369 results about "Diagnostic data" patented technology

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.

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.

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

The disclosure provides a method for constructing a knowledge graph for vehicle fault diagnosis. The method can comprise creating a database of vehicle components; creating a database of abnormal signals; creating a fault knowledge triple from existing unstructured fault diagnostic data and semi-structured fault diagnostic data; and associating the abnormal signal, the vehicle component and the repair suggestion based on the fault knowledge triples to construct the knowledge graph for vehicle fault diagnosis.

The invention relates to the technical field of multi-source heterogeneous data analysis, in particular to a multi-source heterogeneous data-oriented adaptive analysis model construction method and system, and the method comprises the steps: collecting a multi-source heterogeneous data family of a plurality of bolts, and screening alternative diagnosis data to generate a multi-modal key feature vector; judging whether the multi-modal key feature vector is abnormal or not based on the multi-modal key feature vector and the clustering feature vector; obtaining an abnormal multi-modal key feature vector, and generating a fusion feature vector based on a deep learning model; generating an initial diagnosis result based on the fused feature vector; comparing the diagnosis accuracy with a preset threshold value, and judging whether to trigger deep learning model retraining or not; and correcting the attention center of gravity based on the error feature vector and the clustering feature vector, and generating a confidence diagnosis result to position abnormal dimensions and abnormal data. According to the method, the multi-source heterogeneous data analysis efficiency and the anomaly detection accuracy in the bolt tightening process of the battery pack production line are improved.

The invention provides a mental disorder aided decision-making method and system based on multi-modal data, and belongs to the technical field of disease aided decision-making, the method is applied to a system comprising a data acquisition module, a preliminary screening module and an aided decision-making module, and the method specifically comprises the following steps: preprocessing the multi-modal data of a patient; in combination with the mental disorder risk level of the patient of the preliminary screening model, decision assistance is triggered according to the mental disorder risk level, or corresponding decision suggestions are matched and output; when decision assistance is triggered, the disease classification probability and severity are obtained through a diagnosis model based on a cross-modal attention mechanism, meanwhile, according to a time-dependent risk prediction model, a survival probability curve of a recurrence risk is generated in combination with historical diagnosis data of a patient, and decision assistance suggestions are determined by integrating outputs of the two models. According to the method, on the basis of multi-modal data, multiple types of intelligent models are fused, objective data support is provided in the assessment and intervention process, and the missed diagnosis and misdiagnosis risks of mental disorders are reduced.

The invention provides a closed-loop reinforcement learning diagnosis method and system based on a high-fidelity virtual clinical environment, and the method comprises the steps: 1, carrying out the structural processing of basic information and an examination record set of a patient, and obtaining a standardized examination and diagnosis data set; 2, training to obtain a high-fidelity clinical environment system; and step 3, based on the high-fidelity clinical environment system, obtaining a diagnosis agent through reinforcement learning training. A simulation examination result highly consistent with the state of a patient can be generated based on real electronic health record data, a diagnosis agent performs multiple rounds of interaction in a virtual environment, autonomously explores a diagnosis path, and performs strategy optimization through a double-reward mechanism, so that the crossing of a diagnosis model from static prediction to dynamic decision is realized, and the diagnosis efficiency is improved. And the diagnosis accuracy and the reasonability of examination recommendation are greatly improved.

A diagnostic system for assessing engine wear utilizes data from sensors in a vehicle engine compartment and electronic data from a vehicle communications network to provide a comprehensive diagnostic evaluation. The system includes a sensor device with an audio sensor for capturing engine sound, an accelerometer for detecting vibration, and a near-field communication (NFC) tag for sensor information. The sensor device communicates sound and vibration data to a remote server, while a data acquisition and transfer (DAT) device connected to the vehicle's diagnostic port transmits engine performance metrics. A Dynamic Time Warping (DTW) algorithm aligns time-series data from the sensor and vehicle metrics to identify engine problems. The system supports cloud-based diagnostics and enables optimal sensor placement using geo-location or positional data. Applications include smartphone integration, Wi-Fi 6 communication for high-speed data transfer, and scan modes for varying diagnostic detail levels.

The invention belongs to the technical field of fault detection and recovery, and particularly relates to a fault self-diagnosis and self-adaptive recovery method for an embedded controller, which comprises the following steps of: starting a fault self-diagnosis process by adopting a periodic triggering mode and an event triggering mode, and acquiring, processing and generating a diagnosis data source with time sequence characteristics in real time; step-by-step anomaly recognition is carried out through a three-level anomaly recognition mechanism, an anomaly feature set is extracted, cooperative fault positioning is carried out in combination with a weighted cosine similarity algorithm and fault tree analysis, fault types and severity levels are judged, and a fault positioning report is generated; the optimal self-adaptive recovery strategy is matched to carry out preliminary recovery, the strategy is divided into light, medium and deep levels according to serious levels and is provided with priorities, and the scheme with the minimum influence on the core service is preferentially selected; the recovery effect is verified through multi-dimensional comparison, and processing is carried out according to the recovery effect. According to the method, the real-time accurate diagnosis, intelligent positioning and self-adaptive recovery of the fault of the embedded controller are realized, and the reliability and self-healing capability of the system are improved.

A method including: interpreting a user input; generating, based on the user input, a first agnostic vehicle diagnostic command; translating the first agnostic vehicle diagnostic command to a first adapted vehicle diagnostic command; interpreting a first adapted vehicle diagnostic data generated in response to executing the first adapted vehicle diagnostic command; translating the first adapted vehicle diagnostic data to first agnostic vehicle diagnostic data; generating a second agnostic vehicle diagnostic command based at least in part on the first adapted vehicle diagnostic data; translating the second agnostic vehicle diagnostic command to a second adapted vehicle diagnostic command; interpreting second adapted vehicle diagnostic data generated in response to executing the second adapted vehicle diagnostic command; translating the second adapted vehicle diagnostic data to second agnostic vehicle diagnostic data; and generating a state value for the vehicle based at least in part on the second adapted vehicle diagnostic data.

Approaches are described for facilitating remote ophthalmic examinations using three-dimensional (3D) imaging and mixed reality technology. A system obtains real-time or stored 3D data of a patient's eye, capturing detailed anatomical structures. The system analyzes the 3D data to identify specific regions of the eye, such as the cornea or retina, and retrieves corresponding diagnostic data and patient-specific clinical information. The 3D data, diagnostic metrics, and clinical records are integrated to generate an interactive visualization, which is presented through a mixed reality interface. The system allows healthcare professionals to manipulate diagnostic overlays, investigate flagged abnormalities, and adjust the visualization using gesture-based inputs. Machine learning models may be applied to detect potential abnormalities in the eye, while the system also determines stages of the examination based on changes in the anatomical structure of the eye.

The application discloses a diagnosis data processing method and device and a storage medium, relates to the technical field of data processing, and comprises the following steps: acquiring a heart-lung auscultation sound signal collected by a collection device and a traditional Chinese medicine (TCM) diagnosis standard description received based on an interactive interface; determining a western medicine quantitative diagnosis index according to the heart-lung auscultation sound signal, and correlating a TCM syndrome differentiation type according to the TCM diagnosis standard description and the western medicine quantitative diagnosis index; and generating and outputting a TCM medication recommendation scheme corresponding to a diagnosis result according to the western medicine quantitative diagnosis index and / or the TCM syndrome differentiation type. The application provides strong objective evidence and quantitative reference by differentiating the TCM syndrome differentiation type according to the auscultation sound. When diagnosing and treating diseases, the TCM highly depends on and integrates the auscultation result, and takes the auscultation result as one of key bases for differentiation, prescription selection, treatment effect evaluation and prognosis.

1. Name of the product in this design: Surge arrester diagnostic instrument controller (10kV). 2. Purpose of this design: To control a surge arrester diagnostic instrument and convert diagnostic data into diagnostic information that can be evaluated. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

Method for predicting a heating effect of an imaging sequence of a second imaging modality on a detector of a first modality (12) of a combined imaging device (10) as a function of a reference imaging sequence of the second imaging modality, wherein the first modality (12) is designed to acquire diagnostic data of an object of investigation (15) and the second imaging modality comprises a magnetic resonance imaging device (11) designed to acquire magnetic resonance image data of the object of investigation (15), wherein the first modality (12) comprises a temperature compensation unit (59) designed to compensate for a temperature change of the detector, which comprises the following steps: • Performing (S2) a reference imaging sequence with the second imaging modality, wherein in the reference imaging sequence a gradient magnetic field and / or a high-frequency electromagnetic field is applied to the image acquisition area of ​​the combined imaging device (10) and wherein the reference imaging sequence has several imaging parameters that determine a property of the gradient magnetic field and / or the high-frequency electromagnetic field, • Detection (S3) of at least one temperature-dependent parameter of the detector of the first modality (12), • Determining (S4) the heating effect on the detector of the first modality (12) as a function of several imaging parameters of the reference imaging sequence and the at least one temperature-dependent parameter of the detector, and • Prediction (S7) of the heating effect of the imaging sequence on the detector using a smart algorithm, wherein the smart algorithm is designed to predict the heating effect depending on several imaging parameters of the imaging sequence and data acquired by the reference imaging sequence.

This invention provides a method, apparatus, device, and storage medium for fault diagnosis of rotating components of a crane, relating to the field of mechanical fault diagnosis technology. The method includes: processing source and target domain data of the crane's rotating components in a unified format to output a normalized vibration signal; aligning feature distributions through a shared encoder and adversarial training to output domain-invariant features; constructing conditional embedding vectors based on the domain-invariant features and generating target domain fault data using a diffusion model; combining the generated fault data with real data to form a diagnostic dataset, extracting multimodal features, fusing them, and outputting the fault diagnosis result through a classifier. This invention effectively solves the problem of scarce crane fault data through a diffusion model and, combined with multimodal feature fusion technology, improves the accuracy, robustness, and cross-domain generalization ability of fault diagnosis.

The invention provides a diagnosis method and device for a rotary transformer, electronic equipment and a storage medium, and the method comprises the steps: collecting preset diagnosis data in a soft decoding process of the rotary transformer, carrying out the real-time verification of the diagnosis data, obtaining a verification result corresponding to each fault type, and carrying out the real-time verification of the verification result. And finally, generating a diagnosis result of the rotary transformer according to inspection results corresponding to all fault categories, thereby realizing acquisition of signals of the whole rotary transformer and monitoring of a soft decoding process.

The invention discloses a vehicle after-sales fault diagnosis data storage method and device, a server and a product, and relates to the technical field of data storage. The method is applied to a server, and specifically comprises the steps that when diagnosis equipment completes fault diagnosis of a vehicle, a fault diagnosis data packet sent by the diagnosis equipment is received, and the fault diagnosis data packet comprises original diagnosis data, diagnosis context information and maintenance operation records associated with the vehicle; analyzing the original diagnosis data to obtain target diagnosis data with clear semantics; performing structured processing on the diagnosis context information to obtain diagnosis event metadata; and storing the target diagnosis data, the diagnosis event metadata and the maintenance operation record in a preset database in a layered manner. According to the method and the device, the security and reliability of data storage can be improved, and the subsequent data retrieval efficiency is improved.

The present application relates to the technical field of retail management, and particularly refers to a lean operation model for clothing retail stores from the perspective of data empowerment. The model comprises a four-dimensional health degree evaluation module, a data report generation module, a store manager interview consensus module and a closed-loop execution review module. The four-dimensional health degree evaluation module constructs a mathematical model based on four dimensions of sales efficiency, human efficiency quality, member value and product efficiency and their subordinate indicators, and outputs an overall health degree score OHS to realize quality diagnosis of store operation. The data report generation module generates five key business reports to convert raw data into visual insights. The store manager interview consensus module realizes the integration of data and experience through a structured eight-step process and generates a SMART rectification plan. The closed-loop execution review module realizes the PDCA cycle through an agile review mechanism. The present application solves problems such as low operation efficiency, decision-making relying on experience and insufficient rectification execution of clothing retail stores through the deep integration of data-driven and lean operation.

The invention relates to the technical field of vehicle diagnosis, and discloses a multi-core chip, a vehicle diagnosis method and a vehicle diagnostic agent.The multi-core chip comprises a first kernel, a second kernel and a third kernel, the first kernel is used for establishing encrypted communication connection with a mobile terminal, and the second kernel is used for establishing encrypted communication connection with the mobile terminal; receiving a configuration data packet required by vehicle diagnosis from the mobile terminal; the second kernel is used for sending a diagnosis instruction to the vehicle-mounted control unit based on the configuration data packet, collecting diagnosis data as diagnosed information, and returning the diagnosed information to the mobile terminal through the first kernel; and the third kernel is used for establishing a bidirectional encryption channel between the first kernel and the second kernel by using the first communication key distributed to the first kernel and the second communication key distributed to the second kernel. The safety, the integration level and the communication efficiency of the vehicle diagnosis system are remarkably improved.

A battery diagnosis device according to an embodiment of the present invention may comprise: at least one processor; and memory that stores at least one command that is executed through the at least one processor. Here, the at least one command may comprise: a command for acquiring state data including a state value of each of factors for a plurality of batteries; a command for deriving regression lines indicating relationships between the factors by using the state data; a command for calculating coordinates corresponding to the state values of the factors and distances between the regression lines; and a command for inputting diagnostic data including the distances into a pre-trained diagnostic model, and determining an abnormal battery among the batteries by using a diagnostic result output by the diagnostic model.

The application discloses a remote control torch ignition system based on wireless instruction and fault self-diagnosis function and relates to the technical field of space propulsion. The application comprises a remote control center and a field control unit. The remote control center is used for generating an encrypted ignition instruction package containing an ignition instruction sequence and adjustable parameters and sending the package through a wireless data link, simultaneously receiving and displaying control feedback and diagnosis data from the field. The field control unit is arranged on an engine test stand and is electrically connected with a torch igniter and its matched fuel valve, oxidant valve and ignition power supply, used for receiving, decrypting and executing the encrypted ignition instruction package to control the corresponding valves and ignition power supply to act in sequence. The system further comprises a multi-dimensional data acquisition module used for synchronously acquiring multi-channel physical quantity time sequence signals reflecting the working state of the igniter during the ignition process. Through embedded multi-dimensional time sequence fusion diagnosis, the application can quantitatively evaluate the performance gradual state of the torch igniter in real time.

A vehicle diagnostic data acquisition method, applied to a diagnostic device (10). The diagnostic device (10) is connected to a communication device (20), the communication device (20) comprises a communication interface and at least two types of first communication modules (23), the diagnostic device (10) comprises at least two types of second communication modules (13), the communication device (20) is connected to an electronic control unit of a vehicle by means of the communication interface, the communication interface has a plurality of sets of pin pairs, and the electronic control unit sends diagnostic data to the pin pairs of the communication interface. The vehicle diagnostic data acquisition method comprises: sending pin communication parameters to the communication device (20), the pin communication parameters comprising first identification codes of target pin pairs and first communication mode parameters corresponding to the first identification codes (S100); and receiving diagnostic data of the target pin pairs by means of a second communication module (13) corresponding to the first communication mode parameters (S200). In the vehicle diagnostic data acquisition method, the at least two types of communication modules of the diagnostic device (10) and the communication device (20) concurrently transmit and acquire the diagnostic data, thereby improving the data acquisition efficiency and reducing the waiting time.

The application is suitable for the technical field of vehicle electronic diagnosis, and provides a vehicle dynamic diagnosis method and device based on cache data, a terminal equipment and a storage medium, which comprises the following steps: obtaining a vehicle model configuration code corresponding to a vehicle to be diagnosed; wherein the vehicle model configuration code is used to represent the vehicle model version and configuration information of the vehicle to be diagnosed; in the case that the diagnosis cache data of the vehicle to be diagnosed is stored locally, obtaining the first vehicle model configuration information corresponding to the vehicle to be diagnosed from the diagnosis cache data according to the vehicle model configuration code; wherein the diagnosis cache data is used to represent a diagnosis data set corresponding to all configurations of the vehicle to be diagnosed; determining the first diagnosis data of the vehicle to be diagnosed from the diagnosis cache data according to the first vehicle model configuration information; and executing the first diagnosis data to diagnose the vehicle to be diagnosed. The above method can effectively improve the diagnosis efficiency of the vehicle and meet the actual needs of efficient automobile diagnosis.

The application discloses a kind of based on graph coding's diagnosis and surgical matching check method and system, the method includes: obtaining the diagnosis data and surgical plan data of target object;Based on feature extraction algorithm, extract the diagnosis change feature and surgical step feature in the diagnosis data and the surgical plan data;Based on graph network coding algorithm, the diagnosis change feature and surgical step feature are encoded into corresponding node relationship graph;The node relationship graph is input into trained graph neural network to obtain the matching degree parameter of output diagnosis and surgical plan.It can be seen that the present application can realize accurate medical diagnosis and surgery matching evaluation based on graph structure feature association, improve the scientificity and adaptability of surgical plan selection, reduce the surgical risk and treatment deviation caused by mismatch between scheme and diagnosis.

The application provides a power circuit, a fault diagnosis method and a power electronic device, and relates to the technical field of power electronics. The technical scheme provided by the application can analyze the fault type based on the diagnosis data of the diagnosis voltage within the preset time before and after the moment when the IGBT fails, so that the fault type of the power circuit can be quickly and accurately located, and the fault maintenance cost of the power electronic device is effectively reduced. Moreover, the power circuit provided by the embodiment of the application has a simple structure and no use limit, and is suitable for different types of power scenarios.

To transmit self-diagnostic data without obstructing the transmission of sensor data.SOLUTION: In the field communication system 1, each of the first field device 40-1, the second field device 40-2,., and the N-th field device 40-N includes a sensor, transmits sensor information, performs self-diagnosis on the field device, and transmits self-diagnosis information, the relay communication device 10 receives the sensor information and the self-diagnosis information and transmits the sensor information and the self-diagnosis information via different communication paths, and the first communication device 20 receives the sensor information via the first communication path P1. The second communication device 30 receives the self-diagnosis information through the second communication path P2.SELECTED DRAWING: Figure 1

The application discloses a communication fault root cause intelligent diagnosis positioning system and method, and belongs to the technical field of communication, comprising: a collection module, a feature extraction module, an abnormality recognition module and a diagnosis module; the collection module is used for acquiring the topological structure diagram of a target network, and constructing a diagnosis data set; the feature extraction module is used for classifying and normalizing the diagnosis data set, combining feature extraction and topological association labeling, and constructing a diagnosis feature vector; the abnormality recognition module is used for performing multi-channel parallel abnormality recognition on the diagnosis feature vector, performing confidence filtering and association aggregation on the abnormality recognized by each channel, and constructing an abnormal event list; the diagnosis module is used for constructing a fault propagation graph, labeling abnormal events, tracing an abnormal propagation path along the fault propagation graph in a reverse direction through a neural network algorithm, generating a root cause candidate set, performing confidence scoring on the root cause candidate by calculating a deviation abnormality degree, and screening a root cause diagnosis result.