard faults by using a lightweight convolutional neural network, quantifying soft fault degrees through an incremental support vector machine, fusing multi-source information based on a Bayesian network to output fault types, levels and confidence coefficients, and introducing an incremental learning mechanism to realize self-evolution of a diagnosis model; a virtual control instruction is generated by adopting hierarchical sliding mode control, thrust and torque distribution of remaining actuators is optimized based on a dynamic quadratic programming algorithm, control parameters are adjusted online in combination with a Lyapunov adaptive law, aerodynamic interference and model uncertainty are inhibited, attitude stability and trajectory tracking in air-ground mode switching are guaranteed, and the method has the advantages of being high in reliability and high in reliability. And the fault-tolerant performance and the operation safety of the hovercar in the air-ground mode switching process are obviously enhanced.

The invention discloses a fault information adaptive diagnosis method based on a deep residual network. The method comprises the following steps: S1, obtaining a standardized multi-mode operation tensor; s2, outputting a multi-scale fault feature map; s3, generating a fusion feature tensor by using channel attention and a cross-layer connection mechanism; s4, establishing an initial fault classification model and outputting a fault identification result; s5, generating a self-adaptive optimization model; s6, performing pruning and quantification operation on the adaptive optimization model to generate a lightweight fault diagnosis model, and deploying the lightweight fault diagnosis model in edge computing equipment to realize real-time fault diagnosis; and S7, outputting a final fault identification result and an equipment health state evaluation report based on the lightweight fault diagnosis model. According to the method, a solid theoretical basis and a feasible engineering implementation path are provided for a high-robustness, high-adaptability and embeddable industrial equipment fault diagnosis system.

The invention discloses an intelligent diagnosis and risk assessment method and system for cerebral apoplexy related to atrial fibrillation, and relates to the technical field of atrial fibrillation detection.The method comprises the steps that multi-dimensional data of atrial fibrillation patients in a clinical information system are integrated, a structured database is generated, and a standardized data set is output; a standardized data set is adopted to train a first machine learning model, and model performance is optimized through parameter joint search and a training set-verification set convergence dynamic monitoring mechanism; performing cross validation on a feature weight sorting result in the optimized diagnosis model and a clinical index risk association degree calculated by a second machine learning model to generate an interaction map; and based on clinical event data containing timestamps in the structured database, adopting a third machine learning model to extract time sequence features, and combining with a survival probability analysis model to generate a risk assessment report. According to the invention, through an intelligent model adjusting and optimizing mechanism, multi-dimensional medical data are effectively integrated, and the recognition precision of the atrial fibrillation related cerebral apoplexy is greatly improved.

The invention discloses a heterogeneous system integration and fault diagnosis operation and maintenance system based on big data analysis, and the system is characterized in that the system comprises an acquisition cleaning module which is used for collecting structured data, semi-structured data and non-structured data, and carrying out the data cleaning; the mapping calculation module is used for dynamically mapping the cleaned data, and storing the data into a database after federal calculation; the feature extraction module is used for performing multi-modal extraction on the data in the database, constructing a knowledge graph and generating features for fault diagnosis; the model training module is used for constructing a fault diagnosis model and performing fault prediction and root cause analysis by using fault diagnosis features; the collaborative decision-making module is used for carrying out collaborative decision-making on the edge and the cloud according to the analysis result; and the feedback optimization module is used for feeding back the response processing result to the data center and carrying out updating iteration on the diagnosis model.