262 results about "Diagnostic technology" patented technology

This invention discloses a self-learning method and system for motor operation status and fault early warning, relating to the field of motor fault diagnosis technology. It includes the acquisition and standardized processing of multi-source operating data to generate a standardized feature set; a four-level self-learning model deployed at the edge and cloud levels, progressively completing sample mining, fault feature learning, early warning threshold optimization, and maintenance strategy generation, with bidirectional feedback iteration between each level; and master-slave collaboration between the edge master module and the cloud slave module to achieve real-time inference and accurate diagnosis, independent early warning at the edge in case of network anomalies, and synchronization of data and parameters after recovery. This self-learning method and system for motor operation status and fault early warning adopts a four-layer architecture: perception layer – edge master module – cloud slave module – application layer. Through the collaborative cooperation of real-time inference by the edge master module, accurate diagnosis by the cloud slave module, and dynamic optimization by the intelligent maintenance decision module, it achieves early warning of faults throughout the motor's entire lifecycle, prediction of remaining lifespan, and adaptive maintenance decision-making.

The present disclosure relates to a calm expiration phase, forced vital capacity determination and diagnosis method, system and dynamic X-ray machine, and relates to the technical field of calm expiration phase, forced vital capacity determination and diagnosis. The calm expiration phase determination method comprises: performing lung field segmentation on dynamic multiple X-ray two-dimensional chest images in the breathing process to obtain corresponding multiple X-ray two-dimensional lung field mask images; and determining the calm expiration phase in the breathing process according to multiple lung field mask areas of the multiple X-ray two-dimensional lung field mask images and lung field mask area change rates corresponding to the multiple lung field mask areas. The present disclosure can realize calm expiration phase, forced vital capacity determination and diagnosis.

The application discloses a kind of intelligent diagnosis system of diabetic eye disease based on multi-model cluster and cause-effect enhanced LLM cooperation, and it is related to artificial intelligence medical auxiliary diagnosis technical field.The system includes modular detection cluster layer, microaneurysm, bleeding point, exudate, neovascular detection is carried out to input fundus image simultaneously, and the visual feature corresponding to diabetic eye disease is output;Medical text modal alignment layer, the diabetic eye disease visual feature output by modular detection cluster layer is converted into standardized medical description;Large language model diagnosis layer, according to the standardized medical description output by medical text modal alignment layer, combined with the construction of standardized medical description knowledge base system, the input fundus image is carried out diabetic eye disease diagnosis reasoning, and structured diagnostic report is generated.The system has the advantages of high overall detection accuracy, knowledge evolution, diagnosis interpretability, output standardization and the like.

The application discloses a kind of thyroid boundary smoothness detection system and method, it is related to medical image processing and medical auxiliary diagnosis technical field, including the following steps: the image after ultrasonic imaging is analyzed, the imaging feature data in it are extracted, and feature data set is constructed after pre-processing;Using feature engineering technology, from the data set after pre-processing, the key feature index reflecting the potential overlap situation of high-intensity edge region in image is extracted, and the key index extracted is comprehensively analyzed, and the degree of edge overlap in image is quantified.The application intelligently identifies high-intensity overlapping edge region and dynamically adjusts feature contribution, effectively suppresses the interference of ultrasonic artifact on boundary extraction, avoids boundary overlap, self-intersection and deformation, ensures that the extracted boundary has good continuity and smoothness, and provides reliable boundary basis for smoothness quantification analysis and lesion diagnosis.

This invention discloses a dynamic method for determining the stability of support structures in deep foundation pit construction, relating to the field of engineering structure monitoring and diagnosis technology. The method includes: collecting multi-source heterogeneous data of the deep foundation pit support structure for preprocessing to generate a complete reconstruction sequence; constructing stability labels and combining them with the complete reconstruction sequence to build training samples, constructing a master decision model, using the training samples as input to the master decision model, outputting a predicted probability distribution, and selecting a final decision label; obtaining auxiliary labels based on the final decision label, and performing label consistency determination to obtain consistent-inconsistent results; then, based on the inconsistent results, extracting predicted probabilities for penalty correction, and performing label credibility-untrustworthiness determination to obtain credible-untrustworthiness results. This invention improves the accuracy, robustness, and engineering feasibility of determining the stability of support structures.

The objective is to provide image diagnostic technology that utilizes machine learning models. [Solution] One aspect of the present disclosure relates to a machine learning model trained using training data that includes first ultrasonic image data based on a received signal received by an ultrasonic transducer, first ground truth data which is first region information associated with a detection target of the first ultrasonic image data, and second ground truth data which is either first location information associated with a detection target of the first ultrasonic image data, or second region information based on the first location information.

This invention relates to the field of industrial equipment fault diagnosis technology, specifically to a knowledge graph-embedded method and system for causal tracing of industrial equipment events. The method includes the following steps: collecting multi-source heterogeneous event data during the operation of industrial equipment; preprocessing and extracting features from the collected multi-source heterogeneous event data to obtain event feature vectors. This invention constructs a knowledge graph in the industrial equipment domain, structurally representing the complex mapping relationships between entities such as equipment type, fault type, fault cause, and fault result, providing rich semantic prior knowledge for subsequent causal inference. Compared to traditional data-driven fault diagnosis methods, the knowledge graph introduced in this invention can encode domain experts' in-depth understanding of equipment fault mechanisms, making the tracing results more accurate and interpretable.

This application discloses a user-side power safety diagnostic system and method based on multi-channel communication, belonging to the field of power equipment operation and maintenance and intelligent diagnostic technology. The system includes a multi-modal acquisition module, a time synchronization module, an edge processing module, a multi-channel communication module, a caching and retransmission module, and a cloud diagnostic module. These modules work together to form an architecture of real-time processing on the end side, in-depth analysis on the cloud, and multi-link collaborative transmission. The system achieves full-process diagnosis through multi-modal data acquisition and time synchronization, edge preprocessing and lightweight inference, multi-channel dynamic transmission, data caching and retransmission, and in-depth cloud diagnostics. This invention adopts a multi-channel redundant communication and dynamic switching strategy, combined with end-cloud model collaborative inference, to ensure stable and complete data transmission in complex scenarios, significantly improving the accuracy of hidden danger identification and real-time diagnostics. It can output fault type, causal chain, and risk level assessment, and is applicable to power equipment safety diagnosis in multiple scenarios such as substations and transmission lines.

This invention relates to the field of industrial equipment diagnostic technology and discloses a method for fault diagnosis and tracing of industrial equipment based on multi-source heterogeneous parameter fusion and deep learning networks. The method includes: acquiring a multi-dimensional heterogeneous signal sequence representing the operating state of the controlled object; mapping the signal sequence into a two-dimensional spatial image tensor using the Gram angle field algorithm; constructing a dual-stream feature extraction network containing graph convolution branches and cyclic unit branches to extract spatial topological features and temporal evolution features respectively; determining the logical offset matrix based on the spatial flow feature gradient divergence, and resampling the temporal flow features accordingly to achieve spatiotemporal causal phase alignment, and outputting diagnostic analysis results. This invention compensates for the physical system response hysteresis through asynchronous resampling operators in the computational domain, eliminates feature phase contamination caused by conventional rigid alignment, enhances the model's ability to capture weak abnormal signals, and achieves causal consistency in fault root cause localization.

The present application relates to a kind of water temperature change diagnosis systems based on deep reinforcement learning, belong to the technical field of automated monitoring and diagnosis, the system includes: multidimensional data collaborative acquisition module;Time series feature deep evolution module;Reinforcement learning diagnosis decision module;Dynamic reward feedback evaluation module;Abnormal root cause tracing execution module;Thermodynamic physics consistency check unit;Safety constraint layer;Self-adapting working condition identification function unit;Historical trend comparison unit;Meta-learning unit;Digital twin verification unit.The present application builds the physical guided reinforcement learning diagnosis framework and man-machine collaborative closed-loop incremental learning architecture by root cause contribution feedback closed loop, physical consistency forced check and confidence entropy trigger artificial intervention and priority experience feedback mechanism, significantly improves the capture accuracy of water temperature fluctuation under complex working condition and diagnosis real-time, and provides clear physical basis for diagnosis result.

This application discloses a control method, apparatus, medium, and automotive diagnostic equipment for a diagnostic session, relating to the field of vehicle diagnostic technology. The method includes: when initiating a diagnostic session, obtaining at least one diagnostic task and ECU identifier associated with the diagnostic session; for each diagnostic task, querying the associated data block size in a preset mapping table based on its task identifier; sending a negotiation request message carrying the data block size to the target ECU; and, upon receiving a positive response message, continuously sending diagnostic task data to the target ECU using the data block size. This application overcomes the problem that traditional fixed block size modes cannot accommodate the differentiated requirements of different tasks in terms of throughput, real-time performance, and security by intelligently associating the data block size with the diagnostic task type and implementing dynamic negotiation, significantly improving diagnostic efficiency, bus utilization, and system adaptability.

This invention relates to the field of bearing fault diagnosis technology, specifically to a bearing fault diagnosis method based on neural networks and multi-criteria preference consensus. The method includes: data acquisition and feature extraction, data preprocessing, matching a fault diagnosis model, preliminary diagnosis and probability generation, final diagnosis and probability generation, and bearing state decision-making. This invention provides a multivariate decision-making auxiliary model that integrates physical marginal value functions and neural network attention mechanisms, combined with a reinforcement learning-driven consensus-building process, to achieve adaptive optimization of multi-expert fault diagnosis results. This not only solves the problems of existing bearing fault diagnosis methods being unable to handle complex working conditions, lacking physical interpretability, relying heavily on single decision-making patterns, and using static weight allocation, but also improves the accuracy, robustness, and interpretability of bearing fault diagnosis, achieving high-precision, low-round group fault diagnosis.

This invention discloses a battery health diagnosis method based on multimodal image fusion, relating to the field of battery health diagnosis technology. The method includes the following steps: simultaneously acquiring visible light and infrared thermal imaging images of the battery, and dividing the battery into local diagnostic units based on prior data; extracting visible light and infrared thermal imaging feature vectors from the local diagnostic units, and generating cross-modal consistency constraints by calling a pre-constructed cross-modal consistency constraint model; calculating the bimodal correlation degree of each unit by combining the feature vectors and cross-modal constraints to determine the unit failure mode; when the failure mode is a high-confidence failure, analyzing its spatial expansion trend using a spatial clustering algorithm based on graph connectivity; and generating a battery health diagnosis result by combining the unit failure mode and spatial expansion trend. This invention significantly reduces the impact of environmental interference and occasional anomalies through collaborative analysis and consistency constraints of multimodal images.

This application discloses a reaction control device for a centrifugal microfluidic chip, relating to the field of in vitro diagnostic technology. The device includes: a chip holder for mounting the centrifugal microfluidic chip, which can accommodate a sample to be tested; a drive module connected to the chip holder and capable of rotating the chip holder; a heating and temperature control module connected to or located on one side of the chip holder, capable of controlling the temperature of the centrifugal microfluidic chip on the chip holder; a fluorescence detection module located below the chip holder, used to detect the reaction of the sample to be tested, wherein the chip holder has fluorescence detection holes adapted to the fluorescence detection module; and a magnetic bead manipulation module adapted to the heating and temperature control module, used to assist in the processing of the sample to be tested. This application achieves integrated operation of the centrifugal microfluidic chip from sample processing and reaction control to result detection through the organic integration and coordinated control of multiple modules.

This invention provides a transformer fault diagnosis method integrating GAF-MViT V3 and OGJO, belonging to the field of transformer fault diagnosis technology. First, Gram angle field (GAF) is used to transform the one-dimensional DGA data of the transformer into two-dimensional GASF and GADF images, respectively representing the global trend and local abrupt change characteristics of the signal. Second, a dual-branch network based on MobileViT V3 is constructed. The GASF branch uses the CNN branch of MobileViT V3 to extract global low-frequency features, while the GADF branch uses the Transformer branch to capture local high-frequency features. Dynamic fusion of the two types of features is achieved through a feature recombination module. Finally, the improved OGJO algorithm with a reverse learning strategy is used to globally optimize the hyperparameters, improving the model's generalization ability. The t-SNE algorithm is used to visualize the fused features, enabling interpretable analysis of the diagnostic results.

The application discloses a kind of intelligent diagnosis system of diabetic eye disease based on multi-model cluster and cause-effect enhanced LLM cooperation, and it is related to artificial intelligence medical auxiliary diagnosis technical field.The system includes modular detection cluster layer, microaneurysm, bleeding point, exudate, neovascular detection is carried out to input fundus image simultaneously, and the visual feature corresponding to diabetic eye disease is output;Medical text modal alignment layer, the diabetic eye disease visual feature output by modular detection cluster layer is converted into standardized medical description;Large language model diagnosis layer, according to the standardized medical description output by medical text modal alignment layer, combined with the construction of standardized medical description knowledge base system, the input fundus image is diagnosed and reasoned to diabetic eye disease, and structured diagnostic report is generated.The system has the advantages of high overall detection accuracy, knowledge evolution, diagnosis interpretability, output standardization and the like.

This invention discloses a quantitative analysis system and method for overlapping gas components, belonging to the field of gas analysis and vacuum diagnostics technology. The system comprises: a vacuum chamber to be tested, a first valve connection, and a differential device connected in sequence; the differential device is connected to a mass spectrometry detection unit, a spectral detection unit, and a vacuum pump unit; the mass spectrometry detection unit is used to acquire ion current signals of the analyte gas in multiple mass-to-charge ratio channels; the spectral detection unit is used to acquire characteristic spectral signals of the analyte gas in multiple wavelength channels; a data processing and calculation unit is communicatively connected to the mass spectrometry detection unit and the spectral detection unit, respectively, to obtain and output the partial pressure or concentration of each candidate gas component. Overlapping gas components are those gas components that are difficult to accurately distinguish in mass spectrometry detection due to overlapping mass-to-charge ratio channel responses or overlapping or intersecting characteristic spectral lines in spectral detection. This invention improves the identification capability and quantitative accuracy of overlapping components.

The application relates to the technical field of intelligent diagnosis of vehicle-mounted operating systems, in particular to an intelligent diagnosis method and device for a vehicle-mounted operating system and a vehicle, wherein the method comprises the following steps: obtaining historical diagnosis data of the vehicle-mounted operating system, wherein the historical diagnosis data comprises at least one of historical logs, fault diagnosis results and a diagnosis rule library; performing block vectorization on the historical diagnosis data, constructing a vector database and storing the vector database in a local knowledge base, and searching current log data based on the knowledge base to obtain a search result; generating a training data set according to the fault diagnosis data and fine-tuning a large language model. The current log data and a target diagnosis task are input into the fine-tuned large language model, the fine-tuned large language model calls the local knowledge base to execute the diagnosis task, and a fault diagnosis result is generated based on the search result. Therefore, the problems that the fault diagnosis of the vehicle-mounted operating system in the related art depends on artificial experience or fixed rules, cannot dynamically fuse and intelligently infer, and is low in accuracy are solved.

The present application belongs to the technical field of medical signal processing and neural electrophysiological diagnosis, and particularly relates to a method for establishing an epilepsy diagnosis system based on time-varying brain network and evoked potential regulation. The present application aims to solve the problems that the prior art is difficult to capture the dynamic causal flow of neural activity, cannot accurately depict the transient reorganization characteristics of brain regions of juvenile myoclonic epilepsy (JME) patients, and lacks multi-modal feature integration analysis. The present application comprises the following steps: collecting whole brain electroencephalogram signals; extracting TMS evoked potential waveform after preprocessing; constructing a time-varying adaptive directional transfer function matrix; screening a time-varying brain network visualization atlas; performing baseline correction and superimposed averaging on the evoked potential; identifying the differences in connection and potential between JME patients and healthy controls and different clinical subgroups; and finally integrating the whole process to establish a standardized automated analysis system and a structured database. Through the method, the joint quantification of cortical excitability and time-varying causal connection is realized, and the objectivity, sensitivity and mechanism analysis ability of epilepsy diagnosis are significantly improved.

This invention relates to the field of combustion diagnostic technology, specifically to a combustion diagnostic method and system based on a vector light field. The system includes a spectrometer and, sequentially arranged components: a femtosecond laser, a beam-expanding lens group, a spatial light modulator, a first lens, a filter, a quarter-wave plate, a second lens, a Rochi grating, a third lens, and an imaging unit. The spatial light modulator, first lens, second lens, and Rochi grating form a 4f optical system. The spatial light modulator is used to load a holographic grating to phase-modulate the expanded femtosecond laser beam, thereby forming a vector light field. The filter is used to filter out the ±1st order diffraction light of the femtosecond laser modulated by the spatial light modulator. The imaging unit is used to acquire images of the optical filament, and the spectrometer is used to acquire the spectral signal of the interaction between the optical filament and the combustible material, thereby achieving combustion diagnosis. This invention forms the optical filament based on a vector light field, improving the stability of the optical filament and providing a foundation for improving the accuracy of combustion diagnosis.

This invention relates to the field of cardiovascular disease auxiliary diagnosis technology, specifically to a method and system for auxiliary diagnosis of high-altitude hypertension based on electrocardiogram (ECG). It uses 12-lead ECG images as input, constructs a multi-branch adaptive classification model that integrates regional priors, sequentially extracts shallow general features through a shared feature extraction module, combines independent feature branches from multiple leads with a paired spatial attention module to capture relative potential differences between leads, and a multi-path adaptive fusion module dynamically fuses differential features between high-altitude and plain areas using regional labels as gating signals. Finally, a hybrid classifier composed of capsule networks and DenseNet cascades the features to complete a four-class prediction: high-altitude hypertension, high-altitude normal, plain hypertension, and plain normal. This invention effectively overcomes the problem of cross-regional data domain shift, improves the ability to capture weak pathological features in ECG images, and has strong clinical transferability and robustness, making it suitable for auxiliary screening of hypertension across high-altitude and plain areas.

This invention discloses a precise diagnostic system and method for insulation faults in substations based on multi-source signal correlation analysis and spatial positioning. Specifically, it relates to the field of fault diagnosis technology. The method includes capturing partial discharge signals from electrical equipment using monitoring devices, determining the location of insulation faults in the substation's electrical equipment using an ultrasonic sensor array, and adjusting the environmental conditions of the electrical equipment by modifying the fan operating parameters. It also involves constructing an LSTM predictive partial discharge model for the insulation fault location to generate a predicted partial discharge sequence. Furthermore, it involves collecting UHF signals, ultrasonic signals, and transient ground voltage signals, combined with ambient temperature and humidity, to monitor the partial discharge at the spatial location of the insulation fault in real time, determine the objective function of the fan operating parameter optimization algorithm, and diagnose the insulation fault location. This invention helps to optimize the partial discharge phenomenon at the insulation fault location through fan optimization, thereby achieving precise diagnosis of the insulation fault location.

This application relates to the field of surgical diagnostic technology, specifically to a system for adjusting the ratio of intravenous and inhaled anesthetic drugs based on EEG trend-guided analysis. The system generates a standardized sequence set through data acquisition and preprocessing; an association feature construction module extracts association features between drug ratios and EEG trends based on time lag relationships; an influence strength coefficient calculation module combines the ratio structure entropy and EEG fluctuation coefficients to generate an EEG influence strength coefficient; a sensitivity judgment module calculates sensitivity features based on EEG intervals of patients with similar physical signs, combined with real-time fluctuations and influence coefficients; a parameter dynamic adjustment module dynamically optimizes the EEG analysis intervals and trend weights accordingly; and a ratio recommendation generation module generates multiple drug ratio options with expected EEG changes for selection. This system effectively overcomes the shortcomings of static thresholds in adapting to individual differences and intraoperative fluctuations, significantly improving the accuracy of drug ratio recommendations.

The application discloses a power system anomaly treatment method and system based on causal inference, and relates to the technical field of power anomaly diagnosis. The method comprises the following steps: obtaining operation state data and power grid topology data of a target power grid area; based on the operation state data, calculating at least one abnormal index value representing power quality, and comparing the abnormal index value with a preset index threshold to determine whether there is an abnormal power quality; if it is determined that there is an abnormality, determining at least one candidate cause leading to the abnormality based on the operation state data and the power grid topology data; for each candidate cause, calculating a comprehensive scheduling cost required for intervention according to a predefined evaluation rule; selecting a cause subset from a preselected set constituted by the candidate causes; and outputting a treatment action suggestion corresponding to the cause subset. The application automatically selects a treatment measure combination with the optimal comprehensive scheduling cost and generates a treatment suggestion, thereby improving treatment efficiency and reducing resource invalid occupation.

The present application relates to the technical field of medical image processing and intelligent auxiliary diagnosis, and particularly discloses a corneal fungal infection diagnosis method and system based on multi-modal time sequence information, which comprises the following steps: acquiring an image sequence and clinical data; inputting the image sequence and the clinical data into a multi-modal fusion analysis model, which extracts spatial form and space-time dynamic characteristics of the image and a clinical feature vector; fusing the two types of features to generate diagnosis features; and finally outputting a diagnosis result containing mycelium existence determination and judgment basis. The present application has higher diagnosis accuracy, stronger robustness, and good interpretability and interactivity, and simulates and enhances the comprehensive diagnosis thinking process of doctors by fusing space-time dynamic information of the image sequence and patient clinical context.

The application relates to the technical field of line diagnosis, and discloses a distributed line fault diagnosis platform fault prediction method and system. The method comprises the following steps: acquiring dimension-unified electric variable data and environment data in a distributed line to obtain electric variable signal parameters; extracting time-domain statistical features and frequency-domain energy features of the parameters to form an electric variable feature set; adaptively adjusting a fault threshold in historical fault data according to the environment to obtain an electric variable fault dynamic determination threshold; checking the deviation degree of the feature set and the dynamic threshold under a sliding window to obtain a fault feature index; constructing a fault diagnosis classification table according to a warning type template and a fault priority in the historical fault data, and mapping the fault feature index to the fault diagnosis classification table to obtain a fault prediction result; and inputting the fault prediction result into a distributed line fault diagnosis platform to obtain a fault prediction report. The application can improve the efficiency of distributed line fault prediction and report generation.