1830 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 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.

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 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 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.

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.

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 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 gas turbine exhaust temperature sensor fault diagnosis system and method, and relates to the technical field of industrial equipment state monitoring and diagnosis. The system comprises a sensor signal acquisition module for acquiring an original temperature signal; the signal preprocessing module is used for acquiring and preprocessing original signals and working condition parameters; the feature extraction and windowing module is used for acquiring data and calculating features; the data driving diagnosis module is used for receiving the time sequence characteristics and evaluating the health state; and the decision fusion module is used for comprehensively analyzing the multi-source information and making a final fault diagnosis judgment. According to the system, the accuracy and the reliability of fault diagnosis can be remarkably improved, the false alarm rate and the missing report rate are reduced, early warning and accurate identification of early weak faults of the sensor are realized, the adaptability and the robustness of the diagnosis system to variable working conditions of the gas turbine are improved, and the safety, the economical efficiency and the operation and maintenance efficiency of operation of the gas turbine are improved.

The invention discloses an oral tooth lesion AI auxiliary diagnosis system, which belongs to the field of artificial intelligence and comprises an image acquisition module, a three-dimensional modeling module, a lesion marking module, a dual-channel feature extraction module, a cross-modal diagnosis reasoning module, a lesion evolution trend prediction module and a dynamic risk level generation module. The image acquisition module utilizes multi-frequency structured light and a polarization camera to cooperatively acquire oral images; the three-dimensional modeling module is used for reconstructing an upper and lower jaw three-dimensional structure based on edge constraint splicing point clouds and registering images to form double-view fusion data; the lesion labeling module fuses expert labeling and weak supervision pseudo labels to generate joint labels; the dual-channel module extracts skeleton and texture features; the reasoning module realizes cross-modal semantic coupling through an image-text co-occurrence graph; the evolution prediction module models a lesion change path based on the time reversal causal network; and the risk module outputs a five-level risk and re-injects the embedded vector to strengthen prediction. The beneficial effects are that diagnosis intelligence and clinical decision support level are obviously improved.

Systems, methods, and apparatus are disclosed for omnimodal sensing, data fusion, and autonomous decision-making across physical and digital domains and further integrates a Multimodal Diagnostic System (MDS) and Impairment Recognition and Intervention System (IRIS) with defense architecture or a system architecture that can be compliant with the Modular Open Systems Approach (MOSA) and Sensor Open Systems Architecture (SOSA) to ensure interoperability. The system can utilize real-time multisensory fusion, cryptographic provenance via blockchain, and resilient magnetoelectric communication to support mission-critical decision-making across manned and unmanned platforms in denied or contested environments.

The invention provides a fan gearbox dynamic integration fault detection method and system based on feature fusion, and relates to the technical field of data processing, and the method comprises the steps: obtaining each preliminary feature set; performing feature-to-feature and feature-fault nonlinear relation quantization on each preliminary feature set to obtain each preliminary screening feature set; analyzing the contribution degree of each feature in each preliminary screening feature set, executing feature fine screening, and generating each fine screening feature set; and calling a multi-agent integrated fault detection system, executing multi-layer agent information sharing and collaborative decision from bottom to top based on each fine screening feature set, and generating fan gearbox fault detection information. According to the method and the device, the technical problem of low fault detection accuracy caused by lack of deep feature mining and dependence on a single-machine diagnosis system in the prior art is solved, and the technical effect of improving the fault detection accuracy is achieved by constructing the multi-agent integrated fault detection system, so that the false alarm rate and the missing report rate are remarkably reduced.

The invention discloses a garlic disease and insect pest dynamic diagnosis system based on temperature and humidity time sequence data, relates to the technical field of agricultural information processing, and solves the problems that in the prior art, a fixed parameter model is prone to sensitivity sudden drop, false alarm sudden increase and systematic deviation under sudden or semi-sudden changes of field environment and management variables. According to the scheme, semantic processing of temperature and humidity and farming events is carried out through an acquisition module, a system coupling module carries out segmented identification on distribution mutation caused by environment and operation, a domain representation module constructs causal threatening features, a prediction calibration module carries out dual-path drift decomposition and rapid correction, and a prediction result is obtained. The sample adding and label collecting module generates anti-fact samples and actively collects labels, and the decision attribution module outputs a structured evidence chain; according to the method, the dynamic adaptive capacity and reliability of the diagnosis system under the conditions of non-stationary distribution and concept drift are remarkably improved.

The invention provides an L1-L4 lumbar vertebra segment-oriented multi-feature fusion diagnosis system and method, and the system comprises an image preprocessing module which is used for receiving a lumbar vertebra CT image sequence of a patient; a centrum anatomy partition module; the multi-dimensional image feature extraction module is used for extracting four types of quantitative features from each sub-region; the clinical multi-modal data coding module is used for independently acquiring and processing three types of clinical data: a multi-modal graph attention fusion network; and the segment-level diagnosis output module outputs diagnosis results of three levels. Through a parallel processing architecture and an optimized feature extraction algorithm, the whole diagnosis process only needs 45 seconds from data input to report generation, time is saved compared with manual film reading, and the consistency of diagnosis results is remarkably improved.

The invention provides a traditional Chinese medicine intelligent health diagnosis system and method based on multi-source data fusion, and relates to the technical field of health diagnosis. The system comprises a database construction module which is used for collecting condition data and historical disease data of a patient and constructing a traditional Chinese medicine holographic health database. And the multi-dimensional classification module is used for establishing a multi-source data classification standard and adding category fields to the multi-source data in the traditional Chinese medicine holographic health database from multiple dimensions to obtain associated field data. The importance classification module is used for constructing an importance degree judgment standard and judging the importance degree of different associated field data to obtain importance labels, and the exclusive customization module is used for classifying health states according to different importance labels to obtain current health data and formulating exclusive treatment strategies. According to the method, the data are classified and associated from multiple dimensions, the organization and utilization efficiency of the data is improved, and the importance degree judgment method can provide a basis for priority processing and analysis of the data.

The invention relates to the technical field of power equipment monitoring and diagnosis, in particular to a GIS partial discharge high-sensitivity monitoring and microdefect diagnosis system, which comprises a signal acquisition module, a signal processing module, a defect identification module and a data fusion module, wherein the signal acquisition module is used for acquiring partial discharge signals and related environment data in real time; the signal processing module is used for carrying out noise suppression and filtering processing on the partial discharge signals from the signal acquisition module and extracting feature data of the partial discharge signals; the defect identification module is used for diagnosing potential micro-defects and performing defect level evaluation; the data fusion module is used for generating a multi-dimensional equipment health condition report; according to the invention, through multi-dimensional data fusion and accurate signal processing and defect evaluation, the diagnosis precision of the partial discharge signal of the GIS equipment is improved, and comprehensive evaluation and fault early warning of the health state of the equipment are realized.

The invention relates to the technical field of industrial control, and discloses a dry-wet combined cooling tower operation state prediction and diagnosis system comprising a load prediction module used for obtaining production plan data and environment information, constructing a basic heat load prediction model, and outputting a total prediction heat load; the thermal modeling module is used for establishing a heat transfer model and an energy consumption model, the heat transfer model outputs cooling amounts in different operation modes, and the energy consumption model outputs total predicted power; the dry-wet decision module is used for constructing a multi-objective optimization function and solving the multi-objective optimization function to obtain a dry-wet switching strategy; and the control execution module is used for executing the dry-wet switching strategy. According to the method, the basic thermal load prediction model is established, so that the prediction precision of the thermal load is improved, and differentiated cooling strategies are provided for different production stages; on the basis of real-time load requirements and environmental conditions, a dry mode or a wet mode is intelligently selected for operation, and a multi-objective optimization function is constructed, so that the balance between cooling requirement meeting and system energy consumption minimization is realized.

The invention provides an industrial process intelligent fault diagnosis method based on multi-scale depth separable convolution and cross attention fusion. The method comprises the following steps: preprocessing monitoring data to obtain an input feature map; parallel multi-scale two-dimensional depth separable convolution operation is adopted to carry out multi-scale feature extraction on the input feature map to obtain a multi-scale local space-time feature map; obtaining a multi-scale enhanced feature map through multi-scale channel attention; fusing the multi-scale enhanced feature map through a cross-scale cross self-attention mechanism; capturing a global long-distance time sequence dependency relationship in the fusion feature map in the industrial process through a Transform encoder; and outputting a fault classification result according to the feature vector of the global context. The invention further provides an industrial process intelligent fault diagnosis system. The method is used for solving the technical problems that feature extraction is insufficient, a multi-source information fusion mechanism is static and poor in adaptability, and global dependence modeling is insufficient in an existing method.

The invention discloses an AI fault prediction and diagnosis system and method based on a numerical control machine tool, and belongs to the technical field of fault diagnosis. The technical problem that efficient fault early warning and health management cannot be implemented in the full life cycle of a numerical control machine tool in an existing scheme is solved. Monitoring data covering the full life cycle and multiple working conditions of the numerical control machine tool, and providing high-quality annotation data for subsequent model training through association of a high-dimensional feature matrix and a fault tag; an improved variational mode decomposition algorithm is utilized, fault information of each mode is quantified in combination with wavelet packet energy entropy, time migration of multi-sensor data is eliminated through space-time alignment, weights of different features are adaptively distributed by utilizing an attention mechanism, and discrimination of fusion feature vectors is effectively enhanced; a long-term dependency relationship of a feature sequence is captured based on a bidirectional gating circulation unit, a convolutional neural network is improved to reinforce local detail features, and the fitting capability of a model to a complex fault mode can be effectively improved through cooperation of the two.

The invention discloses a high-low voltage linkage line loss comprehensive intelligent diagnosis system and method. The system comprises a distributed storage and high-performance calculation module, a data fusion and processing module, a multi-dimensional feature construction module, a high-low voltage linkage analysis module and a line loss intelligent diagnosis module. The method is used for carrying out line loss comprehensive intelligent diagnosis based on the system, and comprises the following steps: processing multi-source data in real time through the distributed storage and high-performance calculation module; bus-line-user archive data are fused, and a line loss index is calculated; extracting time, space and electrical three-dimensional characteristic indexes; performing high-low voltage linkage analysis based on the Pearson's correlation coefficient and the DTW distance; and fusing the system state model and the isolated forest anomaly detection model to output an anomaly diagnosis result. The method is suitable for accurate analysis and treatment of line loss in an intelligent power grid environment, and the efficiency and quality of line loss management can be improved.

The invention discloses a power equipment fault diagnosis system and method based on edge cloud cooperation. The system comprises an edge end module and a cloud end module. The edge end module is deployed in a power equipment site, is embedded with a lightweight diagnosis model, collects power equipment operation state parameters in real time, executes localized preliminary fault identification and alarm judgment, extracts key characteristic quantities, and uploads a processing result to the cloud end module through a network communication protocol; the cloud module is deployed in a data center or a control platform, and performs complex model reasoning, cross-device historical data comparison, fault depth research and judgment and model updating and distribution according to the characteristic quantity data uploaded by the edge module; the edge end module and the cloud end module perform data interaction through an FTP, MQTT or 5G protocol, a model trained by the cloud end can be automatically distributed to an edge end to realize rapid deployment and switching, and equipment fault diagnosis is realized. According to the invention, through a task division cooperation and data interaction mechanism, an efficient and low-delay fault diagnosis process is realized.

The invention discloses an acute abdominal pain cause differential diagnosis system based on deep learning, and relates to the technical field of medical artificial intelligence, comprising: a data acquisition module acquires initial self-described text data of a patient; the standardization processing module generates a standardized symptom set through standardization processing; the cause reasoning module performs reasoning according to the standardized symptom set and the medical knowledge graph to obtain a suspected disease set; the contradiction detection module is used for detecting the contradiction between the symptom set and the self-contained text and calculating the contradiction intensity and disease cause relevancy; the priority calculation module ranks contradiction priorities according to contradiction intensity and disease cause relevancy; the contradiction processing module judges the contradiction significance score, if the contradiction significance score is lower than a threshold value, a standardized symptom set is output, and otherwise, an optimal clarification action is generated to solve the contradiction; an iteration updating module updates a symptom set according to clarification action feedback, and causes are inferred again until a termination condition is met; the method can ensure that the pathogenesis reasoning process gradually approaches the real pathogenesis, and improves the diagnosis reliability.

The invention discloses a secondary equipment health diagnosis system and method based on multi-source data, and relates to the technical field of secondary equipment monitoring, and the system comprises a data collection module which is used for obtaining multi-source data based on a standard communication protocol, and carrying out the hierarchical collection according to a priority order; the data processing module is used for acquiring the processed standardized data and extracting electrical quantity transient characteristics through wavelet transform; the historical database module is used for establishing a historical data sample storage system and providing multi-source data set samples for model training; the equipment health diagnosis module is used for carrying out periodic prediction by utilizing multi-dimensional equipment feature differentiation fitting and combining a long-short-term memory network model, and correcting to obtain a real equipment health index; and the early warning and decision module is used for analyzing and positioning potential fault elements and generating a maintenance strategy. The method has the advantages of multi-source data real-time grading collection, intelligent feature extraction and health state quantitative evaluation.

The invention relates to the technical field of power equipment fault diagnosis, in particular to a transformer overheating fault intelligent diagnosis system based on a physical information neural network. According to the invention, physical constraint filtering is carried out on signals through the adaptive noise suppression module; a loss function fusing a heat conduction equation, a gas decomposition kinetic equation and an insulation aging kinetic equation is constructed through a physical information neural network, and coupling modeling of temperature and gas and collaborative prediction of the aging state of insulation paper are achieved; spatial topological features and time sequence features are extracted through a multi-modal feature fusion module, and cross-modal association is established by adopting an attention mechanism; predicting a future feature trajectory through a fault evolution prediction module and outputting hierarchical early warning; and finally, outputting fault types, severity and maintenance suggestions. According to the method, a physical mechanism and data intelligence are deeply fused, advanced early warning and accurate positioning of an overheat fault are realized, and meanwhile, the blank that insulation failure cannot be pre-judged in the prior art is filled.

The invention discloses an intelligent elevator host bearing fault diagnosis system based on AI and fault physical fusion, and belongs to the technical field of elevator diagnosis. Comprising an edge end which is connected with a cloud end; when fault diagnosis is carried out, the edge end firstly collects a high-frequency signal through a sensor, sequentially executes signal preprocessing, feature extraction, anomaly detection and data compression, and finally sends compressed feature data to the cloud end; after the cloud end receives and verifies the integrity of the data, the feature data is stored in a time series database, deep analysis is executed through a data analysis engine, a lightweight model is optimized based on historical data, an analysis result is displayed through a remote monitoring platform, and an alarm notification is sent when abnormity is detected; meanwhile, the cloud end issues the optimized lightweight model to the edge end to form a continuously optimized closed loop. Through the feature extraction and data compression technology of the edge end, the data volume needing to be transmitted is greatly reduced, and the network bandwidth requirement and the transmission delay are remarkably reduced.

The invention relates to the technical field of wind power generation, in particular to a wind turbine generator health state diagnosis system and method. The system comprises a monitoring module used for acquiring multi-source data by using a preset sensor group; the calculation module is used for receiving the multi-source data and performing feature extraction operation on the multi-source data to obtain multi-source feature information; and the analysis module is used for fusing the multi-source feature information and evaluating the health state of the generator set based on a fusion result. Therefore, through the generator set health state diagnosis system, multi-sensor fusion, edge calculation and cloud intelligent analysis are integrated, the problem that the prior art is lack of system-level health state evaluation capability and intelligent diagnosis capability is solved, and real-time monitoring and early fault early warning of the health state of the whole wind turbine generator system are realized.

The invention relates to the technical field of power system monitoring and fault diagnosis, and particularly discloses a fault diagnosis system of an electrical variable measurement insulator detection device. The system comprises a synchronous acquisition module, a multi-physical field feature extraction module, a self-adaptive fault diagnosis engine and a feedback module. By synchronously acquiring voltage harmonic waves, leakage current and temperature data, extracting feature vectors of coupling electric-thermal influence and performing two-stage collaborative diagnosis by using a dynamic threshold value and a mahalanobis distance, high-sensitivity and self-adaptive accurate identification and early warning of an early latent fault of the insulator are realized. According to the system, by constructing a synchronous data acquisition module and a multi-physical field feature extraction module, two key influence factors, namely an operation voltage harmonic component and an environment temperature, are brought into a diagnosis system in a quantifiable feature form for the first time.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an artificial intelligence medical diagnosis system based on multi-dimensional information fusion. Comprising the steps that a self-adaptive diagnosis path planning module judges whether a user request belongs to a preset non-diagnosis and treatment category or not, if yes, a quick response path is activated, and a standardized answer is retrieved and returned to a user; the preliminary diagnosis module generates a candidate disease hypothesis list, verifies the candidate disease hypothesis list and outputs a verified disease hypothesis list; the dynamic knowledge enhancement module generates missing knowledge according to the disease knowledge graph and the query verification disease hypothesis list and supplements the missing knowledge into the medical knowledge graph; a composite confidence evaluation module performs confidence evaluation on each hypothesis disease in the verification disease hypothesis list, and outputs a final disease confidence; the result integration module sorts the final disease confidence in a descending order and integrates the multi-dimensional information of each hypothetical disease to generate a structured differential diagnosis report; the system and the method can assist doctors in realizing high-accuracy, high-reliability and explainable intelligent medical diagnosis.

The invention discloses a thermal control instrument fault self-diagnosis system based on multi-source data fusion, and relates to the technical field of prediction and health management. The thermal control instrument fault self-diagnosis system based on multi-source data fusion comprises the following steps: a data collection and arrangement module used for collecting thermal control state data in real time and preprocessing the thermal control state data; the feature construction and extraction module is used for performing thermal deviation anomaly judgment on the preprocessed thermal control state data; the state intelligent evaluation module is used for performing temperature trend comparison on the thermal control state data after the thermal deviation abnormity judgment; the diagnosis linkage control module is used for fusing thermal deviation abnormity judgment and a temperature trend comparison result to execute control and participate in adjustment; and the result displaying and filing module is used for sorting and recording the measuring point diagnosis result and the processing state. The problems that false alarm is caused by wall temperature thermocouple signal drifting and abrupt change, fusion analysis of adjacent measuring points and operation states is lacked, and real overheating and instrument faults are difficult to distinguish are solved.