3128 results about "Diagnostic system" patented technology

The invention belongs to the technical field of bridge diagnosis, and discloses a bridge crack intelligent diagnosis system based on multi-modal data fusion. By fusing multi-source data such as visual images, sound wave detection and vibration signals, comprehensive perception and characterization of crack features are realized; constructing a bridge crack characteristic spectrum diagram by adopting a cross-modal feature extraction and heterogeneous feature coding technology; generating a crack evolution situation map based on space-time correlation analysis and knowledge graph construction; the robustness of the system in a complex environment is improved through environmental adaptability feature enhancement and multi-scale characterization; constructing a bridge safety risk hypergraph in combination with multi-dimensional risk analysis and multi-agent collaborative diagnosis; analyzing and revealing a crack evolution mechanism by applying a causal relationship; and finally, through dynamic fusion and uncertainty quantification, a crack intelligent diagnosis comprehensive report is generated. According to the system, the limitation of traditional single-mode diagnosis is broken through, and dynamic prediction and accurate risk assessment of fracture evolution are realized.

The invention discloses an intelligent power distribution network equipment state perception and abnormity diagnosis system, and the system operation process specifically comprises the following steps: collecting the operation state data of power distribution network equipment in real time, carrying out the time-space alignment and feature fusion, and generating an equipment multi-dimensional state vector; inputting a pre-constructed equipment health dynamic baseline model, and outputting a real-time health deviation degree; when the real-time health deviation degree exceeds an early warning deviation threshold value, triggering an abnormal preliminary screening mechanism, and extracting abnormal feature fragments; inputting a multi-stage diagnosis knowledge graph model, and generating an abnormal cause hypothesis set; performing confidence ranking on the abnormal cause hypothesis set, and outputting first # imgabs0 diagnosis results and corresponding confidence weights; and generating an equipment maintenance strategy instruction set according to the diagnosis result. The method has the following advantages and effects: the dynamic baseline is adaptively generated from multi-source data, and a multi-stage diagnosis framework of a physical model, a power grid rule and a historical case is fused, so that the accuracy and timeliness of anomaly diagnosis are finally improved.

The invention discloses a children MPP (Mycoplasma Pneumoniae) auxiliary diagnosis system based on multi-modal time series data modeling, and aims to improve the auxiliary diagnosis, clinical typing and severe prediction capabilities of mycoplasma pneumonia and reduce the workload of doctors. The MPP dynamic representation method based on multi-modal time sequence modeling is innovatively provided, multi-modal data such as images, electronic medical records and inspection indexes are fused, the time sequence feature embedding technology is adopted, and the evolution trend of the disease course can be effectively captured. Furthermore, deep fusion of different modal data is realized by using a progressive cross-modal semantic alignment strategy and a modal attention gating mechanism, and the problem of insufficient diagnosis accuracy caused by incompatibility of data modals in a traditional method is solved.

The invention discloses a smart power grid fault early warning and diagnosis system, and relates to the technical field of smart power grids. Comprising a multi-modal data acquisition and fusion module, a deep reinforcement learning early warning module, a fault diagnosis module based on a graph neural network, an adaptive diagnosis module integrated with multi-task learning, an edge calculation and cloud collaboration module and a system test and verification module, and comprises the following steps: step 1, acquiring multi-source data through a power sensor, an intelligent electric meter and a meteorological station; dimension reduction and feature fusion are carried out on the data by using a depth auto-encoder model; 2, importing the collected data into a deep reinforcement learning early warning module, and dynamically adjusting an early warning strategy through interaction with a power grid operation environment; 3, a fault diagnosis module based on the graph neural network analyzes the topological structure of the power grid and identifies a fault position; 4, learning a model and processing a fault type; 5, edge calculation and cloud cooperative work are combined; and 6, testing, verifying and optimizing the model by the system testing and verifying module.

The invention relates to the technical field of intelligent monitoring, in particular to an intelligent diagnosis system and method for a comprehensive energy station, and the system comprises a dynamic trajectory capture module, a lag response analysis module, a mismatch behavior diagnosis module and a fault chain positioning module. According to the method, a dynamic time warping algorithm is adopted to process sliding window time sequence data and calculate track slope difference, capture precision of instantaneous disturbance characteristics is enhanced, a grey correlation model is utilized to analyze track matching degree and set a dynamic lag threshold value, and a continuous non-convergence condition trigger mechanism is combined to improve hidden fault identification sensitivity. Multi-parameter range calculation and direction consistency check integrate start-stop frequency and load rate change trend, reduce misjudgment risk caused by single parameter fluctuation, construct a time sequence alignment axis and calculate delay response joint probability distribution, realize time-space association of historical data and real-time monitoring, accurately locate a coupling relationship of a multi-source fault chain, and improve the reliability of the multi-source fault chain. The abnormal state early warning capability is enhanced, and the fault positioning efficiency is optimized.

The invention belongs to the technical field of fault prediction and management, and discloses a production line abnormity real-time diagnosis system based on industrial Internet of Things, which comprises a data acquisition and processing module, a distributed sensor network covering key equipment of a production line is constructed, multi-dimensional production line data is acquired, and the data acquisition and processing module is used for acquiring data of the production line; a self-adaptive sampling technology is adopted to dynamically adjust the multi-dimensional production line data acquisition frequency according to the equipment state, and preliminary multi-dimensional production line data processing is executed at the edge end; and the multi-scale time sequence management module adopts a hot, warm and cold three-level hierarchical storage architecture, compulsively switches sampling frequencies of key equipment parameters in combination with a multi-level safety threshold mechanism, performs resource allocation through a hierarchical calculation architecture, and introduces an abnormal sensitive new mode detection and double-track system template updating mechanism to identify a novel abnormal mode. The state change of the equipment is continuously monitored; it is ensured that resources can be efficiently scheduled in normal, early warning and abnormal states, and the anti-risk capacity of the system is improved.

The invention relates to the technical field of power Internet of Things fault diagnosis, and discloses a power Internet of Things abnormal behavior diagnosis system based on large model dynamic optimization. The system comprises a data acquisition module, a feature extraction module, an anomaly detection module, a dynamic optimization module and an early warning response module. The data acquisition module acquires operating parameters of power equipment in an area; the feature extraction module extracts state feature vectors through operation parameters, obtains a deviation coefficient in combination with an anomaly analysis area and the like, fuses risk assessment values to generate an anomaly index, and judges whether deep diagnosis is started or not according to the anomaly index; the anomaly detection module utilizes an attention mechanism model to mine depth features and generate a report, and judges whether to trigger early warning or not in combination with real-time adjustment parameters; the dynamic optimization module guarantees data interaction through an edge computing node, and a standby node is started when a main link is abnormal; and the early warning response module matches an emergency scheme according to the risk level and issues an instruction. According to the system, accurate diagnosis and efficient response of abnormal behaviors of the power Internet of Things can be realized.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties / analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties / analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

A medical image disease diagnostic system that is included in a clinical medical diagnosis workflow. In one embodiment, a user interface is configured to assess a medical imaging application that provides for viewing, analyzing, and annotating of medical images and medical non-image data and medical training data. A processor includes a pre-trained subsystem configured to generate AI pre-trained models based on the medical image and medical non-image data, a training data generation subsystem configured to generate the medical training data, and a medical image model training subsystem configured to transform the medical image and medical non-image data into the medical training data. The medical image model training subsystem is further configured to train or generate the pre-trained AI models based on the medical training data, and a model drift subsystem is configured to determine accuracy of the pre-trained AI models. A diagnostic reporting subsystem configured to generate clinical diagnostic reports.

A diagnostic system and method for identifying and resolving vehicle Controller Area Network (CAN) bus faults is disclosed. The system connects to a vehicle's data link connector (DLC) and automatically retrieves diagnostic trouble codes (DTCs) from multiple electronic control units (ECUs). It filters the DTCs to identify those related to CAN communication errors, groups repeated DTCs across ECUs to highlight systemic faults, and assigns repair priority based on status, scope, and criticality. A user interface displays prioritized DTCs with definitions, affected systems, possible causes, and repair guidance. Upon technician input, the system re-scans the network to confirm resolution and updates the display accordingly. The invention supports semantic and conceptual recognition of communication faults, enabling robust, language-agnostic analysis. This system reduces diagnostic time, improves repair accuracy, and supports multilingual or manufacturer-specific DTC definitions without requiring external infrastructure or advanced user expertise.

The invention relates to the technical field of fluid valve actuator equipment diagnosis, in particular to a fluid valve actuator monitoring and diagnosing system based on AI intelligence. According to the method, the target opening control instruction is generated through fluid medium characteristic data matching, and accurate regulation and control of the fluid valve are achieved; an opening degree change data set and a torque time sequence data set are captured in real time through a deployed sensor group, multi-dimensional dynamic fluctuation analysis is carried out, and intelligent recognition and screening of fluid valve abnormity are achieved; the medium temperature, the pipeline pressure and the medium flow velocity in the dynamic working condition parameters are substituted into a multiple regression linear model to generate reference torque and opening, and dynamic diagnosis of clamping stagnation abnormity is achieved by combining fusion calculation of a torque fluctuation coefficient and an opening deviation coefficient; through the correlation between the opening degree and the torque time sequence data and the traceability analysis of the current dynamic working condition parameters, the accurate positioning of the abnormal source is realized, and a systematic solution is provided for the state diagnosis and reliability evaluation of the fluid valve actuator.

The invention discloses a storage hard disk remote diagnosis system and method based on the Internet of Things, and relates to the technical field of health management of the Internet of Things and storage equipment. The system comprises a data fusion module, a causal analysis module, a risk analysis module and a map construction module. The data fusion module collects SMART parameters, IO operation time sequence data and environment data through Internet of Things equipment, dynamically distributes multi-source data weights by using an attention mechanism, and extracts hard disk health state features. And the causal analysis module is combined with the multi-scale time sequence convolutional network and the Bayesian causal network to identify periodic abnormal fluctuation and generate a fault root cause analysis report. The risk analysis module matches historical cases through federal incremental learning, calculates a hard disk fault risk index and generates an early warning signal. And the atlas construction module optimizes resource isolation, data migration and response paths according to the risk indexes, generates a visual operation and maintenance atlas, provides fault positioning, risk links and repair priorities, and improves the operation and maintenance management efficiency.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties / analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties / analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

The invention relates to the technical field of image processing, in particular to an intelligent fracture diagnosis system based on image recognition, which comprises an image analysis module, a mode recognition module, a form analysis module, a risk assessment module and an auxiliary decision module. According to the method, skeleton gray level distribution and boundary consistency are analyzed through continuous frames of X-ray images, fracture feature extraction precision and time sequence coherence are improved, key point space distribution, symmetry standards and form proportions are fused, fracture area structured quantitative evaluation is achieved, the form change trend and abnormal offset point screening are combined, and the accuracy of fracture feature extraction is improved. The method enhances abnormal trajectory recognition precision, associates bone mineral density and form offset, calibrates high-risk time periods, improves risk assessment perspectiveness and individual adaptability, dynamically corrects output content according to an inter-frame suggestion change trend and extended feedback, enhances timeliness of diagnosis suggestions and closed-loop feedback quality, and improves risk assessment accuracy. Image evolution, structural geometry and physiological data are integrally fused, and multi-dimensional intelligent judgment of fracture recognition and evaluation is achieved.

The invention discloses an intelligent diagnosis system of a power distribution network fault self-judgment switch device, and relates to the technical field of power system automation and intelligent power grids, and the intelligent diagnosis system comprises an intelligent diagnosis system interconnection hub which is in communication connection with a power parameter monitoring module, an intelligent fault detection module and a visual decision support module; the electric power parameter monitoring module monitors the state of a power distribution network in real time through a mutual inductor, voltage, current, power factors and harmonic data are collected through an intelligent electric meter, the intelligent fault detection module achieves fault recognition, trend prediction and anomaly detection through an LSTM neural network, and the decision support module provides a graphical interface and automatically generates a fault response strategy. According to the method, autonomous judgment and quick response of the power distribution network fault are achieved, the accuracy and efficiency of fault detection are remarkably improved, the false alarm rate and the missing report rate are reduced, meanwhile, the decision support module of the graphical interface simplifies the fault processing flow, and the stability and safety of power grid operation are improved.

The invention relates to the technical field of the Internet of Things, and discloses an equipment fault prediction and diagnosis system for the Internet of Things. The system comprises a multi-source data acquisition module which is used for acquiring heterogeneous sensing data of Internet of Things equipment in real time; the data purification module is used for carrying out noise suppression and abnormal value repair on the data and generating a standardized time sequence data stream; the feature enhancement module is used for extracting equipment state features through a multi-scale decomposition algorithm; the fault prediction module is used for constructing an equipment degradation prediction model based on the cascade residual network and generating a dynamic evolution graph of an equipment health index; and the diagnosis decision module is used for generating a fault positioning result and a maintenance strategy optimization instruction through a hybrid inference engine based on the atlas. In addition, the system also comprises an equipment life calibration model, and a prediction model is dynamically adjusted by considering the individual difference of equipment. The system can effectively process heterogeneous data, accurately predict faults, accurately diagnose and optimize a maintenance strategy, and improve the operation reliability and maintenance efficiency of the Internet of Things equipment.

Provided are a method and a system for Medical auxiliary diagnostic for gastritis. The medical auxiliary diagnostic system includes: a sampling system collecting and storing medical images, and including a first dataset and a second dataset; a user device connected to the sampling system, and reading medical images from the sampling system or uploading medical images; and an image analysis system connected to both the sampling system and the user device to analyze medical images based on requests from the user device, thereby generating auxiliary images for assisting in diagnosis. The image analysis system performs deep learning based on the first dataset and the second dataset. Based on the results of the deep learning, the image analysis system performs inferences on a target image to generate auxiliary diagnostic images for assisting in identifying precancerous lesions. Both the first and second datasets include upper gastrointestinal endoscopic images.

The invention relates to the technical field of pile foundation monitoring, and discloses a pile foundation state real-time monitoring and diagnosis system based on a digital twinborn technology. The system comprises a multi-source data acquisition module, a data confidence evaluation module and an acoustic emission monitoring decision module. The multi-source data acquisition module comprises a plurality of sensor groups deployed at different depths of a pile foundation, each group comprises a strain sensor, an acceleration sensor, an acoustic emission sensor and a temperature sensor, and pile foundation data can be acquired in multiple dimensions; the data confidence evaluation module receives original data, generates a correction data sequence through time sequence noise separation and reconstruction, and calculates data confidence according to correction data distribution dispersion; the acoustic emission monitoring decision module judges whether acoustic emission monitoring is started or not according to the data confidence coefficient, and controls the acoustic emission sensor array at the top of the pile foundation to collect acoustic emission signals during starting. The system can comprehensively obtain pile foundation data, improve data accuracy, achieve early damage recognition and guarantee pile foundation safety.

A method for diagnosing diseases in human patients can include providing a set of global X-ray diffraction (XRD) data to a global data center (GDC) and processing the set of global XRD data and categorizing it into data clusters, where each data cluster corresponds to a diagnostic indicator for assessment of a physiological or pathological condition. The method can further include communicating local XRD data and local patient data from a local autonomous cell (LAC) to the GDC, where the local XRD data includes information about a tissue sample measured using a tissue diffractometer of the LAC, and wherein the tissue sample includes skin. The method can further include processing the local XRD data, comparing it with the data clusters to determine a local diagnostic indicator for the local patient, and communicating the local diagnostic indicator to the LAC.

The invention belongs to the field of fault diagnosis, and particularly relates to a main shaft bending common characteristic diagnosis system based on a stacking integration method, and the system comprises the following modules: a sensor module which is used for collecting a vibration signal of a main shaft in real time; the data acquisition and processing module is used for converting an analog signal into a digital signal and preprocessing the signal acquired by the sensor; the feature extraction module is used for extracting features from the preprocessed vibration signals; the machine learning diagnosis module analyzes and classifies the extracted features by using stack integration based on a plurality of trained machine learning models, and judges the health state of the main shaft; and the output module is used for outputting the diagnosis result and sending out an alarm signal when abnormality is detected. Real-time monitoring and automatic diagnosis of the main shaft bending fault are achieved, compared with a traditional method depending on expert experience and manual detection, the fault detection efficiency and accuracy can be greatly improved, and misjudgment and missed judgment risks caused by human factors are reduced.

The invention relates to the technical field of ophthalmology medical diagnosis, and discloses a three-dimensional reconstruction-based fundus focus intelligent labeling method and diagnosis system. The method comprises the following steps: receiving multi-modal image data streams such as fundus color photos, OCT images and FFA images of an ophthalmological patient; performing spatial registration and feature fusion by using a pre-trained lesion feature fusion model to generate a three-dimensional lesion probability distribution diagram and a lesion category confidence matrix; constructing an adaptive annotation threshold model to generate a multi-modal annotation instruction set; based on the focus development chain model, focus development is simulated, and instruction set parameters are optimized and labeled; and iteratively optimizing through a distributed reinforcement learning framework, and outputting the focus labeling action sequence to an ophthalmology diagnosis platform. According to the method, multi-modal image information can be integrated, the diagnosis accuracy and efficiency are improved, personalized diagnosis is realized, resources are reasonably utilized, and powerful support is provided for ophthalmic disease diagnosis.

The invention relates to the technical field of medical diagnosis systems, in particular to an early gonitis diagnosis system and method based on multi-modal deep learning, a data collection module is used for collecting and preprocessing multi-modal data including knee joint X-ray image data, MRI (Magnetic Resonance Imaging) image data and clinical evaluation data; the model construction module constructs a deep learning model by combining a convolutional neural network and a Transform architecture, realizes mapping from multi-modal data to a clinical assessment phenotype label by adopting a feature automatic learning module and a potential relationship mining module, and integrates a Grad-CAM interpretability method for understanding a model prediction basis; diagnostic data are continuously collected to be fed back to the data collection module for model optimization, the convolutional network is good at capturing local image features, the Transform architecture is good at modeling a long-range dependency relationship, and the feature extraction and relationship modeling capabilities of the model are greatly improved through combination of the convolutional network and the Transform architecture.

The invention discloses a thermal power plant powder manufacturing equipment state remote diagnosis system, and relates to the technical field of powder manufacturing equipment state diagnosis, and the system comprises a data collection module which obtains the real-time state information of equipment; the edge calculation module is used for carrying out edge real-time analysis and detecting abnormal conditions; the data processing and analyzing module is used for identifying a fault mode and trend and dynamically adjusting a self-adaptive threshold value; the remote monitoring and diagnosis module is used for displaying equipment state, alarm and fault information; the maintenance and decision support module is used for providing maintenance and repair suggestions; and the security and authority management module is used for setting different levels of user authorities and carrying out data encryption. Through data acquisition and edge calculation, the system can monitor the equipment state in real time, identify and alarm abnormity in time, improve the safety and reliability of equipment operation, and utilize deep learning and time sequence analysis, the system can identify potential fault modes, optimize maintenance decisions, and reduce downtime and maintenance cost.

The invention discloses a power equipment fault diagnosis system based on deep learning and related equipment, and relates to the technical field of power equipment operation and maintenance and intelligent diagnosis. Comprising the steps of data acquisition, data preprocessing, deep learning model training and feature extraction, health scoring and fault diagnosis, fault data generation and enhancement, federal learning model optimization, online diagnosis and early warning and the like. Equipment operation data is collected through a multi-modal sensor, data features are extracted by using a deep learning model and fault analysis is carried out, a fault sample set is expanded in combination with a generative adversarial network, and the accuracy and robustness of diagnosis are improved; meanwhile, a global model is optimized through a federated learning framework, data privacy is protected, and distributed monitoring and diagnosis of equipment are achieved; the method can accurately identify the fault type, dynamically evaluate the health condition of the equipment and quickly generate an alarm signal and a maintenance suggestion, and is widely applied to intelligent operation and maintenance scenes of the power equipment.

The invention discloses a weld quality intelligent diagnosis system based on deep learning, and relates to the technical field of welding quality detection, and the weld quality intelligent diagnosis system comprises an image quality evaluation module, a feature alignment module, a deviation detection module, a path reconstruction module, a prior enhancement module and a defect identification module, identifying an area of which the signal-to-noise ratio is lower than a preset threshold value, and constructing a noise interference distribution diagram; and the feature alignment module executes a deformable convolution feature alignment operation with a confidence factor adjustment mechanism based on the noise interference distribution diagram to generate an initial space mapping result. Through mechanisms such as image quality perception, robust alignment, deviation detection, self-adaptive reconstruction and prior enhancement, a closed-loop weld joint intelligent diagnosis process is constructed, false alignment errors are effectively inhibited, the multi-modal fusion stability and the defect recognition precision are improved, and the reliability and the intelligent level of the system under complex working conditions are enhanced.

The invention relates to the technical field of electric power system fault risk diagnosis, and particularly discloses an electric power system fault risk diagnosis method and system, and the electric power system fault risk diagnosis method is implemented based on an electric power system fault risk diagnosis system by building the electric power system fault risk diagnosis system. A time-space dynamic model based on time change is established, node states and line states obtained in real time serve as input, the state of the system at the next moment is predicted through the time-space dynamic model, and then multi-dimensional analysis is conducted on system faults. And finally, the intelligent decision-making and feedback adjusting unit calculates the fault risk probability of the power system, discriminates and accurately compares and divides fault levels, so that the problem that a traditional risk diagnosis model lacks general mathematical modeling for different disaster situations and is difficult to comprehensively reflect multiple influences of disasters on the power system is solved.

The invention provides an oral disease early screening system and screening method based on an artificial intelligence auxiliary diagnosis system. According to the system, oral images and medical information of a patient are collected through a screening tool, an AI model is trained for auxiliary diagnosis, and intelligent services are provided for doctors in the diagnosis system. Artificial intelligence is introduced into oral medicine, the oral disease diagnosis efficiency and accuracy can be remarkably improved, support is provided for intelligent medical treatment, remote medical treatment and the like, and good application prospects are achieved. The method is suitable for the cross field of oral medicine and artificial intelligence, can be applied to hospitals, physical examination institutions and the like, and realizes early screening and early diagnosis of oral diseases.

The invention relates to the technical field of equipment fault diagnosis, in particular to an intelligent equipment fault diagnosis system based on a knowledge graph and deep learning, which comprises a data acquisition module, a knowledge graph construction module, a deep learning reasoning module, a diagnosis output module and a feedback optimization module. According to the system, real-time reflection of the equipment operation state is realized through multi-source heterogeneous data acquisition and knowledge graph dynamic modeling, and the fault recognition capability is improved in combination with a double-branch deep learning network and an attention mechanism. A diagnosis result is displayed in a graphical interface, and closed-loop optimization is supported. The device state change can be comprehensively captured, the complex working condition adaptability is enhanced, and the diagnosis accuracy and the intelligent level are improved.

The invention discloses a current transformer real-time state diagnosis system based on Internet of Things, which relates to the technical field of electrical equipment state diagnosis and comprises a reference construction module, a disturbance excitation module, a modeling identification module, a driving unwrapping module, a self-adaptive setting module and a closed-loop control module, in the operation process of a secondary winding loop of the current transformer, a nanosecond time synchronization reference is established, a phase reference baseline is locked, current vector trajectories are continuously collected based on the time synchronization reference and the phase reference baseline, and a discrete phase residual image is constructed. According to the method, through high-precision time synchronization, phase disturbance excitation, sparse modeling identification, driving unwrapping and self-adaptive setting control, hysteresis oscillation of the secondary circuit of the current transformer is accurately identified and suppressed, the fault diagnosis accuracy and the system stability are remarkably improved, and the limitation of a traditional method in the aspects of identification sensitivity and response capability is broken through.

The invention provides a medical diagnosis system based on chain reasoning and multi-modal fusion and application thereof, and belongs to the technical field of medical care informatics. The system comprises a data preprocessing module, a data fusion module, a model reasoning module and an output module, and can perform deep processing and intelligent analysis on multi-modal medical data (including text data, medical image data and structured data) of a patient. And the output module provides a diagnosis result and a detailed reasoning process, so that the interpretability and the credibility of the diagnosis result are improved. Through multi-modal data fusion, a chained reasoning mechanism and optimized loss function design, the accuracy, generalization ability and interpretability of disease diagnosis are remarkably improved, the limitation of the traditional technology in the aspects of single-modal analysis and black box reasoning is broken through, transparent, efficient and intelligent diagnosis decision support is provided for clinicians, and the clinical application prospect is wide. And intelligent and precise development of medical diagnosis is promoted.