1394 results about "Diagnostic data" patented technology

A system for remotely monitoring and managing health statuses of a plurality of users includes software instructions storable on a memory device usable by a computing device, the software instructions causing a hardware processor of the computing device to receive a plurality of sets of health-related information from a plurality of mobile computing devices of a plurality of users. The health-related information includes physiological information derived from wearable devices of the users indicative of an onset of symptoms associated with an infection, contact tracing data, and diagnosis data. The hardware processor determines exposure levels based on at least the contact tracing data, and determines user-specific risk states based on physiological information, diagnosis data, and exposure levels.

A device fault diagnosis method, comprising: acquiring data for diagnosis of a target device; determining whether one or more abnormal sequences exist in the data for diagnosis; when it is determined that one or more abnormal sequences exist, on the basis of a statistical feature of each abnormal sequence, forming a feature vector of the abnormal sequence; and on the basis of the feature vector and a preset fault classification model, determining a fault type corresponding to the abnormal sequence.

A system and method for ΔI-powered engine diagnostics combine OBD-II data, high-frequency sound, and vibration analysis to detect engine wear and mechanical faults. A detachable puck sensor in the engine bay captures sound and vibration signals, while an OBD-II module collects engine performance metrics. A smartphone app collects and uploads data to a backend AI engine, where Dynamic Time Warping (DTW) aligns event-driven or asynchronous data and time series data from multiple sources, ensuring accurate feature fusion. Machine learning models then detect engine wear, belt degradation, knocking, and bearing faults, generating a diagnostic report with severity assessments and predictive maintenance recommendations. By integrating multi-modal sensor data, this system enhances early-stage fault detection beyond traditional OBD-II diagnostics, offering greater accuracy in assessing physical wear conditions and optimizing vehicle maintenance.

The invention relates to the technical field of medical information processing, in particular to a main diagnosis rationality judgment method and system based on artificial intelligence. The method comprises the following steps: acquiring diagnosis request data, and performing candidate main diagnosis generation on the diagnosis request data by using preset medical knowledge graph data and preset intelligent detection rule data to obtain candidate main diagnosis data; performing diagnosis execution parameter generation according to the candidate main diagnosis data to obtain candidate main diagnosis parameter data; acquiring historical diagnosis request data and historical candidate main diagnosis data, and performing diagnosis linkage processing according to the historical diagnosis request data and the historical candidate main diagnosis data to obtain diagnosis linkage network data; and performing node interaction processing on the candidate main diagnosis data according to the diagnosis linkage network data to obtain candidate main diagnosis weight data. According to the method, the intelligent level of diagnosis rationality judgment, the credibility of a scoring result and the adaptability of a clinical system are improved.

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

The invention discloses an intelligent auxiliary fracture monitoring system for orthopedic nursing, which comprises the following steps that: the system acquires mechanical parameters, movement track data and biological signals of a fracture patient through a multi-mode sensor, and intelligently analyzes the data by using a deep learning algorithm; generating a fracture state diagnosis result and recovery trend prediction data; the low-power-consumption wireless communication module realizes real-time transmission of diagnosis data and supports remote system parameter adjustment; the personalized nursing scheme generation module generates a rehabilitation plan, a load adjustment strategy and an auxiliary device configuration scheme through an optimization algorithm based on the patient biomechanical model and the diagnosis result; and the adaptive feedback adjustment module dynamically adjusts a data acquisition strategy and processing parameters to ensure the real-time response capability of the system to the state of the patient. The system is suitable for the technical field of intelligent auxiliary monitoring, and can realize real-time monitoring, intelligent diagnosis and personalized rehabilitation guidance of fracture patients.

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

The invention discloses a root cause analysis method and device based on a large language model, equipment and a medium. The method comprises the steps that a target entity and a topological relation are extracted in response to a root cause analysis request; searching matched historical root cause cases in a vector database, inputting the topological relation, the historical root cause cases and cue words into a large language model to generate a doubtful point list, and extracting downstream entities in the doubtful point list; calling a detection tool to collect entity diagnosis data and identify abnormal downstream entities; returning to execute the operation of retrieving the historical root cause case until a preset iteration ending condition is met; and inputting the final doubtful point list, the abnormal diagnosis data, the topological relation and the historical root cause case into the large language model again to obtain a root cause reasoning result and generate a root cause report. According to the embodiment of the invention, through the full-chain design of natural language understanding, topological constraint, historical cases, dynamic detection and iterative reasoning, the fault positioning efficiency and the root cause accuracy are improved, and the operation and maintenance labor cost and the service fault time consumption are remarkably reduced.

An action generation method includes receiving, by one or more processors via a conversational interface, a query from a user, receiving, by the one or more processors, building subsystem data for one or more building subsystems in a building, retrieving, by the one or more processors, subject matter expert data, determining, by the one or more processors, an anomaly of the one or more building subsystems, based on the subject matter expert data and the building subsystem data, generating, by the one or more processors, a recommendation to resolve the anomaly based on fault detection and diagnostic (FDD) data related to a fault determined in the one or more building subsystems, and generating, by the one or more processors using a generative large language model, a response to the query based on the recommendation, the FDD data, and the subject matter expert data.

The invention discloses a fault prediction and health management system, which comprises a data collection module, an equipment monitoring module and an equipment management and control module, and is characterized in that the data collection module is used for collecting state data in the operation process of medical equipment, historical fault data of the equipment and diagnosis data of a patient; the equipment monitoring module is used for detecting whether the running state of equipment is abnormal or not and predicting the failure rate of the equipment according to the running state of the equipment, and the equipment management and control module is used for recording historical maintenance data of the equipment, giving an alarm and managing the full life cycle and the part maintenance cycle of the equipment when the equipment fails. The data collection module, the equipment monitoring module and the equipment management and control module are electrically connected with one another, the data collection module comprises a historical data entry module, a data acquisition module and a diagnostic data entry module, and the data acquisition module is used for collecting equipment operation data.

The invention provides a time-varying working condition transfer learning bearing fault diagnosis method based on a diffusion model, and the method comprises the steps: obtaining and preprocessing an original fault vibration signal of a bearing, and obtaining a two-dimensional time-frequency spectrum image; constructing a de-noising diffusion model, and enabling the de-noising diffusion model to learn the image features of the two-dimensional time-frequency spectrum image under the stable working condition; through a Repaint method of a two-dimensional time-frequency spectrum image, a complete bearing fault data set under a stable working condition is constructed based on a vibration signal of a time-varying working condition; and carrying out bearing fault diagnosis by utilizing the expanded bearing fault data set and a field generalization method. According to the method, the problem of low bearing fault diagnosis accuracy caused by inconsistent distribution of field generalization bearing fault diagnosis data is solved, the bearing fault diagnosis accuracy is improved, and the method is more suitable for bearing fault diagnosis of different bearing application scenes.

The invention relates to a rotating machine fault diagnosis method based on a self-supervised time-frequency comparison fusion network, and belongs to the technical field of fault diagnosis. The method comprises the following steps: converting a time sequence signal into a frequency signal by using fast Fourier transform, and respectively performing data enhancement on the time frequency signal; initializing model parameters, adopting a multi-stage contrast learning strategy, and combining instance-stage, clustering-stage and fusion-stage contrast loss to obtain a pre-training target loss function; variables in the target loss function are selected and updated until convergence or the maximum iteration number is reached, and the pre-training model is stored; pre-training model parameters are loaded, a cross entropy loss function is adopted, and a fine tuning target loss function is obtained through label guidance; selecting and updating the target loss function of the fine tuning model until convergence or reaching the maximum iteration number, and storing the fine tuning model; and loading the trained fine-tuning model parameters, and inputting diagnosis data to obtain a prediction label, namely a diagnosis result. According to the invention, the precision and efficiency of fault diagnosis of the rotating machinery are obviously improved.

Systems and methods for providing assistance to a surgeon during an implant surgery are disclosed. A method includes defining areas of interest in diagnostic data of a patient and defining a screw bone type based on the surgeon's input. Post defining the areas of interest, salient points are determined for the areas of interest. Successively, an XZ angle, an XY angle, and a position entry point for a screw are determined based on the salient points of the areas of interest. Successively, a maximum screw diameter and a length of the screw are determined based on the salient points. Thereafter, the screw is identified and suggested to the surgeon for usage during the implant surgery.

The invention discloses a diagnostic data remote transmission method based on a regional remote diagnosis and treatment platform. The method comprises the following steps: S1, collecting diagnostic data of a patient; s2, preprocessing the acquired diagnosis data; s3, carrying out data transmission by utilizing a short-distance wireless communication technology and a long-distance mobile communication technology; s4, performing data encryption and identity verification by adopting AES and RSA encryption algorithms; s5, after the regional remote diagnosis and treatment platform receives the diagnosis data, decryption and identity verification are carried out on the data; s6, performing image data analysis by using a convolutional neural network, processing character data by using a natural language processing technology, predicting and classifying physiological parameters by using regression analysis and a classification algorithm, and performing fusion analysis on multi-modal data in combination with a deep learning model; and S7, transmitting the analyzed and processed diagnosis result back to a local data terminal through a long-distance mobile communication technology. The system has the advantages of high transmission stability, high safety, high transmission efficiency and high diagnosis accuracy.

An action generation method includes receiving, by one or more processors via a conversational interface, a query from a user, receiving, by the one or more processors, building subsystem data for one or more building subsystems in a building, retrieving, by the one or more processors, subject matter expert data, determining, by the one or more processors, an anomaly of the one or more building subsystems, based on the subject matter expert data and the building subsystem data, generating, by the one or more processors, a recommendation to resolve the anomaly based on fault detection and diagnostic (FDD) data related to a fault determined in the one or more building subsystems, and generating, by the one or more processors using a generative large language model, a response to the query based on the recommendation, the FDD data, and the subject matter expert data.

The invention provides a fault diagnosis data annotation method and device, electronic equipment and a medium, belongs to the technical field of data annotation, and aims to solve the problem of low efficiency of manual data annotation in related technologies. The method comprises the steps that a work order processing record is acquired from a work order system, and the work order processing record comprises a log identification code used by a fault processing terminal for processing a work order and a fault label of a fault component; downloading a corresponding log packet from a log system according to the log identification code; performing standardization processing on the log data in the log packet to obtain standardized log data; and marking the standardized log data by using the fault label of the fault component to obtain a training sample for training a fault diagnosis model.

A navigation equipment real-time fault diagnosis method based on cross-modal attention fusion belongs to the technical field of power grid equipment fault diagnos.The method comprises the steps that firstly, the type of real-time diagnosis data capable of being obtained by navigation equipment is systematically analyzed, and the real-time diagnosis data is divided into four modals of sensor data, electrical data, acoustic data and text data; after data cleaning is completed, preliminary feature extraction is implemented. Then, constructing a fault diagnosis model based on a cross-modal attention mechanism, constructing a training data set by using the joint features, and carrying out neural network model training; after model training is completed, real-time fault diagnosis of the navigation equipment can be realized through a data pre-processing technology and a fault diagnosis model based on acquired data. According to the scheme, a multi-modal real-time sensing system is constructed, deep feature interaction is realized in combination with a cross-modal attention mechanism, and closed-loop diagnosis capability is established. And core guarantee is provided for real-time state sensing and accurate fault positioning of navigation equipment.

The invention discloses a children respiratory tract infection diagnosis method and system based on multi-modal data fusion, and relates to the technical field of respiratory tract infection diagnosis. Distributing the priority of each modal data according to the change trend of the physiological data monitored in real time; generating a comprehensive diagnosis characteristic index of the child health state; through time sequence analysis, the fused feature indexes are monitored in real time, when an abnormal mode is detected, early warning information is automatically generated, and diagnosis result suggestions are sent through a medical staff or parent interface; the comprehensive diagnosis characteristic indexes are optimized according to comparison between a diagnosis result and an actual situation; and storing each diagnosis data and early warning record in a children exclusive health database for long-term trend analysis and future diagnosis reference. According to the invention, the real-time performance and accuracy of children respiratory tract infection diagnosis are improved, the disease monitoring and health management capabilities are enhanced, and a more scientific and comprehensive health protection means is provided for children.

The invention relates to a fault diagnosis method, system and device for a chip test equipment control circuit, and the method comprises the steps: obtaining an operation parameter set of the chip test equipment control circuit, carrying out the abnormal threshold matching, and obtaining a fault classification result; performing power supply dynamic signal tracking on the chip test equipment control circuit based on the fault classification result to obtain power supply deviation data, and extracting a static offset set of the chip test equipment control circuit; performing internal failure association on the power supply deviation data and the static offset set, and performing peripheral failure association identification on the chip test equipment control circuit to obtain a diagnosis data set; and constructing a hierarchical fault tree according to the diagnosis data set, and performing peripheral root cause matching on the chip test equipment control circuit to obtain fault positioning information. According to the invention, the propagation path and the influence range of the fault can be comprehensively analyzed, and the fault positioning accuracy is improved.

The invention discloses a database anomaly diagnosis method and system based on a large language model. The method comprises the steps that a diagnosis document is split according to chapters to extract diagnosis knowledge fragments so as to construct a diagnosis knowledge base; clustering the diagnosis knowledge fragments according to a preset topic, and generating a corresponding prompt word template for each cluster; judging whether the monitored index value in the diagnosis knowledge base exceeds a preset threshold value or not, and matching related diagnosis knowledge from the diagnosis database according to the prompt word template based on a judgment result that the index value exceeds the preset threshold value so as to collect corresponding abnormal information; and inputting the exception information into a plurality of large language models for parallel processing to obtain corresponding database exception diagnosis results, and synthesizing the diagnosis results of the large language models through a tree search algorithm to carry out diagnosis path optimization. The method aims at effectively analyzing abnormal root causes of different databases in a real scene.

The invention relates to the technical field of new energy station fault diagnosis, in particular to a new energy station fault diagnosis system and method based on an intelligent sensor, and the system comprises an intelligent processing unit which collects operation parameters in a station in real time, carries out the fault diagnosis of the operation parameters, and obtains diagnosis data; when the method is used, potential problems of equipment can be found in time, the data transmission bandwidth is effectively reduced, rapid transmission of real-time data is guaranteed, the equipment can adjust the working state in a self-adaptive mode under different operation conditions, false alarm or missing alarm caused by a fixed threshold value is avoided, and the service life of the equipment is prolonged. The influence of a network bottleneck on the overall efficiency of the system is reduced, the response speed and stability of the system are improved, the bandwidth resource waste is reduced, the overall performance of the network is improved, the timeliness and reliability of fault diagnosis are enhanced, meanwhile, the real-time performance and reliability of a data acquisition and monitoring system in a new energy station are greatly improved, and the application prospect is wide. And the potential cause of the fault can be accurately identified.

The invention relates to the technical field of data processing, and discloses a photovoltaic module parameter monitoring method and device based on multichannel synchronous potential acquisition. The method comprises the following steps: performing parameter configuration on a multi-channel voltage acquisition module in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain acquisition setting parameters; performing synchronous trigger mechanism setting on master and slave devices in the photovoltaic module parameter monitoring system based on multi-channel synchronous potential acquisition to obtain a multi-channel acquisition trigger signal; performing synchronous sampling and local processing on the voltage signal of the photovoltaic module to obtain a standardized data frame with a timestamp; compressing and transmitting the standardized data frame with the timestamp to obtain a multi-channel synchronous data set; and performing multi-dimensional analysis on the multi-channel synchronous data set to obtain photovoltaic module performance diagnosis data. The photovoltaic module parameter monitoring efficiency and accuracy based on multi-channel synchronous potential acquisition are improved.

Disclosed herein is a multi-modal sensor system, including a wearable device configured to receive signals relating to a swallowing process of a subject, the wearable device including one or more surface Electromyograph sensors configured to receive signals relating to electrical potential in muscles of the throat, one or more bio-impedance sensors, one or more memories, one or more processors configured to operate one or more sensors of the wearable device, synchronize the signals to one or more predetermined events to generate a synchronization feature, receive the signals as a first diagnostic data set, analyze the first diagnostic data set, assess, based on the analysis, the swallowing process of the subject to yield an assessment output, and present the assessment output and determine a bio-impedance signal.

Systems and methods for vehicle diagnostic services utilizing a diagnostic server. The systems comprise a diagnostic server that is in data communication with a service tool and the vehicle communication interface (VCI) of a vehicle. The data communication may be accomplished using a wide area network (WAN). The diagnostic server acquires diagnostic data from the VCI and generates a diagnostic report for a user to utilizing during service actions of the vehicle. Some embodiments utilize an external gateway adapter to enable data communication between the diagnostic server and vehicles lacking a native function to interface with external devices or components across networks.

The invention provides a fault diagnosis system and a construction method thereof, a storage medium and a computer, and the method constructs an ontology architecture of a fault knowledge graph, and processes maintenance data and historical fault diagnosis data into structured maintenance data and structured diagnosis data based on the ontology architecture of the fault knowledge graph. And further performing knowledge association on the structured maintenance data and the structured diagnosis data to obtain structured diagnosis maintenance data, associating maintenance knowledge with fault diagnosis knowledge, and obtaining a target fault knowledge graph in combination with an ontology architecture of the fault knowledge graph. According to the fault diagnosis system, the construction method thereof, the storage medium and the target fault knowledge graph in the computer, various maintenance knowledge and fault diagnosis knowledge can be effectively associated, maintenance personnel can be accurately guided to find fault points, sufficient maintenance knowledge can be provided together, and the fault diagnosis and maintenance efficiency can be effectively improved.

A system and method includes connecting an interface device to a port of a vehicle to be in communication with an electrical system of the vehicle for monitoring the vehicle, and running a selected one of multiple available calibration and / or repair programs via the interface device to generate a calibration / repair data file of the vehicle, where the data / log file is generated in one of a plurality of possible native file formats. A report generation tool acquires screen captures and / or screen recordings to document the data / information displayed during the steps of a calibration / repair procedure. An evaluation tool extracts calibration / repair data from the data / log file, where the evaluation tool is configured to extract calibration / repair diagnostic data from data / log files in each of the plurality of possible native file formats, and outputs the calibration / repair diagnostic data to a database in a common format from which detailed calibration / repair diagnostic reports are generated.

A method of providing vehicle diagnostics includes receiving a request for AI-assisted diagnostics, receiving diagnostic data obtained from a vehicle, prompting a user to describe one or more symptoms exhibited by the vehicle, processing the one or more symptoms and the diagnostic data at least in part using a natural language processing (NLP) model, and providing natural language guidance to the user based on a result of the processing. The natural language guidance may include testing instructions for the user to perform a step-by-step procedure to diagnose the vehicle and / or repair instructions for the user to perform a repair on the vehicle.

The invention relates to an infection risk digital diagnosis and evaluation method integrated in a medical information system, and belongs to the technical field of risk evaluation. The method comprises the following steps: acquiring three-tube risk screening data, and automatically generating a risk score through a medical information system according to the three-tube risk screening data; a three-management early warning threshold value is preset, and the medical information system obtains continuous prediction data through a monitoring early warning model according to the risk score and the three-management early warning threshold value; obtaining patient infection risk digital diagnosis data through a risk prediction model according to the continuous prediction data; and obtaining a personnel optimization scheduling result through a nursing personnel scheduling model according to the patient infection risk digital diagnosis data. The infection risk assessment integrated in the medical information system is realized, the accuracy of infection risk assessment is improved, and the nursing service is improved.

The invention belongs to equipment diagnosis and maintenance systems, particularly relates to an online predictive maintenance system universal for all equipment of oil and gas pipelines, and aims to solve the problems of difficulty in anomaly positioning and low processing efficiency of a passive anomaly response mode of an existing fault diagnosis system. The method comprises the following steps: through corresponding sensors arranged on different devices or parts, according to a unified data acquisition interface and protocol, obtaining operation data and self-diagnosis data, and then selecting database types to carry out distributed storage and carry out unified device management; constructing an early warning model corresponding to each type of equipment or part based on the historical operation data, and setting early warning conditions for the operation data and the self-diagnosis data or each type or combination of the operation data and the self-diagnosis data; and based on the operation data and the self-diagnosis data, analyzing the operation trend of the equipment through an early warning model, and when the operation trend of the equipment triggers an early warning condition, sending out an early warning signal. According to the invention, equipment maintenance is converted into predictive maintenance management, and stable operation of the whole pipeline system is ensured.

The present invention discloses a fault diagnosis method, system, device and medium. The method comprises: receiving a vehicle diagnosis push message sent by a remote diagnosis server; wherein the vehicle diagnosis push message comprises at least one of the following: a diagnosis task type and a diagnosis script information; obtaining a corresponding target diagnosis script file and a corresponding target diagnosis condition according to the diagnosis task type and / or the diagnosis script information; wherein the target diagnosis script file is obtained by converting a diagnosis database and a diagnosis sequence, and the target diagnosis script file is an interpretive language file; and performing fault diagnosis on a target vehicle according to the target diagnosis condition and the target diagnosis script file. The present invention eliminates the need for the remote diagnosis client to parse the diagnosis sequence and frequently read the diagnosis database, thereby reducing the load on the remote diagnosis client and improving the operating efficiency of the diagnosis task; at the same time, the diagnosis task type is partitioned to improve the diagnosis efficiency.