2420 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 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.

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.

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.

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 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 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 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 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 intelligent inspection and diagnosis system, method and device for a photovoltaic power station unmanned aerial vehicle and a medium, and the system comprises a heterogeneous perception fusion module which is used for constructing digital mirror image mapping of a power station global physical field through multi-mode sensor space-time coding and electromagnetic fingerprint matching; the group intelligent decision module is used for realizing autonomous task negotiation and anti-fragile path evolution of a multi-unmanned aerial vehicle cluster based on a dynamic Bayesian game model; the embedded diagnostic kernel module is used for running a quantization feature extraction algorithm at an edge computing node to realize component-level defect subsurface-level diagnostic reasoning; the superbody self-evolution module is used for continuously reconstructing a diagnostic knowledge graph by means of a genetic programming framework to realize Darwin asymptotic optimization of a system cognitive architecture; the problems that in unmanned aerial vehicle inspection, fault type judgment and accurate geographic positioning cannot be conducted online, photovoltaic array arrangement changes and sudden shielding scenes cannot be self-adapted, and transmission bandwidth limitation and analysis lag are likely to happen in a 5G weak coverage area are solved.

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 medical diagnosis processing, and discloses an intelligent disease diagnosis and differential diagnosis system based on a knowledge graph, and the system comprises a data input module which is used for receiving and standardizing clinical symptoms, signs, laboratory examination data and historical medical record data of a patient; the knowledge graph construction and updating module is used for constructing and updating a knowledge graph of diseases and symptoms according to the medical literature and the clinical data, and the knowledge graph automatically extracts an incidence relation between the symptoms and the diseases from the medical literature through a natural language processing technology; and the reasoning and diagnosis module is used for performing intelligent disease diagnosis and differential diagnosis. According to the method, by optimizing reasoning path selection and information gain calculation, the path with the most information content is selected from multiple reasoning paths for diagnosis reasoning, and the most representative path is selected by calculating the correlation degree between each symptom and the disease.

The invention provides a power consumption behavior diagnosis system and method based on an anomaly detection model, and relates to the technical field of power system anomaly diagnosis, and the system comprises a data collection module which is used for collecting the data of the power consumption behavior of a user; the data preprocessing module is used for obtaining basic time sequence data; the feature extraction module is used for extracting multi-dimensional features; the feature selection module is used for screening the multi-dimensional features by adopting a feature screening function to obtain a screened feature set; the model processing module is used for inputting the screened feature set into a pre-trained anomaly detection model to generate a reconstructed feature sequence; the anomaly recognition module is used for calculating an anomaly score; and the diagnosis output module is used for outputting the abnormal level and generating an abnormal diagnosis report of the power consumption behavior of the user. The method can effectively solve the problems that in the prior art, the modeling capacity of complex multi-dimensional time series data is insufficient, feature extraction and selection depend on manual work, information loss is likely to be caused, and the anomaly detection precision and the calculation efficiency are difficult to balance.

The invention discloses a construction method of a dyskinesia phenotype classification model of a Parkinson's disease patient and a diagnosis system, and belongs to the field of medical auxiliary diagnosis devices. The system comprises an image acquisition module and a phenotype classification module, wherein the classification module is composed of a space-time diagram convolution feature extraction module, a hierarchical node fusion module, a functional brain network construction and feature extraction module, a local brain region feature screening module, a hierarchical brain feature pairing fusion module and an output module. According to the method, multi-modal brain image data are fused, space-time and complex relation characteristics of a brain region are deeply mined, a key lesion brain region is positioned, and dyskinesia phenotypes are accurately classified by training a neural network. The system can significantly improve the diagnosis accuracy of the Parkinson's disease dyskinesia phenotype, has good expansibility and adaptability, and provides a scientific basis for early diagnosis and personalized intervention. The invention further relates to an operation method of the system, an auxiliary diagnosis device and a computer readable storage medium.