236 results about "Diagnostic analysis" patented technology

The invention provides a circuit breaker action coil current characteristic diagnosis and analysis system and method. Belongs to the technical field of power system automation and substation state monitoring. The method comprises the following steps: carrying out synchronous wave recording on current of a plurality of circuit breaker actuating coils on a direct current feeder screen side to generate multi-channel synchronous wave recording data; meanwhile, SOE message information is collected, a circuit breaker with specific action is automatically matched and positioned by utilizing a timestamp and action identification information in an SOE message, and action circuit breaker positioning data is generated; the current waveform data of the circuit breaker actuating coil are collected in real time, and a high-precision algorithm is used for feature extraction and analysis, so that abnormal fluctuation in current change can be accurately captured, such as overlarge starting current and unstable holding current, the risk of misoperation or rejection of operation of the circuit breaker caused by abnormal current characteristics is reduced, and the service life of the circuit breaker is prolonged. Electric power system accidents caused by equipment faults are avoided, and the operation reliability of the circuit breaker is improved.

The invention relates to the technical field of power system power distribution network fault detection, in particular to an arc fault diagnosis and analysis method based on an artificial intelligence algorithm, and the method comprises the four steps: synchronous data collection and topological excitation, deployment of terminals at transformer area nodes, injection of characteristic current, and synchronous collection of response waveforms; signal preprocessing: separating a key frequency band through double-digital band-pass filtering, and calculating energy ratio and other enhanced fault features; performing multi-source feature fusion and intelligent diagnosis, constructing a vector containing statistical features, time domain distortion features and topology identification, and inputting a gradient boosting decision tree and deep neural network hybrid model to obtain fault confidence and type; and based on fault positioning and verification of topology, scheduling multi-node cooperative monitoring, and determining a fault point in combination with a topological relation. The method improves the data reliability and diagnosis precision, achieves the precise positioning of a fault, and guarantees the safe operation of a power distribution network.

The invention relates to the technical field of report optimization, in particular to a user medical history-based physical examination report conclusion priority adjustment method, which comprises the following steps of: acquiring multi-source data, and preprocessing the multi-source data to obtain a structured multi-source data set; based on the structured multi-source data set, mining an association relationship between health data and diseases through association analysis, constructing a health trend prediction model in combination with a deep Q network, and obtaining a multi-dimensional diagnosis analysis result; determining a diagnosis conclusion priority in combination with a multi-dimensional diagnosis analysis result and a priority calculation formula; calculating a comprehensive risk score through the data fusion score, and dividing risk levels according to the comprehensive risk score to match corresponding stage intervention strategies; and inputting a multi-dimensional diagnosis analysis result, intervention strategy execution feedback data and user health preference into a comprehensive diagnosis decision model, and obtaining a differentiated health association strategy in combination with a diagnosis conclusion priority. According to the scheme, the severity of the disease is accurately identified by dynamically adjusting the priority.

The invention particularly relates to an engineering wire harness testing method and system, and relates to the technical field of engineering wire harness testing. A measurement and self-calibration module; a test channel health degree real-time monitoring module; and a data processing and diagnosis analysis module. According to the invention, the standard gold wire harness and an automatic self-calibration mechanism solve the problems of long period and low efficiency of traditional manual calibration; the system can automatically trigger self-calibration according to multiple conditions such as time, events and the like, the measurement deviation is captured through multi-dimensional reference measurement, real-time deviation compensation is achieved by means of software closed-loop calibration, meanwhile, fault components can be accurately positioned according to hardware deviation, a maintenance report can be generated, and reference drift caused by environment fluctuation and component aging in a calibration interval is avoided.

The invention belongs to the technical field of medical diagnosis, and discloses a quantum multicolor immune digital pathological diagnosis analysis system, which comprises the following steps: acquiring high-resolution digital images of an immunohistochemical or immunofluorescence slice and a slide, and analyzing multicolor signal distribution in the images to obtain a quantum chromatographic image set; pixel-level color separation and intensity quantization are carried out on the quantum chromatographic image set, and a quantum intensity index set of each pixel is generated; performing multi-target color overlay analysis and post-translational modification protein specificity evaluation on the quantum intensity index set to construct a multi-color modification index set; identifying a single cell boundary and a multi-cell community based on the multicolor modification index set, and extracting a cell morphological parameter and a spatial adjacency relation to obtain a cell quantum analysis set; performing consistency correction and traceable recording by applying an automatic quality control mechanism, and generating a digital diagnosis report; the accuracy of accurate qualitative and quantitative analysis of immunohistochemistry and immunofluorescence is greatly improved.

The invention relates to the technical field of electric energy metering anomaly detection and diagnosis analysis, in particular to a dynamic return and compensation method and system based on an electric energy metering data restoration model, and the method designs a distortion traceability algorithm of multi-dimensional feature fusion and improved SVM (Support Vector Machine), and extracts the characteristics of load features, environment features, packet loss rate, sampling delay distortion and the like. And load fluctuation-dominated distortion, environment interference-dominated distortion and coupling-dominated distortion can be accurately positioned. For multi-source distortion restoration, a load self-adaption-environment compensation-coupling correction three-level restoration model is provided, and through differential compensation of different load types, dynamic calibration of environment parameters such as temperature and humidity, electromagnetic interference and the like and coupling coefficient matrix optimization, the measurement data restoration precision is stably maintained to be 98% or above. According to the invention, through the research on the metering data distortion traceability-coupling distortion data reduction-dynamic return and supplement calculation-return and supplement result closed-loop optimization method, the precise repair of metering errors and the scientific calculation of electric quantity return and supplement in a complex scene are realized.

The invention belongs to the technical field of heat supply equipment diagnosis, and discloses an equipment diagnosis analysis system based on a performance attenuation characteristic curve. The method comprises the following steps: a sensor network acquisition layer, a data governance layer, a curve construction module layer, a dynamic calibration layer and a prediction output layer are included; the sensor network acquisition layer is responsible for comprehensively acquiring operation state data of heat supply equipment and constructing an initial data set by using the data in a stable operation period of the first 7-10 days after the equipment is put into production; and the data governance layer is used for processing and standardizing the collected state data and the original data set to construct a high-dimensional original feature set. According to the method, the equipment performance attenuation characteristic curve is generated through the health score and the curve generation model, the time-varying trend of the performance, the attenuation rate and possibly existing stage characteristics are clearly displayed, the problems of threshold comparison and statistical analysis are solved, and meanwhile, the remaining service life of the equipment can be dynamically predicted.

The invention relates to the technical field of image data processing, and discloses an intelligent drawing auditing method and device based on a vector atlas and an active perception closed loop, and the method comprises the steps: obtaining a to-be-audited engineering drawing file and a compliance specification text; analyzing vector data of the engineering drawing file, and converting the vector data into an attribute graph used for representing a topological structure and semantics of the drawing; generating an audit instruction through a multi-modal large language model planner based on the attribute graph and the compliance specification text; executing the auditing instruction on the attribute graph through a graph signal processing verifier so as to carry out iterative compliance verification and diagnostic analysis on the attribute graph, and generating a corresponding compliance verification result and a diagnostic result; and generating a compliance audit report containing the traceable evidence based on the compliance verification result and the diagnosis result. According to the method, the accuracy and the reliability of the auditing result can be improved while automatic auditing of the engineering drawing is realized.

The invention discloses a fault analysis system and method for ship equipment, and the system comprises a multi-source sensing synchronization module which is configured to carry out the phase synchronization of the operation data of multiple equipment through a hardware phase locking mechanism; the multi-physical field modeling module is coupled to the multi-source sensing synchronization module, and is configured to construct a device virtual state field based on the multi-device operation data after phase synchronization, and generate a dynamic parameter representing a multi-device state time sequence imbalance degree; the adaptive diagnostic analysis module comprises a deep learning fault analysis model, and recessive feature extraction parameters and fault evolution time sequence modeling parameters of the deep learning fault analysis model are adaptively adjusted in real time according to dynamic parameters so as to output system-level fault characterization parameters; and the predictive decision-making module is configured to generate decision-making information containing fault development trend prediction and a preventive maintenance window based on the system-level fault characterization parameters. And high-precision correlation diagnosis and predictive maintenance decision-making of early faults of multiple devices of the ship can be realized.

The invention discloses a multifunctional cardiovascular monitoring system which comprises an electrocardiosignal acquisition module, a conduction block feature extraction module, an arrhythmia classification processing module, a cardiac function parameter calculation module, an intelligent diagnosis analysis module and a monitoring data output module. The electrocardiosignal acquisition module acquires and transmits original signals, the conduction block feature extraction module extracts feature parameters by using an exclusive algorithm, the arrhythmia classification processing module obtains classification results through a multi-dimensional classifier, and the cardiac function parameter calculation module generates calibration parameters by using a dynamic calibration model in combination with the classification results. The intelligent diagnosis analysis module generates diagnosis data through comprehensive analysis of the auxiliary platform, and the monitoring data output module processes the data and then outputs the data. The system realizes module cooperation and data interaction, improves the monitoring efficiency, guarantees data accuracy by using a professional algorithm and a model, and provides reliable support for clinical cardiovascular monitoring.

A system and method for distinguishing the type of seizures in a human patient, such as an epileptic seizure (ES), or a functional or dissociative seizure (FDS). The system and method use a diagnostic analytical platform that gets heart rate variability (HRV) analytical metrics from a ECG and uses an analytical diagnostic algorithm to determine if an ES or FDS has occurred in the patient. The diagnostic analytical platform can create a model for distinguishing that a predetermined type of seizure has occurred from the HRV analytical metrics.

The invention discloses a remote operation and maintenance diagnosis and predictive maintenance system for heterogeneous equipment, and relates to the technical field of equipment operation and maintenance. The system comprises a data acquisition and access module, a data processing module, a diagnostic analysis module, a prediction maintenance module, a man-machine interaction module and a storage module. The data acquisition and access module supports multiple communication protocols and acquires operation data of heterogeneous equipment; the data processing module preprocesses the collected data and completes multi-source data fusion; the diagnosis analysis module realizes equipment real-time fault detection, fault positioning and fault type association through a two-layer detection mechanism, and generates a fault diagnosis report; and the prediction maintenance module outputs the residual service life and the potential fault risk of the equipment based on the prediction model, and generates a targeted maintenance strategy and plan. According to the system, the problems of difficult access of heterogeneous equipment, poor fault detection real-time performance and insufficient maintenance planning are solved, remote intelligent operation and maintenance are realized, and the equipment shutdown risk and the maintenance cost are reduced.

A method for diagnosing and recommending treatment to a patient that may or may not present with occlusal disease includes acquiring 3D scans of the patient's maxillary and mandibular arches, and acquiring occlusal assessment data of the patient's teeth and dental arches. The method also includes automatically generating a treatment plan recommendation, and a recommended treatment plan projection and a non-treatment projection based, at least in part, on the acquired 3D scans of the patient's maxillary and mandibular arches, and the acquired occlusal assessment data of the patient's teeth and dental arches. The method also includes automatically displaying a rendering of the patient's simulated dental arch N years in the future based on the automatically generated recommended treatment plan projection, and a rendering of the patient's simulated dental arch N years in the future based on the automatically generated non-treatment projection, where N is greater than 1.

The invention discloses an ADHD screening method and system based on mobile terminal eye movement tracking and a multi-modal agent. The method comprises the following steps: (1) collecting and preprocessing mobile terminal eye movement data; (2) performing fixation point estimation based on a lightweight MGazeNet model; (3) eye movement feature extraction based on a multi-normal form interaction task; and (4) carrying out comprehensive diagnosis analysis based on machine learning and a large model agent. The invention further provides an ADHD screening system based on mobile terminal eye movement tracking and the multi-modal intelligent agent. The ADHD screening system sequentially comprises the following modules: (1) a front-end data acquisition and preprocessing module; (2) an end side eye movement tracking reasoning module; (3) an interactive task control and feature calculation module; and (4) a multi-mode intelligent diagnosis and analysis module. The ADHD screening method effectively solves the problems that traditional ADHD screening depends on expensive professional equipment and subjective scales, so that the popularity rate is low, and objectivity is poor, screening portability and diagnosis objective accuracy are improved, and the intelligent level of ADHD auxiliary diagnosis and user interaction experience are improved.

The application discloses a partial discharge signal denoising method, comprising the following steps: building a partial discharge signal detection platform and a partial signal collection platform; measuring and detecting characteristic signals in the partial discharge signals; screening and classifying the measured signals through a Gaussian test to obtain the characteristic signals; processing and denoising the signals through a fast S transform algorithm and a singular value decomposition SVD algorithm; testing and diagnosing the denoising effect by using two coefficients of noise suppression ratio and amplitude attenuation ratio; and displaying the diagnostic analysis result on a platform display interface. The application discloses a partial discharge signal diagnostic system based on a fast S transform and an adaptive singular value, and solves the problems that the existing technical methods are difficult to balance the denoising effect and extraction speed in the characteristic extraction of the partial discharge signals and are difficult to extract the characteristics.

The invention relates to the technical field of intelligent diagnosis, and discloses a water meter measurement data quality intelligent diagnosis system which comprises a data preprocessing module, a service feature extraction module, a data anomaly detection module, an anomaly diagnosis analysis module, a self-adaptive updating module and an interaction report generation module. The data processing module is used for performing data denoising processing on an original signal of water meter measurement data, generating clean original data, extracting an enhanced feature set from the clean original data, performing abnormal mark analysis on the enhanced feature set, generating abnormal mark data, performing root cause analysis on the data in combination with external environment data to obtain a data quality diagnosis result, and performing configuration optimization analysis to obtain a data quality diagnosis result; optimal configuration is obtained, and finally an interactive visual report is generated. According to the invention, the diagnosis accuracy and efficiency of the intelligent diagnosis system for the water meter measurement data quality can be improved.

The invention provides a courseware generation method and device, and relates to the technical field of data processing. According to the specific implementation scheme, the method comprises the steps of obtaining user information and structured cue words generated by a dynamic semantic knowledge graph; performing diagnostic analysis according to the user information and a structured cue word generated by the dynamic semantic knowledge graph, and outputting a learning situation difference graph; determining a learning path according to the learning condition difference graph and the dynamic semantic knowledge graph, and outputting a user learning blueprint; obtaining a visual AI workflow of the executable teaching method metadata model; and according to the visual AI workflow of the executable teaching method metadata model and the user learning blueprint, generating structured courseware content conforming to a preset courseware data structure. Through fine diagnosis of a dynamic semantic knowledge graph, personalized learning path planning and a visual AI workflow technology, personalized courseware generation is realized, and accurate intervention and personalized learning resource supply are provided for user learning.

The invention belongs to the technical field of rail traffic signals, particularly relates to a rail circuit interference test analysis method, system and equipment, and aims to solve the problems that a mixed interference source is difficult to accurately position, accidental faults cannot be captured and diagnosis is subjective. The method comprises the following steps: performing multi-channel synchronous acquisition and real-time storage on a voltage signal and a current signal of a track circuit; processing the original signal in real time, analyzing different frequency components through high-resolution spectrum analysis, and comparing with a preset threshold in combination with time domain analysis to trigger an alarm and generate a real-time analysis report; and performing intelligent analysis on historical data, performing verification and feature recognition on fault period data by establishing a probability model of a normal working condition, and generating a diagnosis analysis report. According to the method, the interference source can be accurately separated and positioned, the transient fault is automatically captured, objective and intelligent fault diagnosis is provided, and the efficiency and reliability of track circuit troubleshooting are remarkably improved.

An analyzer having an inner chassis surrounded by a housing includes: sample and diluent probes, a mixing housing including a first mixing chamber and a second mixing chamber, a flow cytometer including a flow unit, and sample and sheath pumps configured to perform a first set of tasks and a second set of tasks, respectively. The first set of tasks includes: aspirating a sample into the sample probe; dispensing a sample from the sample probe into the first and second mixing chambers; delivering a first sample diluent mixture to the flow unit; and delivering a second sample diluent mixture to the flow unit. The second set of tasks includes: dispensing a sheath into the flow unit in a manner cooperating with delivering the first sample diluent mixture to the flow unit; and dispensing a sheath into the flow unit in a manner cooperating with delivering the second sample diluent mixture to the flow unit.

The application provides a circuit breaker operating coil current characteristic diagnosis and analysis system and method. It belongs to the technical field of power system automation and substation state monitoring. The method comprises the following steps: synchronously recording the current of a plurality of circuit breaker operating coils on the DC feeder line screen side to generate multi-channel synchronous recording wave data; collecting SOE message information at the same time, using the time stamp and action identification information in the SOE message to automatically match and locate the specific action circuit breaker, and generating action circuit breaker positioning data; through real-time collection of the current waveform data of the circuit breaker operating coil and the use of high-precision algorithms for feature extraction and analysis, abnormal fluctuations in current changes can be accurately captured, such as excessive starting current and unstable maintenance current, thereby reducing the risk of circuit breaker misoperation or refusal caused by abnormal current characteristics, avoiding power system accidents caused by equipment failure, and improving the reliability of circuit breaker operation.

The invention provides an operation and maintenance failure analysis and diagnosis method and device, electronic equipment and a storage medium, and relates to the technical field of computers.The method comprises the steps that related texts of operation and maintenance failures are obtained, and the related texts comprise log texts and / or question texts input by a user and related to the operation and maintenance failures; matching the related text with a pre-constructed knowledge graph, and determining at least one recommended question text corresponding to the related text from the knowledge graph; and on the basis of each recommended question text, diagnosing the cause of the operation and maintenance failure. Through the related text of the operation and maintenance failure and the pre-constructed knowledge graph, accurate diagnosis and analysis of the operation and maintenance failure reason are realized, the accuracy and efficiency of diagnosis of the operation and maintenance failure reason are improved, and meanwhile, the solving efficiency of user problems and the user experience are improved.

This utility model relates to the field of medical diagnostic analysis technology, specifically an RFID-based reagent buffer device. It includes a test tube holder, an electronic tag fixed to one side of the test tube holder, multiple RFID readers mounted on the inner wall of a temporary storage rack, an anti-precipitation component mounted on a cooling module, and a control circuit board fixed to the inner bottom of the temporary storage rack. When the electronic tag on the reagent rack is facing the RFID reader, the RFID reader can decode the electronic tag signal. The anti-precipitation component drives the reagent tubes containing magnetic beads / microspheres to rotate. The cooling module, the extraction motor, the RFID reader, and the anti-precipitation component are all electrically connected to the control circuit board. This utility model can automatically identify the reagent rack, achieving a higher degree of automation, and can also drive the reagent tubes containing magnetic beads / microspheres to rotate, ensuring that the concentration at the bottom of the reagent tube is consistent with the concentration in the middle of the reagent tube, thus guaranteeing the accuracy of the detection results after extraction by the detector's extraction head.

1. Name of the design product: data processing equipment. 2. Use of the design product: used in the automobile industry, general industrial automation production line and intelligent instrument data acquisition and diagnosis analysis system. 3. Design points of the design product: in shape. 4. Pictures or photos that best show the design points: perspective view 1.

The invention discloses a motor drive control system and method for automation equipment, and relates to the technical field of motors. The system comprises a data acquisition module, a diagnosis analysis module and a protection module, and the data acquisition module is convenient for real-time acquisition of data parameters of rotation speed, current, voltage, temperature, vibration amplitude and noise sound waves in the operation process of the motor, so that multi-dimensional monitoring of the motor is realized; the diagnosis and analysis module is convenient to diagnose and analyze the collected data, the purpose of comprehensively analyzing and diagnosing the running state of the motor is achieved, the control precision of the motor is improved, and the purposes of protecting the motor in real time and early warning the fault are achieved by combining the protection module to carry out coping treatment and early warning on the fault after diagnosis and analysis.

The present application relates to the technical field of flow measurement and industrial monitoring, in particular to a full waveform diagnosis and adaptive gas flow measurement system; comprising full waveform acquisition, feature extraction, diagnostic analysis, dynamic weight calculation and health degree evaluation modules; the system obtains acoustic channel characteristics through digital sampling and multi-dimensional analysis of ultrasonic echoes; the core is to dynamically adjust the weight of each acoustic channel according to the waveform similarity and sound velocity deviation index to obtain the standard flow and generate the measurement health index; the present application realizes the transformation from single feature analysis to full waveform physical state deconstruction, can accurately distinguish scale, noise and turbulence anomalies, and overcomes the defect that the traditional technology cannot identify the root cause of the fault.

The application discloses a kind of server fault diagnosis method and system for server group, it is related to server operation and maintenance technical field, present and propose the following scheme, including data acquisition unit, data preprocessing unit, data training unit, data diagnostic analysis unit, scheme generation unit, scheme verification trial unit, data storage unit and data early warning unit, each unit is connected by communication bus or network link and forms closed loop diagnosis system.The application constructs full-link fault diagnosis system, through multidimensional collection hardware, software, network data, improves data quality by scientific pretreatment, adopts rule matching+machine learning hybrid model, combined with self-learning mechanism, improve diagnostic accuracy and coverage, generate personalized repair scheme and be verified by simulation / real machine, avoid secondary failure, full-process data safety storage and double early warning mechanism, realize fault tracing and prevention and control in advance, greatly improve server group operation and maintenance efficiency, guarantee business continuity.

The invention discloses a method for constructing a special digestive disease large model based on two-stage pre-training, and the method comprises the steps: constructing a digestive tract pathological image data set containing different magnifications through multi-scale pathological image feature extraction, and extracting macroscopic and microscopic features through a multi-magnification segmentation algorithm. A visual converter architecture is adopted, first-stage pre-training is carried out based on multi-view mask self-supervised learning of a teacher-student architecture, and universal pathological features are learned. And in the second stage, a region-of-interest classifier is constructed through multi-instance learning, key regions are screened, a high-quality sample library is constructed, and comparative learning fine tuning is performed on the feature encoder to obtain an enhanced model. The model is used for digestive tract pathology downstream tasks, full-slice image level diagnostic analysis can be achieved through feature aggregation, and the accuracy and efficiency of digestive pathology diagnosis are improved.

Systems, methods, and apparatuses for industrial equipment diagnostics are disclosed. Specifically, a bus controller operatively connected to a processor is physically connected to two or more controller area network (CAN) buses, wherein each CAN bus operates according to a different protocol. The bus controller is configured to control the rate at which messages are received on certain buses by adjusting parameters such as a baud rate and sample rate. The bus controller is further configured to filter or mask certain CAN bus messages based on identifiers within the messages. The bus controller is operatively connected to a remote processing system for processing the CAN bus messages from their raw data format into a useable format. The remote processing system performs diagnostic analysis on the processed CAN bus messages, and furthermore performs predictive analytics on the processed CAN bus messages to identify patterns or discrete events indicative of future system failures.