772 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 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.

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.

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 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 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 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.

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.

This invention relates to methods and systems relating to medical diagnostics. In particular, the invention relates to a method of processing data from a medical diagnostic system which comprises a detector arrangement configured to detect interaction of high energy particles with tissue. The method comprises receiving diagnostic data associated with interaction of high energy particles with tissue, wherein the diagnostic data is generated as a result of interaction of the high energy particles and the tissue being detected by the detector arrangement of the medical diagnostic system. The method further comprises processing the received diagnostic data with a trained machine-based classifier in order to facilitate and / or assist a medical diagnosis, wherein the trained machine-based classifier is trained at least with digital diagnostic data generated by a digital twin of the medical diagnostic system, wherein the digital twin is a computer-implemented simulation of at least part of the medical diagnostic system.

The invention discloses a vehicle diagnosis service method and system based on dynamic protocol adaptation and context awareness, and the method comprises the steps: analyzing a diagnosis request, and determining a service range and a dependency relationship required by the diagnosis request; obtaining multi-dimensional context information, and based on the obtained information and the dependency relationship, executing a routing decision to select an optimal service instance and a protocol path; dynamically calling a protocol adapter through a protocol adaptation matrix based on the service instance and the protocol path, converting the diagnosis request into a protocol message, and sending the protocol message to a vehicle network; and receiving the original diagnosis data returned by the vehicle network to generate a complete diagnosis report. According to the method, flexible calling of the protocol adapter is realized through the dynamic protocol adaptation matrix, the service range and the dependency relationship are analyzed in combination with the service governance engine, and the routing decision is optimized by utilizing the multi-dimensional context information, so that the method has the advantages of dynamic and flexible protocol adaptation, accurate and intelligent service analysis and global optimization of the routing decision.

The invention provides an intelligent fault diagnosis and self-healing method and system for a low-voltage standardized cabinet, and belongs to the technical field of standardized cabinet fault diagnosis and self-healing control based on deep learning. The method comprises the following steps: firstly, by constructing a digital twin system and adopting a three-dimensional matrix type sensor layout, collecting data containing a limit fault working condition and fusing sensor position information, completing the construction of a fault diagnosis data set; then designing an intelligent fault diagnosis model to realize fault type judgment and position sensing; meanwhile, a fault self-healing reinforcement learning model is constructed, and a fault state feature representation network, a repair strategy network and reward strategy design are covered; and finally, constructing a reinforcement learning multi-stage action step execution strategy based on the model framework to gradually guide strategy iteration, and ensuring the safety and efficiency of fault self-healing operation so as to optimize the overall performance of the model. According to the invention, full-process automation from fault diagnosis to fault self-healing is realized, and the automation and intelligence degree of fault detection is greatly improved.

Techniques for controlling a vehicle to safely perform a maneuver are discussed herein. For example, techniques can include determining, based on diagnostic data associated with the vehicle, a first constraint to be applied by a vehicle system of the vehicle. At a first instance, the first constraint may be transmitted to the vehicle system. The systems and / or methods may determine an absence of an action by the vehicle that is associated with first constraint and determine a second constraint to be applied by the vehicle system. At a second instance, the second constraint may be transmitted to the vehicle system. The second constraint may be associated with an emergency stop to ensure the safety of passengers within the vehicle and persons and objects in a surrounding environment.

The invention provides an interaction method and system for thromboembolism risk assessment, and can be applied to the technical field of artificial intelligence. The method comprises the steps that a first server side responds to an inquiry request sent by a first terminal, and inquiry diagnosis data and historical clinical data are sent to a second server side; the second server obtains an initial risk assessment score according to the inquiry diagnosis data and the historical clinical data; under the condition that the initial risk assessment score is greater than a preset risk threshold value, the second server side sends inquiry diagnosis data and historical clinical data to a second terminal; the second terminal determines risk influence factors according to the inquiry diagnosis data and the historical clinical data; the second server obtains the detection result, and performs hospital thromboembolism risk assessment on the detection data by using a Bayesian network to obtain a target risk assessment result; and the second server determines a rehabilitation guidance strategy according to the target risk assessment result and the discharge diagnosis result, and sends the rehabilitation guidance strategy to the first terminal.

A method of diagnosing whether there is abnormality includes obtaining data having a quantity of state of one or more assessment items from the facility, classifying, for each operation state of the facility, data obtained from the facility, assessing sufficiency of the number of pieces of data for each classified data group, calculating multiple parameter values configuring a trained model in accordance with the sufficiency and holding the parameter values in association with the operation state, obtaining the parameter values configuring the trained model without overlaps for each parameter, in accordance with the operation state of data to be diagnosed and a status of holding of the parameter values associated with each operation state, creating the trained model to diagnose data, with the parameter values, calculating a degree of abnormality for determination as to abnormality based on the trained model, and determining whether or not there is the abnormality.

The invention discloses a marine environment spring layer characteristic diagnosis data processing method, and aims at complex dynamic characteristics of a land frame edge sea spring layer to realize accurate diagnosis through multi-source data cooperation and an intelligent algorithm. The method comprises the following steps: acquiring and preprocessing field observation, remote sensing, sonar, terrain and tidal ocean current data; extracting spatio-temporal characteristics based on a CNN-LSTM hybrid network, and generating a dynamic spring layer threshold; fusing multi-source data by adopting an improved D-S evidence theory, and constructing a three-dimensional spring layer characteristic field; predicting an internal wave in combination with an attention mechanism model, and optimizing a preorder model by using sonar data. The method breaks through the limitation of a fixed threshold value, realizes spring layer dynamic identification, three-dimensional reconstruction and internal wave prediction, improves the diagnosis precision of a complex sea area, and provides key technical support for underwater detection, engineering safety and environmental research.

The embodiment of the invention discloses a diagnosis method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical driving data of a target vehicle; acquiring historical diagnosis data of a first vehicle part of the target vehicle; according to the historical diagnosis data and the historical driving data, the reference diagnosis frequency of the first vehicle part in the first time period is determined; the first time period is a preset time period after the current moment; acquiring a target driving route and a target vehicle load of the target vehicle in the first time period; determining a target driving road type and a target driving weather type of the target driving route in the first time period; according to the target vehicle load, the target driving road type and the target driving weather type, the reference diagnosis frequency is adjusted, and the target diagnosis frequency of the first vehicle part is obtained; and diagnosing the first vehicle part according to the target diagnosis frequency to obtain diagnosis information of the first vehicle part. By adopting the embodiment of the invention, the diagnosis efficiency can be improved when the vehicle is diagnosed.

The invention relates to the technical field of subway equipment state monitoring, and discloses a multi-source sensing fusion subway equipment state monitoring method and system, and the system comprises a data collection module, an information fusion module, an equipment state diagnosis module, an early warning execution module and a collaborative management module. The data acquisition module acquires multi-source equipment state original data through a multi-type sensor array; the information fusion module carries out calibration and feature fusion on multi-source equipment state original data to generate fusion equipment state information; the equipment state diagnosis module combines and fuses the equipment state information and the early warning execution data, and adopts a mode matching method to obtain diagnosis data; the early warning execution module controls an early warning device to complete early warning action according to the diagnosis data; and the collaborative management module adjusts parameters of each module to form closed-loop control. According to the system, through multi-source sensing data fusion, accurate diagnosis and collaborative management, comprehensive monitoring of the state of subway equipment is realized, the fault early warning accuracy and processing efficiency are improved, and safe operation of a subway is guaranteed.

An automotive diagnostic method aimed at focusing diagnostic data retrieval and analysis on a possible fault condition includes receiving vehicle data from a vehicle at a data acquisition and transfer device (DAT) and analyzing the vehicle data to identify a possible fault condition. A drive cycle associated with the identified possible fault condition is identified, and driving conditions are monitored to identify completion of the identified drive cycle. Upon completion of the identified drive cycle, vehicle data is analyzed to identify an abnormal monitor. Vehicle data is also analyzed to identify an abnormal value of In-Use Monitor Performance Ratio (IUMPR) associated with the abnormal monitor. When an abnormal value of IUMPR is identified, a likely diagnostic condition associated with the identified abnormal IUMPR is further identified.

The invention provides a task execution method based on an intelligent agent, and relates to the technical field of artificial intelligence. The method comprises the steps that an execution plan is generated based on task description of a to-be-processed task, the execution plan comprises a plurality of sub-steps with an execution sequence, and each sub-step has a corresponding sub-step description; aiming at each target step, determining a tool to be called from a preset tool library by utilizing target step description corresponding to the target step, and retrieving reference memory data from the memory data; calling the to-be-called tool in combination with the reference memory data to obtain a tool calling result; and if the tool calling result is invalid, acquiring calling diagnosis data of the tool to be called, updating the execution plan into a new execution plan by utilizing an invalid type in the calling diagnosis data, and executing the task to be processed according to the new execution plan. When the tool calling result is invalid, the execution plan can be flexibly adjusted according to the specific invalid type, normal execution of the task is ensured, and then the success rate of task execution can be increased.

The invention relates to the field of wireless communication networks, and particularly discloses a wireless sensor data transmission and storage method based on distributed nodes, which comprises the following steps: extracting feature vectors of local sensing data by nodes; local structure response field reconstruction is carried out in cooperation with the feature vectors of the adjacent nodes, and the abnormal confidence degree of own data is evaluated; according to the field reconstruction quality and the abnormal confidence coefficient, only a compressed state report or a complete diagnosis data packet is selected to be transmitted intelligently; and the node learns a state evolution trend based on historical data, and autonomously generates and adjusts a future monitoring strategy. According to the method, the defects of high resource consumption, shallow monitoring depth and stiff strategy of a traditional method are overcome, the resource utilization efficiency is greatly improved, the monitoring depth and reliability are remarkably enhanced, and autonomous optimization and prospective operation and maintenance of the system are realized.

The invention provides a vehicle fault after-sales service method and device, a storage medium and electronic equipment, and is applied to the technical field of after-sales services, and the method comprises the steps: obtaining a fault after-sales request sent by diagnosis equipment; when the fault after-sales request is a tire pressure monitoring system fault diagnosis request, vehicle diagnosis data of a vehicle is obtained through diagnosis equipment, and a target after-sales service person is determined in all after-sales service persons through a recommendation algorithm; and pushing the fault after-sales request and the vehicle diagnosis data to a target after-sales service worker, so that the after-sales service worker uses the vehicle diagnosis data to remotely assist a customer in performing tire pressure detection system fault diagnosis on the vehicle. The fault after-sales request is sent through the diagnosis equipment, the after-sales service system quickly responds to the fault after-sales request, the response time of after-sales service is shortened, vehicle diagnosis data are provided at the first time through the diagnosis equipment, accurate data are provided for after-sales service personnel, the diagnosis work difficulty of the after-sales service personnel is reduced, and the diagnosis accuracy is improved.