952 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 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 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.

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.

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 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.

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.

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 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.

The invention relates to the technical field of medical health information, in particular to a patient disease evolution relation prediction method and system based on a time sequence diagram neural network, and the method comprises the steps: S01, obtaining offline diagnosis data and online diagnosis data, and building first medical record data information and second medical record data information based on the offline diagnosis data and the online diagnosis data; and S02, sorting the first medical record data information and the second medical record data information according to a time sequence, obtaining comprehensive medical record data information, analyzing all medical record data, obtaining medical record data information of a target disease, and establishing medical record data information of the target disease. According to the system and the method, offline diagnosis and online diagnosis are integrated, so that a silent period caused by a single data source can be filled, the integrity of a patient treatment record is ensured, and the continuity of disease management is improved.

Techniques are disclosed for reporting diagnostics data by a first network device to a cloud-based Wide Area Network (WAN) assurance system, responsive to the first network device detecting a communication issue with the cloud-based WAN assurance system. For example, the first network device detects an issue with sending telemetry data to the cloud-based WAN assurance system via a first communication path. In response, the first network device determines a second network device that has connectivity to the WAN assurance system. The first network device sends diagnostics data to the second network device along a second communication path for forwarding to the cloud-based WAN assurance system. The cloud-based WAN assurance system receives the diagnostics data from the second network device. The cloud-based WAN assurance system controls the second network device to remediate the first network device based on the diagnostics data.

The invention relates to a transformer fault diagnosis method, and particularly discloses a GAN-MCNN-based transformer fault diagnosis method, which comprises the following steps of: 1, carrying out standardization processing on transformer fault diagnosis data of a sampling set, then increasing original sample feature dimensions from 5 dimensions to 25 dimensions based on a ratio method, and enhancing sample features; 2, increasing the number of minority class fault samples by adopting a generative adversarial network algorithm; a third step of designing a transformer fault identification model based on a multi-scale convolutional neural network by adding a multi-scale convolutional layer on a 1DCNN structure; and a fourth step of training and testing the designed model by using the data set after dimension and quantity enhancement, and performing generalization experiment comparison with some intelligent algorithms, and the result shows that the method provided by the invention has the average recognition accuracy of 93.58%, and the generalization performance is good.

The invention relates to a vehicle remote diagnosis method and system and a medium, and the method comprises the steps: collecting diagnosis data from a vehicle, and the diagnosis data comprises vehicle controller area network data, vehicle electronic control unit logs, fault diagnosis codes and vehicle driving data; transmitting the diagnosis data to a cloud platform through a wireless network; analyzing the diagnosis data on the cloud platform by using an artificial intelligence model, wherein the artificial intelligence model comprises a fault diagnosis model and a fault prediction model; generating a diagnosis result based on an analysis result of the artificial intelligence model, wherein the diagnosis result comprises current fault diagnosis and potential fault prediction; sending the diagnosis result to the vehicle or the user equipment; and executing corresponding maintenance or preventive maintenance operation based on the diagnosis result. According to the invention, real-time monitoring, remote diagnosis and predictive maintenance of vehicle faults can be realized, so that the diagnosis efficiency is improved, and the maintenance cost is reduced.

The invention discloses a thyroid ultrasound image diagnosis method based on deep learning, and the method comprises the following steps: collecting a thyroid ultrasound original image set, and carrying out the preprocessing; performing focus segmentation on the standardized thyroid ultrasound image set; performing morphological constraint and boundary refinement; calculating the blood flow direction, blood flow velocity and blood flow power of each thyroid focus area and neighborhood; generating a preliminary fusion feature map based on a feature adaptive deep learning network, and fusing the preliminary fusion feature map with the thyroid focus blood flow feature vector set; obtaining a thyroid focus detection list through thyroid focus benign and malignant discrimination branches; and generating thyroid focus structured diagnosis data records based on the thyroid focus detection list, and writing the thyroid focus structured diagnosis data records into a computer-aided diagnosis system. According to the method, deep learning and multi-modal blood flow features are fused, intelligent diagnosis of the thyroid focus is achieved, and the method has the advantages of being high in precision, high in interference resistance and structured in result.

The invention provides a temporal-mandibular joint disease intelligent diagnosis method and device based on multi-modal data, and the method comprises the following steps: obtaining multi-modal medical data, and extracting the vector representation of each piece of multi-modal medical data to form a knowledge vector library; extracting entities, entity relationships and events from the multi-modal medical data, and establishing a disease diagnosis knowledge graph based on the entities, the entity relationships and the events; and obtaining to-be-diagnosed data, and matching the to-be-diagnosed data with the disease diagnosis knowledge graph in the knowledge vector library to obtain an optimal diagnosis method. According to the scheme, the knowledge vector library and the disease diagnosis knowledge graph are constructed by acquiring the multi-modal medical data, feature extraction and analysis are performed on different modal data in multiple modes, the different modal data are integrated into the knowledge graph, and meanwhile, the optimal diagnosis result is acquired based on multi-mode matching weighted sorting of the to-be-diagnosed data, so that accurate and intelligent diagnosis is realized.

The invention provides a vehicle fault diagnosis method, apparatus and device, and a readable storage medium. The method comprises the steps of obtaining fault description information and a vehicle identification identifier input by a user; the fault description information is used for representing a vehicle fault condition; in a preset diagnosis database, determining a target fault code corresponding to the fault description information, and determining a plurality of fault codes associated with the vehicle identification identifier; the diagnosis database stores a corresponding relation between the fault description information and the fault codes; and under the condition that the target fault code accords with the multiple fault codes corresponding to the vehicle identification identifier, generating a fault diagnosis result according to the target fault code so as to complete vehicle fault diagnosis. The vehicle fault diagnosis efficiency can be improved.

The invention provides a liver cancer patient diagnosis data retrieval precise diagnosis and treatment system based on big data, and relates to the technical field of medical diagnosis. The liver cancer patient diagnosis data retrieval precise diagnosis and treatment system based on big data comprises a case database module, a clinical decision support system, a deep learning module, an early warning visualization module, a data analysis and processing module, a multi-modal data fusion module, a data collection and storage module, a feature selection and selection module and a data acquisition module. Image data, genome data, pathological data and electronic medical record data are integrated through multi-modal data fusion, comprehensive analysis is carried out, the accuracy of risk prediction is improved, treatment schemes are dynamically adjusted according to specific conditions and treatment responses of patients, personalized risk prediction and treatment scheme recommendation are achieved, and the risk prediction and treatment scheme recommendation efficiency is improved by combining gene data. Medicine selection and dosage are optimized, the treatment risk is reduced, the treatment effect is improved, doctors can understand the basis of system decision making, and the credibility of clinical application is improved.

The invention relates to the technical field of Tokamak plasma diagnosis and data fusion, in particular to a multi-modal fusion Tokamak high-frequency Thomson scattering reconstruction method. According to the technical scheme, the method comprises the following steps: acquiring multi-modal diagnosis data on a Tokamak device, wherein the multi-modal diagnosis data comprises high-frequency auxiliary diagnosis data and low-frequency Thomson scattering data; and performing feature engineering and physical enhancement processing on the high-frequency auxiliary diagnosis data. According to the method, through multi-modal diagnosis data fusion and interpretable KAN network modeling, the Thomson scattering time resolution is greatly improved on the premise that hardware is not transformed, and high-fidelity reconstruction of sub-millisecond transient processes such as ELM and magnetic island evolution is achieved; the method has physical interpretability, can reveal the implicit relationship between diagnostic quantities, enhances the robustness under complex experimental conditions through strategies such as modal loss, and provides key high-frequency diagnostic support for Tokamak high-performance operation and real-time control.

The invention discloses a power distribution network single-phase grounding reason diagnosis method based on confidence and ambiguity evaluation. The method comprises the following steps: acquiring original diagnostic data corresponding to a waveform to be detected; determining a confidence level corresponding to the original diagnosis data according to a preset grading rule based on the similarity distance corresponding to each of the plurality of neighbor sample waveforms in the original diagnosis data; determining a plurality of candidate reasons based on the single-phase grounding reasons corresponding to the plurality of neighbor sample waveforms; based on the number of the plurality of neighbor sample waveforms, determining occurrence frequencies corresponding to the plurality of candidate reasons; determining target ambiguity based on the occurrence frequencies corresponding to the candidate reasons and the reason label semantic association matrix; and based on the confidence level and the target ambiguity, adaptively generating a diagnosis result. According to the method, the technical problem that the information completeness of the conclusion output by the existing analysis method is insufficient, so that an objective judgment basis for the conclusion reliability is lacked when the operation and maintenance personnel make decisions is solved.

Disclosed in the present invention are a vehicle door state recognition method and system based on time series data to image conversion. The method comprises: collecting time series data in a vehicle door moving process and performing data preprocessing, and defining a set of calibration data and a set of data to be diagnosed; separately performing distribution density grayscale image processing on the preprocessed set of calibration data and the preprocessed set of data to be diagnosed, and generating a difference density image; performing change area recognition and change degree evaluation on the basis of the difference density image to obtain a vehicle door state offset degree; and establishing a vehicle door functional component zoning map, performing functional component association on the basis of the vehicle door state offset degree, and recognizing an abnormal functional component. According to the present invention, the degree of a vehicle door abnormality can be quantitatively evaluated, and an abnormal area is associated with a moving component, so that the present invention achieves high universality and good interpretability, and facilitates on-site precision maintenance.

The invention discloses an ultrasonic auxiliary system for automatic recognition and diagnosis of emergency fracture, and relates to the technical field of acoustic fracture detection. The auxiliary system comprises an ultrasonic image acquisition module used for acquiring ultrasonic image data of a fracture position of a patient; the processing module is used for processing the ultrasonic image data and outputting identification data; and the diagnosis decision module is used for outputting diagnosis data based on the identification data of the processing module. Through the arrangement of the region focusing analysis unit, resource allocation is optimized and calculated by gradually reducing the analysis range, the primary detection layer quickly eliminates intact regions and outputs primary fracture region marks, the intermediate classification layer performs soft tissue reaction quantitative analysis on the primary fracture regions and generates marked regions, and the advanced positioning layer processes the marked regions. The resource consumption is reduced in combination with sub-pixel-level analysis, and the architecture enables system resources to be quickly concentrated in a fracture area, so that the processing time efficiency of fracture recognition is reduced.