898 results about "Diagnosis methods" patented technology

The invention provides an AI intelligent diagnosis method and system based on an intelligent system, and the method comprises the steps: collecting a building electromechanical equipment operation data flow containing multi-source heterogeneous data, such as a vibration spectrum, a thermal imaging characteristic and an energy consumption fluctuation parameter, through an edge calculation node, and carrying out the state time sequence coding to generate a coding sequence; and inputting the sequence into a fault prediction neural network to obtain a multi-dimensional fault feature distribution matrix. And then obtaining a standard diagnosis report of the expert knowledge base and an emergency diagnosis report of the field operation and maintenance record. And then a composite training objective function is constructed based on the result and equipment operation characteristic hidden space mapping, a fault prediction neural network parameter space is optimized through a back propagation algorithm, and a target fault prediction neural network is generated. And finally, fault prediction and diagnosis are carried out on the operation data flow of the building electromechanical equipment by using the network, so that comprehensive, accurate and intelligent fault diagnosis is realized.

The invention discloses a multi-modal diagnosis and treatment data analysis and auxiliary diagnosis method for internet medical treatment, which comprises the following steps: S1, performing feature extraction and fusion on multi-modal diagnosis and treatment data through a nonlinear mapping algorithm based on quantum state optimization; s2, constructing a self-adaptive dynamic relation network, and capturing a complex interaction relation between cross-domain features in the diagnosis and treatment data; s3, fusing implicit features in the sparse medical data with semantic association; s4, optimizing the diagnosis and treatment decision process by applying a causal inference technology; s5, processing anomalies in the diagnosis and treatment data by using a fusion generative adversarial network and sparse tensor decomposition technology; s6, constructing a decentralized encrypted federated learning network to protect the privacy of the patient; s7, predicting a disease evolution path in real time to form dynamic decision support; and S8, performing accelerated analysis on the high-dimensional feature mode in the diagnosis and treatment data in combination with quantum calculation. The method has the advantages of high data fusion capability, high diagnosis precision and high real-time decision support capability.

The invention relates to the technical field of equipment fault diagnosis, in particular to a complex equipment fault diagnosis method and system based on a multi-modal knowledge graph. The method comprises the following steps: performing multi-modal feature extraction on acquired equipment operation data; constructing a knowledge graph based on the extracted multi-modal features; performing multi-modal feature enhancement on the knowledge graph through cross-modal comparative learning; defining a small sample task, and performing feature initialization on the enhanced knowledge graph; according to the invention, in combination with the causal association relationship contained in the knowledge graph, the system can realize dynamic perception and trend prediction of the equipment health state, and early warning of potential fault risks is realized in advance.

The invention relates to the technical field of rail transit, and discloses an intelligent diagnosis method and system for the health degree of urban rail equipment based on a cloud platform and a medium, the intelligent diagnosis method for the health degree of the urban rail equipment based on the cloud platform comprises the following steps: S1, multi-source data collection and edge preprocessing: collecting operation data of the urban rail equipment through a sensor, performing time sequence alignment and abnormal value filtering on the data to generate preprocessed data; s2, triggering type data uploading: based on the preprocessed data, judging whether an uploading condition is met or not through a statistical rule or a model prediction result, and uploading the data meeting the condition to a cloud platform; and S3, cloud model training and health degree prediction: constructing an equipment health state prediction model through a transfer learning algorithm by using the uploaded data. According to the invention, through deep learning modeling, edge calculation deployment and a multi-factor early warning mechanism, efficient evaluation, stable early warning and low-delay response of the equipment state are realized.

The invention provides an intelligent fault diagnosis method, system and device and a storage medium, and relates to the technical field of industrial automation, and the method comprises the steps: obtaining the operation parameters of a plurality of devices in an industrial automation production line, and enabling the devices to be sequentially connected according to a production technology process; inputting the operation parameters of each device into a preset fault detection model to generate a first diagnosis result of each device; obtaining a topological relation between the devices, establishing a device association diagram in combination with a production process flow and the topological relation, and generating association parameters between the devices according to the device association diagram; generating a second diagnosis result of each piece of equipment according to the associated parameters; and combining the first diagnosis result and the second diagnosis result to generate a final fault diagnosis result of each device. The technical effect of the application is that accurate and comprehensive fault diagnosis of the equipment is realized.

The invention provides a shield tunnel longitudinal seam joint recessive damage diagnosis method based on multi-mode ultrasonic guided waves, and the method comprises the steps: applying orthogonal polarization in a specific region of a longitudinal seam joint, and forming a space-time coupled guided wave interference pattern in the whole region of the longitudinal seam joint; the acquired guided wave interference map is utilized to separate baseline interference characteristics and abnormal disturbance components caused by hidden damage through multi-physics field reverse decoupling, and the abnormal disturbance components are represented as energy abnormal attenuation and phase distortion in a specific polarization direction; constructing a three-dimensional damage sensitive vector field in the longitudinal seam joint; and carrying out dynamic manifold topology analysis by utilizing the generated three-dimensional damage sensitive vector field, identifying topological characteristics in the three-dimensional damage sensitive vector field, and tracking a migration path and an intensity evolution trajectory of the topological characteristics along with the change of the detection condition. According to the method, full-dimension diagnosis and evolution prediction of the hidden damage of the longitudinal joint connector of the shield tunnel are realized.

The invention relates to the technical field of power equipment fault diagnosis, in particular to a power equipment fault intelligent diagnosis method and system based on deep learning. The method comprises the following steps: automatically learning high-dimensional space-time correlation features in original time series data through a deep feature extraction network, and generating feature vectors representing potential abnormal modes of equipment; performing adaptive weight distribution on the high-dimensional space-time correlation features by using an attention enhancement mechanism, and marking a fault sensitive area to form enhanced fault features; inputting the enhanced fault features into a multi-level classifier for joint fault mode recognition and severity evaluation, and outputting a diagnosis result tensor containing a fault type and confidence; and an equipment maintenance decision signal is triggered based on the diagnosis result tensor, and the feature extraction network and classifier parameters are iteratively optimized according to feedback data, so that the intelligent level of operation and maintenance of the power equipment can be comprehensively improved.

The invention provides a multi-parameter fusion drilling tool state intelligent diagnosis method, device and equipment, and the method comprises the steps: determining a stable working period through obtaining basic operation parameters of a drilling tool, and applying specific frequency excitation vibration to the drilling tool in the stable period to form an active propagation wave; a multi-point monitoring technology is adopted to obtain a response vibration signal and extract a time sequence, and a time sequence offset is obtained through differential processing; identifying a signal propagation delay section based on the time sequence offset, extracting actual propagation time, comparing the actual propagation time with standard propagation time to generate a time delay abnormal value, and determining an internal state change position; a high-damage section is determined by combining energy dissipation analysis, and fault positioning information is generated through spatial superposition; performing frequency sensitivity analysis by utilizing the damage characteristic parameters, capturing a resonance response peak value through frequency sweep excitation, and forming a secondary diagnosis result in combination with the wear severity; and finally, determining a fault development rate, generating a partition maintenance instruction, and completing intelligent diagnosis of the drilling tool state.

The invention relates to the technical field of bearing detection, and discloses a bearing fault diagnosis method and system, and the system comprises the steps that an acquisition unit collects magnetic signals of a to-be-detected bearing in a non-contact manner in the operation process of the to-be-detected bearing, and the magnetic signals specifically comprise magnetic flux density and magnetic conductivity; the preprocessing unit is used for preprocessing the acquired magnetic signals to form signal fragments to be analyzed; the first processing unit judges whether an early fault exists or not according to the preprocessed magnetic conductivity; the second processing unit extracts a fault discrimination feature vector based on Hilbert transform and wavelet packet reconstruction, inputs the fault discrimination feature vector into a historical discrimination database or a health state baseline model, and outputs a judgment result of a fault degree; the early warning unit determines an alarm level according to the fault degree and an early fault. According to the method, continuous sensing of multi-stage fault states is realized, and the recognition capability of early micro-damage such as fatigue cracks and local stripping is effectively improved.

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 discloses a multi-source heterogeneous medical data fusion and intelligent diagnosis method, and relates to the technical field of medical data processing and intelligent diagnosis, and the method comprises the specific steps: firstly, synchronously collecting medical images and clinical text data of a patient, and carrying out the correlation and integration to form a heterogeneous diagnosis data set; performing standardized feature extraction to obtain a feature set in a unified format; then constructing a parallel model, fusing features by using a cross-modal attention alignment technology, and guiding correction by means of a knowledge graph; and finally, the cross-modal diagnosis features are input into the reference model, automatic focus positioning is realized through processing, and a visual marker graph is output. Heterogeneous data of medical images and clinical texts are synchronously integrated, and the diagnosis feature reliability is improved through standardization processing, feature fusion and the like; a focus sensing mask is generated through comparison with a normal model, a multi-scale feature fusion technology is combined to realize automatic and accurate positioning of the focus, a large amount of labeled data is not needed, the process is simplified, and the diagnosis efficiency and accuracy are improved.

The invention relates to the technical field of fault diagnosis, in particular to a complex equipment fault diagnosis method and system based on dynamic feature modeling. The method comprises the following steps: acquiring multi-channel sensor data; performing data preprocessing on the acquired multi-channel sensor data; extracting initial channel features from the preprocessed data; on the basis of a direction perception mechanism, performing convolution enhancement after splicing the initial channel features, and generating global context features of structure perception; performing multi-time-sequence scale feature fusion on the global context features based on a multi-scale modulation mechanism; performing health trend guidance on the fused features based on a trend guidance supervision mechanism; the diagnosis precision, the trend perception capability and the practical response efficiency of the system are remarkably improved, the system is effectively adapted to core application scenes of multi-industry equipment in predictive maintenance, online fault perception, remote diagnosis analysis and the like, and the system has good popularization prospects and practical values.

The invention discloses an equipment fault diagnosis method and system based on a large electric power model, and the method comprises the steps: collecting and fusing voltage and current waveform data and alarm log information in a short time window before and after a fault moment, achieving the context integration of a multi-dimensional feature vector through multi-level feature coding and semantic embedding, and achieving the fault diagnosis of a large electric power model. And relevant knowledge fragment retrieval is carried out in combination with a power field knowledge base. On the basis, relevant knowledge fragments, voltage-current waveform fusion feature coding vectors and fault alarm key information semantic embedding coding feature vectors are jointly embedded into a preset Prompt template and then are input into a diagnosis engine based on large model driving, so that intelligent analysis and report generation of fault types, reasons and key features are realized. Through the mode, the limitation of dependence on single data or rules traditionally is broken through, the accuracy, automation and interpretability of fault diagnosis under complex working conditions are remarkably improved, and a solid support is provided for intelligent and high-reliability equipment operation and maintenance.

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 provides a helical gear speed reducer fault diagnosis method under intelligent monitoring, and relates to the technical field of fault diagnosis, and the method comprises the steps: obtaining real-time operation parameters of an inclined logistics conveyor belt, carrying out the load state evaluation of a helical gear speed reducer, configuring a first monitoring frequency and a first monitoring index number based on a load scale, activating a first sensing device, and carrying out the fault diagnosis of the helical gear speed reducer; monitoring is carried out according to the first monitoring frequency to obtain a first fault risk coefficient, if the first fault risk coefficient exceeds a first fault risk threshold, the sensing monitoring array is activated to obtain a second fault risk coefficient, and if the second fault risk coefficient exceeds a second fault risk threshold, the acousto-optic early warning device is activated to send out an early warning signal to carry out fault early warning. The technical problem of low fault diagnosis efficiency and accuracy caused by the lack of real-time monitoring and intelligent diagnosis means of the helical gear speed reducer in the prior art is solved. The technical effects of monitoring the running state of the helical gear speed reducer in real time and improving the fault diagnosis efficiency and accuracy are achieved.

The invention discloses a thyroid tumor diagnosis method and system based on ultrasonic and cytological image conjoint analysis. The method comprises the following steps: converting a thyroid B ultrasonic image of a patient to be diagnosed into an image feature vector FUS; performing structured feature extraction on the thyroid cytological image of the patient to be diagnosed to obtain a structured feature vector FCYTO; the image feature vector FUS and the structured feature vector FCYTO are converted into a fusion Token sequence; and inputting the Token into a multi-modal feature fusion and prediction network based on a Transform architecture, and carrying out feature fusion and classification prediction so as to obtain the probability that the thyroid tumor of the patient to be diagnosed is malignant. The thyroid tumor diagnosis based on multi-modal fusion is carried out on the basis of ultrasonic and cytological images, so that the diagnosis accuracy is improved.

The invention discloses a health monitoring system and method based on multi-sensor fusion. The system comprises a health index acquisition system used for acquiring various health index data through various sensors; the multi-modal data analyzing and processing system is used for analyzing and processing the obtained multi-modal health index data to generate sensing information and analyzing and processing different data to obtain corresponding health indexes; and the health index evaluation report system is used for generating a health index evaluation report according to the perception information. According to the system, multiple data sources including images, sign signals, voice interaction and the like are fused through multi-modal data, and comprehensive health assessment is achieved. Different from a traditional health monitoring system which only depends on a single signal, the system fuses various data and can provide a more comprehensive and detailed health analysis result. Four diagnosis methods of watching, smelling, asking and cutting in traditional Chinese medicine are creatively combined with a modern intelligent technology, and modern application of health monitoring is achieved.

The invention relates to the technical field of electric power fault diagnosis, and discloses a transformer area line loss abnormity diagnosis method and system based on a large model, and the method comprises the steps: collecting multi-dimensional data used for supporting transformer area line loss abnormity diagnosis, carrying out the cleaning, correlation fusion and standardization processing of the multi-dimensional data, and obtaining a comprehensive data set; a multi-layer diagnosis system based on a rule model, a random forest model and a large model is constructed, and the rule model identifies the simple and conventional anomalies of the transformer area according to a preset anomaly diagnosis rule based on the basic attribute data and the power operation state data in the comprehensive data set; the random forest model locates complex anomalies and novel anomalies by mining a coupling relationship among energy access condition data, external environment influence data and line loss fluctuation in the comprehensive data set; the big model carries out fusion verification on diagnosis results of the rule model and the random forest model, and outputs a final transformer area abnormity diagnosis result; and based on the diagnosis result, a differential loss reduction strategy adaptive to the actual data characteristics of the transformer area is recommended. The working efficiency is improved.

The invention discloses an equipment preventive maintenance and diagnosis method based on multi-dimensional data verification, relates to the technical field of equipment preventive diagnosis, and solves the technical problems that fault reasons are difficult to comprehensively and deeply analyze, and maintenance measures are lack of pertinence due to the fact that the fault reasons are often misjudged or cannot be positioned. According to the method, the fault diagnosis model is constructed, the adaptation algorithm is adopted for different types of data, the diagnosis accuracy of the model to equipment faults and the adaptability of the model to complex working conditions are improved, the equipment fault type and the abnormal state can be rapidly and accurately recognized, and the fault diagnosis accuracy is improved through the methods of fault phenomenon matching, parameter correlation analysis, historical data deep backtracking and the like. According to the method, fault reasons can be accurately determined, the performance degradation trend and potential risks of the equipment can be effectively identified in combination with a hybrid similarity measurement method, correlation among data is further mined through a secondary analysis mechanism, the potential risks are accurately pre-judged in combination with historical data, and real preventive maintenance is achieved.

The invention discloses a bridge state diagnosis method based on data visualization, and relates to the technical field of state diagnosis, and the method comprises the following steps: obtaining the rigidity value of a main beam monitored at each monitoring point, judging the fuzzy state of the monitoring point, screening to-be-analyzed monitoring points, setting a first mark, calculating the diffusivity according to the first mark, and setting a second mark. And calculating a growth rate according to the second mark, judging a damage level, and constructing a state judgment model. According to the invention, a safety early warning function is realized, abnormal conditions of a bridge structure can be monitored in real time, bridge operation safety can be guaranteed, the service life of the bridge can be prolonged, and scientific basis and guidance are provided for bridge managers and engineers based on bridge bearing capacity evaluation and damage identification results of big data and artificial intelligence. The method helps the bridge to formulate an effective maintenance and repair strategy, analyzes the sensor data of the bridge through a deep learning algorithm, recognizes potential damage and structural defects, and improves the accuracy of damage recognition.

A small pressurized water reactor fault diagnosis method based on a CNN-LSTM hybrid network comprises the following steps: firstly, acquiring sensor signals in a pressurized water reactor control system under a normal working condition, introducing corresponding faults at different positions according to characteristics of various typical faults, and acquiring corresponding sensor signals under different working conditions; discrete sampling processing is carried out as a training set and a test set; preprocessing the fault training set by adopting a signal-image conversion method, inputting obtained image data into a CNN-LSTM diagnosis model and training, and then verifying the diagnosis accuracy and robustness of the model based on test set data; parameters in the diagnosis model are continuously adjusted, so that an optimal diagnosis model is obtained; according to the technical means of combining the CNN network for extracting image features and the LSTM for extracting time sequence data fault features, the defects of a traditional fault diagnosis method in the aspects of diagnosis efficiency, accuracy, effect and the like are overcome; the invention further comprises a system, equipment and a storage medium for implementing the method.

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 relates to the technical field of wind power generation, in particular to a blade icing diagnosis method and device, an intelligent deicing system and a storage medium. According to the invention, blade icing diagnosis is carried out by adopting multi-modal data, the early detection accuracy of blade icing is improved, the false alarm rate of single sensor detection is reduced, and accurate diagnosis of blade icing is realized. When the multi-modal data is processed, a dynamic alignment algorithm is adopted, the limitation that traditional data alignment only depends on timestamps is broken through, meteorological data is introduced to serve as an alignment constraint condition, and the problem that physical relevance of the multi-modal data is missing is solved. And meanwhile, a hybrid expert architecture is adopted to process the multi-modal data, so that effective fusion and high-robustness diagnosis of the multi-modal data are realized.

The invention discloses an intelligent automobile interpretable abnormity diagnosis method and system, and relates to the technical field of intelligent traffic. The method comprises the steps of collecting multi-dimensional sensor data based on an intelligent automobile test platform, and constructing a directed causal graph and a causal adjacency matrix which are used for describing a causal relationship between the sensor data; designing a causal constrained graph attention mechanism based on the causal adjacency matrix, and constructing a causal constraint enhanced graph attention anomaly diagnosis model; and based on the directed causal graph and the graph attention anomaly diagnosis model, constructing a hierarchical anomaly diagnosis strategy integrating a feature reconstruction error, a variable causal relationship and a graph attention network weight, positioning an anomaly root cause and identifying a propagation path of the anomaly in the sensor network. According to the invention, the problems of false correlation and lack of exception explanation ability of graph attention network learning in the prior art can be overcome, and reliable exception detection and root cause diagnosis of intelligent automobile multi-sensor data are realized.

The invention discloses a shale gas well middle and later period effusion intelligent diagnosis method based on LSTM and TCN models. The method comprises the following steps: S1, collecting and preprocessing multi-source time sequence data, performing sliding window segmentation, and generating a standardized data set; s2, manually marking an effusion starting point, a critical point and a process intervention point, and generating a training sample set with a label; s3, performing multi-layer expansion causal convolution on the time sequence convolutional network, and extracting multi-scale local features; s4, the modeling of the long-short-term memory network is globally dependent, and output diagnosis and early warning are discriminated; and S5, regularly collecting newly added data and diagnosis results, dynamically updating the sample set, and retraining and optimizing the model. According to the method, intelligent, efficient and real-time diagnosis and early warning of the liquid accumulation state in the middle and later periods of the shale gas well are achieved.

According to the lightweight helicopter fault diagnosis method and device based on cloud edge cooperation and the medium, a dynamic cooperative distributed intelligent diagnosis system is constructed between the cloud end and the edge end, so that efficient lightweight reasoning of the edge end and centralized optimization training of the cloud end are realized; therefore, an aviation health management platform with self-learning, self-adaption and online updating capabilities is constructed. The whole system adopts a cloud-edge-end three-level architecture, wherein the end side is responsible for data acquisition and preprocessing; the edge side undertakes real-time diagnosis and lightweight model reasoning; and the cloud is responsible for global training, model scheduling and strategy optimization. Cyclic interaction of model parameters, task instructions and diagnosis results is achieved between the cloud and the edge through a secure communication link, and a closed-loop intelligent updating mechanism is formed. According to the method, the real-time performance, the computing power efficiency, the model generalization ability and the system adaptability of a fault diagnosis system are remarkably improved.

The invention discloses a three-dimensional positron emission tomography data image classification method based on multi-stage progressive feature extraction, and is applied to the technical field of Alzheimer's disease auxiliary diagnosis. The auxiliary diagnosis method comprises the following steps: acquiring and preprocessing PET image data of an Alzheimer's disease patient; improving the reliability of the data set by using data enhancement; performing long-range dynamic modeling on the three-dimensional voxel sequence through a stacked Lmamba block; global context semantic adaptive fusion is realized through a layer-by-layer cross-scale channel attention fusion module (CSACF), and a channel spatial perception module (CSPM) is constructed to optimize spatial feature fusion; an inverted bottleneck module is mixed with long-distance space and position information to enhance the capturing capability of the model on detail features; and finally, predicting the disease category probability through global average pooling, full connection and softmax functions. According to the method, the precision of AD early diagnosis and MCI conversion risk prediction can be greatly improved, the defects of a medical image diagnosis method of a convolutional neural network (CNN) and Transform in long-range dependence on modeling and calculation complexity are overcome, and the method has good application prospects and is suitable for AD early detection and MCI conversion risk assessment.

The invention provides a fault map learning-based early abnormality diagnosis method and system for an anion exchange membrane electrolysis system, and relates to the technical field of fault diagnosis. The method comprises the following steps of: acquiring multi-working-condition operation measurement and constructing a time sequence characteristic fragment, establishing a fault map containing parts, working conditions and measurement nodes by combining a part relationship and working condition dependency, training and calibrating a map learning model on the fault map, and extracting a self-adaptive baseline; and projecting the feature fragments to a fault map, outputting multi-level risk indexes, generating a comprehensive risk score and an anomaly candidate set, and implementing multi-scale threshold judgment and stability test in combination with a baseline to form early warning and disposal suggestions. And performing further attribution analysis on the abnormal candidate set, identifying key components, and reinjecting maintenance feedback to update the atlas and the model to form a continuously optimized knowledge base. According to the method, causal correlation modeling and interpretable diagnosis are realized, and the accuracy and timeliness of early abnormality identification of the AEM electrolysis system are effectively improved.

The invention relates to the technical field of deep well ventilation intelligent control, and discloses a deep well multi-stage intelligent ventilation control method and system.The deep well multi-stage intelligent ventilation control method comprises the steps that a multi-layer mixed causal graph model is constructed; extracting a cross-layer causal relationship, identifying a causal dependency relationship among different level nodes through a mode of combining data mining and expert knowledge, and establishing an inter-level state mapping function; multi-level fault diagnosis is executed, and fault propagation path analysis and root cause positioning are achieved; generating a multi-scale collaborative intervention strategy, comprehensively considering the intervention cost and effect of each level, solving an intervention combination, and passing an intervention conflict detection and reconciliation mechanism; multi-level self-healing control is implemented, and multi-level self-healing control measures are automatically executed; according to the method, fault propagation characteristics among different levels can be accurately captured through multi-level causal reasoning, and compared with a traditional single-level fault diagnosis method, the fault root cause positioning accuracy is improved.

The invention relates to the field of traditional Chinese medicine diagnosis, and discloses a traditional Chinese medicine intelligent diagnosis system based on multi-source data fusion and an AI technology, and the system comprises a tongue picture collection and classification unit which is used for collecting tongue picture data of a patient through imaging equipment, carrying out the feature extraction of the tongue picture data, and obtaining a tongue picture feature data set; performing tongue picture pathological mode classification on the tongue picture feature data set based on a deep convolutional neural network to obtain tongue diagnosis identification result data; and the face diagnosis feature correlation analysis unit is used for inputting the tongue diagnosis identification result data into a multi-modal data fusion engine and carrying out face diagnosis pathological feature correlation analysis based on a graph attention network to obtain face diagnosis identification result data. The deep semantic understanding is realized by introducing a plurality of data acquisition modes such as three-dimensional imaging, thermal imaging, micro-expression recognition, spectral information and pulse condition harmonic analysis and combining advanced models such as a graph neural network, U-Net and Bi-LSTM.