580 results about "Diagnostic analysis" patented technology

The invention discloses a medical diagnosis intelligent decision-making system based on multi-modal data fusion, and the system comprises the steps: obtaining a medical image, biological features and electronic medical record data of a patient through a patient information collection unit, and extracting feature vectors from different modal data through a feature processing unit; the core of the system is a modal fusion engine, and the module combines a deep neural network and a Bayesian inference engine to realize deep learning and probability-dependent modeling of an association relationship between features; carrying out weight optimization on different modal features by adopting an attention mechanism through a self-adaptive feature optimizer, and generating optimized feature representation; the intelligent diagnosis analysis unit calculates the diagnosis probability based on the optimized features, performs auxiliary diagnosis in combination with a case knowledge base when the confidence coefficient is insufficient, and outputs a diagnosis report containing diagnosis categories, basis and suggestions; according to the method, multi-source heterogeneous data are fused, deep learning and probabilistic reasoning are combined, intelligent medical auxiliary diagnosis is achieved, and diagnosis accuracy and interpretability are improved.

The invention provides a photovoltaic operation fault diagnosis method and system, and the method comprises the steps: constructing a fault mode and influence factor database based on real-time monitoring data and historical record data, carrying out the feature extraction, obtaining a fault feature vector, and constructing a fault diagnosis model through a preset decision algorithm; performing label classification on the fault feature vector to obtain a label classification data set, and constructing a label classification model; constructing a fault propagation model based on the fault mode and the influence factor database; and based on the plurality of models, performing fault diagnosis analysis on the real-time monitoring data to obtain a fault diagnosis report. According to the method, the problem that the operation reliability of a photovoltaic system is poor due to the fact that only processing is carried out when abnormity occurs in the whole operation state and performance expression and further analysis and processing are not carried out on the propagation path and potential influence of the abnormity or the fault in the prior art is solved. The fault condition of the system is diagnosed and analyzed by constructing a plurality of models, and stable operation of the system is ensured.

The invention relates to the technical field of ultra-high performance concrete detection, in particular to an ultra-high performance concrete construction process detection and analysis system based on multi-source data fusion. According to the technical scheme, the system comprises a multi-physics field sensing network, a heterogeneous data fusion engine, an intelligent diagnosis and analysis system and a visual decision support platform, and the multi-physics field sensing network is composed of multi-modal sensors deployed in links of raw materials, stirring, transportation, pouring and maintenance; and cross-scale data acquisition and heterogeneous data fusion engines from nanoscale interface reaction to meter-scale structure morphology are realized. Cross-scale and multi-physics-field comprehensive monitoring is achieved through the multi-physics-field sensing network, efficient data processing and analysis are conducted through the heterogeneous data fusion engine, construction quality dynamic evaluation is conducted through the intelligent diagnosis and analysis system, and scientific decision making and construction management are assisted through the visual decision support platform. Therefore, the construction quality is improved, the construction process is optimized, the construction risk is reduced, and comprehensive guarantee is provided for ultra-high performance concrete construction.

The invention discloses an unmanned aerial vehicle group cooperative inspection control method for power station inspection, and the method comprises the steps: determining an unmanned aerial vehicle group according to a power station inspection task; performing task allocation of the heterogeneous unmanned aerial vehicle cluster by using a multi-objective optimization algorithm; performing unmanned aerial vehicle path planning by adopting an improved dynamic multi-population particle swarm algorithm; based on a graph theory and master-slave control, unmanned aerial vehicle group attitude consistency control is realized; integrating a target detection technology based on deep learning, and constructing an inspection anomaly detection model; performing remote fault diagnosis analysis on the real-time inspection data through an inspection anomaly detection model to obtain an inspection anomaly result; optimizing the inspection anomaly detection model according to the target condition of the real-time inspection data; and dynamically controlling task allocation and path planning of the unmanned aerial vehicle group according to an abnormal inspection result. Through the scheme, potential problems can be found and processed in time, the intelligent control level of power station inspection is improved, and the efficiency and accuracy of unmanned aerial vehicle group cooperative inspection are improved.

The invention relates to the technical field of medical data processing systems, in particular to an artificial intelligence-driven medical diagnosis and treatment data processing method and system. The method comprises the steps that a multi-modal medical data acquisition module acquires and processes multi-source heterogeneous medical data of a patient, and a standardized data set is generated; a medical feature depth extraction module performs multi-dimensional feature extraction on the data set, and constructs a dynamic evolution feature matrix; a multi-dimensional health state space construction module constructs a patient health state multi-dimensional space according to the matrix and determines a key medical early warning index set; the real-time medical data fusion module maps real-time data to the space to generate real-time health risk factors; and the medical risk prediction and decision-making module establishes a personalized model, outputs a disease occurrence probability and generates personalized treatment suggestions. The system solves the problems that medical data processing is difficult, diagnosis analysis is not comprehensive, and a treatment scheme lacks personality, and diagnosis accuracy and treatment pertinence are improved.

The invention discloses a power cable fault diagnosis and analysis method and system based on multi-source data, and relates to the technical field of diagnosis and analys.During operation of the system, various sensors are installed in a cable and the surrounding environment of the cable, operation data of the power cable are collected in real time, and after the collected multi-source data are preprocessed, the power cable fault diagnosis and analysis method and system based on the multi-source data are obtained. The method comprises the following steps: calculating key diagnosis parameters by using a machine learning algorithm, obtaining a comprehensive fault diagnosis index CDI, comparing the comprehensive fault diagnosis index CDI with a preset first threshold M and a preset second threshold N, obtaining a fault level and outputting a corresponding processing strategy, and carrying out fault point positioning by using a reflected wave method in combination with multi-source data according to fault characteristics and a cable model. And outputting a specific position of a fault point, accurately positioning the fault point in the cable, integrating analysis results of the modules, and automatically generating a cable fault diagnosis report which comprises a fault type, a fault point position, potential risk analysis and maintenance suggestions.

The invention discloses a numerical simulation prediction method and system for typhoon binocular wall structure reconstruction judgment and maintenance duration after terrain interference and a medium, and relates to the technical field of typhoon fine structure evolution description and numerical simulation. According to the method, the binocular wall typhoon is identified through multi-source data fusion, a high-resolution numerical simulation system is constructed, and a typhoon structure is divided into four quadrants for differential diagnosis according to the wind shear direction based on vertical wind shear phase limit analysis. And the distribution characteristics of the rapid vortex silking area are captured by calculating vortex silking time parameters. And establishing a ditch sinking airflow detection mechanism, and quantitatively evaluating the contribution of radial advection, tangential advection, vertical advection and friction force items to tangential wind evolution by using tangential wind income and expenditure diagnostic analysis. And calculating an energy growth rate, and predicting a double-eye wall maintaining time length. According to the method, the outer eye wall re-formation judgment is output, the outer eye wall radius zone, the maintenance duration interval and the uncertainty are predicted, and the method is suitable for real-time business and research.

The invention discloses an IMU (Inertial Measurement Unit) fault diagnosis and repair method and device, vehicle-mounted navigation equipment and a storage medium, and the IMU fault diagnosis and repair method comprises the following steps: obtaining IMU time domain data, and analyzing and processing the IMU time domain data to obtain an initial measurement value; performing periodic analysis on the initial measurement value, and performing calculation and statistics to obtain missing measurement points; diagnosing and analyzing the missing measurement points to obtain fault feature description; a fault data mapping relation is established, and fault judgment parameters are obtained through characteristic decomposition; calculating to obtain a fault classification value by using the fault judgment parameter; data accuracy measurement is carried out based on the fault classification value, and a fault degree value is obtained through threshold value dynamic comparison; calculating a correction parameter by using the fault degree value, and obtaining an adjustment sequence through parameter conversion; performing extreme value decomposition, and obtaining a steady-state quantity through waveform recombination; and inputting a quantization equilibrium equation, and performing frequency domain modulation to obtain a final repair result. According to the technical scheme, the fault diagnosis accuracy and the repair comprehensiveness are improved, and the fault can be accurately diagnosed and repaired under the complex working condition.

The invention discloses a battery health diagnosis and analysis method based on big data. The method comprises the following steps of performing real-time monitoring and data acquisition through various sensors and data sources; performing multi-source data fusion based on feature selection of the multi-source data to obtain a key feature set; creating an adaptive deep learning model, and inputting features in the key feature set into the adaptive deep learning model to obtain a battery health state prediction result; generating a battery health score based on the battery health state prediction result and the real-time data of the battery; based on the real-time battery health score, an early warning threshold value is adjusted, a dynamic alarm is generated, and corresponding maintenance suggestions and early warning measures are provided according to different health scores; and continuously optimizing the adaptive deep learning model according to historical battery health data and a model prediction result. According to the method, the data weight is dynamically adjusted based on the data quality, the sensor precision and the influence degree of the sensor precision on health prediction, and more accurate battery health state prediction can be realized.

The invention provides a doctor inquiry assisting method and system based on a large language model, and belongs to the technical field of intelligent medical treatment. According to the scheme, the method comprises the following steps: collecting communication information between a doctor and a patient in an inquiry process, inputting the communication information into a trained large voice model, and obtaining keywords related to the condition of the patient in the communication information; obtaining a diagnosis result of the disease of the patient by using the keyword for reference of a doctor; generating a prescription suggestion for the doctor according to the diagnosis result; according to communication information and prescription data between doctors and patients in the inquiry process, medical records are preliminarily filled in, and the doctors finally confirm the medical records. Through the functions of intelligent guide type inquiry, historical case matching, diagnosis analysis, doctor assistance and the like, comprehensive inquiry assistance and diagnosis support are provided for doctors. The efficiency and accuracy of doctor inquiry can be remarkably improved, and more comprehensive and personalized medical services are provided for patients.

The present invention relates to the field of video image processing technology, and specifically discloses a video image quality diagnosis and analysis detection system and detection method. By extracting images from an input video stream frame by frame and performing preprocessing, the key feature areas in the image are identified based on edge detection and color segmentation algorithms, and consistency features, frame rate stability features, and spatial distribution density features are extracted. By calculating and analyzing these features, a consistency feature index, a frame rate stability feature index, and a spatial distribution density feature index are generated. Finally, the image quality score of each key feature area is obtained by comprehensive processing. Through this process, the quality change of the video image can be accurately evaluated to ensure that it meets the preset quality standards. The present invention also has an adaptive adjustment capability, and can dynamically adjust the parameters of image quality detection according to real-time monitoring results, such as optimizing the frame rate calculation threshold and improving the feature extraction accuracy.

The invention discloses a lingual surface diagnostic information acquisition, diagnosis and analysis method and system, and belongs to the technical field of medical auxiliary diagnos.The method comprises the steps that an acquired lingual surface image is matched with patient information, a symptom associated lingual surface area is determined, the associated lingual surface area is subjected to priority division, and whether the acquired image meets a clear standard or not is judged; the method comprises the steps of constructing an image index evaluation model based on tongue vibration and image texture, performing tongue vibration, texture stability and water vapor fuzzy interference evaluation on an image needing to be processed, constructing a stable frame evaluation model, and importing vibration intensity, texture stability and a water vapor proportion into the stable frame evaluation model to evaluate image area stability. Image registration is carried out on the stable frame set, multi-frame registration and fusion are carried out based on an image stable region, region stability and processing information are recorded, a high-quality image for tongue picture analysis is generated, the image definition of a key diagnosis region is improved, and the accuracy and stability of tongue picture analysis are improved.

The invention discloses a diagnostic analysis method and system based on a digital twinborn technology and an intelligent management and control platform. The method comprises the following steps: acquiring environmental data of a photovoltaic power station in real time and matching an inspection scheme; calling a corresponding inspection device and completing data acquisition according to the inspection scheme by using the intelligent management and control platform; preprocessing the collected data, performing fusion arrangement on the data by utilizing a knowledge graph of the photovoltaic power station, and dividing the data by adopting a preset layering and blocking mode; mapping the divided data to a pre-constructed three-dimensional model of the photovoltaic power station according to a progressive verification and construction strategy by using a digital twinborn technology, and generating a digital twinborn body of the photovoltaic power station; and performing fault prediction and judgment on the photovoltaic power station by utilizing the digital twinborn body of the photovoltaic power station and combining a machine learning algorithm, and obtaining a prediction and judgment result and displaying the prediction and judgment result in the digital twinborn body. According to the invention, the fault of the photovoltaic power station can be diagnosed timely and accurately in a complex or extreme environment.

The invention discloses a primary and secondary fusion pole-mounted circuit breaker intelligent fault diagnosis and monitoring system, and relates to the technical field of electric appliance fault diagnosis analysis, and the system comprises an information collection module, a sensing monitoring module, a diagnosis prediction module, an evaluation module and an optimization module. Communication barriers among different devices are broken through, the system can be compatible with various devices, comprehensiveness and compatibility of data acquisition are improved, it is ensured that data of various devices can be effectively acquired and integrated, accuracy and reliability of the data are ensured, data transmission quantity is reduced through compression transmission, network bandwidth pressure is reduced, and key fault information can be transmitted in time. Operation and maintenance personnel can quickly respond and process, the fault influence range and time are reduced, key information can be accurately extracted according to actual requirements through the feature extraction module, and the pertinence and accuracy of fault diagnosis are improved.

The present invention discloses a computer motherboard automated integrated test and intelligent diagnosis system and method, which relates to the field of computer motherboard test diagnosis technology, including a test platform building module, multi-interface adaptation and automatic motherboard identification, including a high-precision signal source and acquisition card, a modular architecture and high-speed bus interconnection; a test project execution module, which carries out multiple functional tests and simulates multiple scenarios to adjust the load; a data acquisition and storage module, which synchronously acquires multiple types of data through multiple channels, and has distributed storage and redundant backup; an intelligent diagnosis and analysis module, big data and machine learning algorithm diagnosis, a report generation and output module, a template engine generates reports and optimizes layout; a system self-check and calibration module, which starts each self-check module. The present invention realizes efficient and accurate testing of computer motherboards, reliable data acquisition and storage, modular design facilitates upgrading, and convenient remote functions, improves test and diagnosis efficiency and quality, helps motherboard production, maintenance and research and development, and enhances industry competitiveness.

The invention provides a circuit breaker action coil current characteristic diagnosis and analysis system and method. Belongs to the technical field of power system automation and substation state monitoring. The method comprises the following steps: carrying out synchronous wave recording on current of a plurality of circuit breaker actuating coils on a direct current feeder screen side to generate multi-channel synchronous wave recording data; meanwhile, SOE message information is collected, a circuit breaker with specific action is automatically matched and positioned by utilizing a timestamp and action identification information in an SOE message, and action circuit breaker positioning data is generated; the current waveform data of the circuit breaker actuating coil are collected in real time, and a high-precision algorithm is used for feature extraction and analysis, so that abnormal fluctuation in current change can be accurately captured, such as overlarge starting current and unstable holding current, the risk of misoperation or rejection of operation of the circuit breaker caused by abnormal current characteristics is reduced, and the service life of the circuit breaker is prolonged. Electric power system accidents caused by equipment faults are avoided, and the operation reliability of the circuit breaker is improved.

The invention relates to the technical field of fault diagnosis, and discloses a transformer adaptive fault diagnosis method, system and equipment based on a sensor, and the method comprises the steps: collecting an operation signal of a transformer through a sensing device, carrying out the feature extraction of the operation signal, and obtaining a plurality of signal feature data; each signal feature data is input into each corresponding signal fault diagnosis model to obtain a plurality of fault diagnosis sub-results, and each signal fault diagnosis model is constructed by adopting a probabilistic neural network model; according to a preset weight value, weighted summation is carried out on the fault diagnosis sub-results, a fault diagnosis result of the transformer is obtained, and the weight value is obtained through calculation based on the diagnosis accuracy of each signal fault diagnosis model. According to the invention, comprehensive data processing and diagnostic analysis are carried out on various sensor signals, so that efficient and accurate diagnosis of the transformer fault can be realized, and thus a powerful guarantee is provided for stable operation of a power system.

The invention belongs to the technical field of vehicle diagnosis, and discloses a diagnosis report analysis method and system, terminal equipment and a storage medium, and the method comprises the steps: carrying out the vectorization of fault information, received by automobile diagnosis equipment, of a target vehicle, and obtaining target vector data; preprocessing the collected original data of the vehicle to obtain original vector data; calculating the similarity between the target vector data and the original vector data, and obtaining matched data in a preset similarity range; and performing diagnostic analysis on the target vector data and the matching data by using the first pre-constructed model to obtain a diagnostic analysis result of the target vehicle. After the original data is preprocessed to obtain the original vector data, the similarity between the target vector data and the original vector data is calculated, then the selected matching data is input into the first pre-purchase modeling for diagnosis analysis, the diagnosis decomposition result is obtained, and the interpretation efficiency and accuracy of interpreting the automobile diagnosis report are improved.

The invention discloses a remote diagnosis and treatment method and system based on a pulse simulator, relates to the technical field of medical care, and discloses a patient pulse acquisition module, a pulse condition parameter simulation setting module, a pulse condition dimension diagnosis module and a pulse remote diagnosis and treatment module. By arranging the patient pulse collection module, the pulse condition parameter simulation setting module, the pulse condition dimension diagnosis module and the pulse remote diagnosis and treatment module, pulse signals of a patient can be periodically simulated through the pulse simulator, and the diagnosis precision of pulse diagnosis on the patient is guaranteed; according to the method, the potential abnormal pulse condition dimension data of the patient can be preferentially diagnosed and analyzed, the pulse condition data of all dimensions does not need to be analyzed at the same time, the pulse condition data processing efficiency of pulse diagnosis of the patient can be improved, and meanwhile the pulse condition data processing difficulty of pulse diagnosis of the patient is effectively reduced.

The invention discloses a well station instrument fault intelligent diagnosis system and diagnosis method based on big data and artificial intelligence, and the system comprises a well station instrument data management and acquisition module which collects and stores well station instrument data in real time, and provides a visual data display interface at the same time, so that an operator can know the operation state of a well station in time; the well station instrument data analyzing and processing module is used for analyzing the collected well station instrument data and extracting key index data; according to the well station instrument fault early warning model system, a multi-level early warning model is created, and potential faults of well station instruments can be effectively recognized and predicted; and the well station instrument fault diagnosis and analysis module is used for quickly identifying abnormal conditions in the well station instrument data, making fault early warning and analysis reports and providing the fault early warning and analysis reports to management personnel. According to the intelligent diagnosis system and the diagnosis method, problems possibly occurring in production are analyzed and predicted through an intelligent algorithm and a model, and corresponding measures are taken in advance so as to ensure safety and stability of oil well production.

The invention relates to the technical field of power system power distribution network fault detection, in particular to an arc fault diagnosis and analysis method based on an artificial intelligence algorithm, and the method comprises the four steps: synchronous data collection and topological excitation, deployment of terminals at transformer area nodes, injection of characteristic current, and synchronous collection of response waveforms; signal preprocessing: separating a key frequency band through double-digital band-pass filtering, and calculating energy ratio and other enhanced fault features; performing multi-source feature fusion and intelligent diagnosis, constructing a vector containing statistical features, time domain distortion features and topology identification, and inputting a gradient boosting decision tree and deep neural network hybrid model to obtain fault confidence and type; and based on fault positioning and verification of topology, scheduling multi-node cooperative monitoring, and determining a fault point in combination with a topological relation. The method improves the data reliability and diagnosis precision, achieves the precise positioning of a fault, and guarantees the safe operation of a power distribution network.

The invention discloses a big data-based machine tool fault diagnosis and analysis method and system. The method comprises the following steps of: acquiring signal data of each part of a machine tool by utilizing a sensor; carrying out de-noising processing on the acquired signal data; the de-noised signals are decomposed by using successive variational mode decomposition, screening is carried out based on a Pearson's correlation coefficient, and the unscreened signals are fused and then are subjected to signal reconstruction with the screened signals; the method comprises the following steps: constructing a fault diagnosis model based on an Adaboost-Reformer integrated algorithm, and optimizing hyper-parameters of the Reformer model by using an improved water circulation optimization algorithm; the reconstructed signals are divided into a training set and a test set, the training set is input into the optimized model for training, machine tool signals collected in real time are input into the trained model after being subjected to denoising and reconstruction processing, and corresponding fault categories are obtained. The method can effectively extract fault features from the multi-mode sensor signals and perform accurate diagnosis, has strong robustness and high efficiency, and is suitable for intelligent fault diagnosis of industrial equipment.

The invention discloses a transformer voiceprint recognition and abnormal state diagnosis method based on lightweight edge calculation, and the method comprises the steps: obtaining transformer voiceprint sampling data, carrying out the smooth gain equalization processing of the transformer voiceprint sampling data, and determining the transformer voiceprint sampling data as first voiceprint data; the edge computing end identifies and collects transformer abnormal state voiceprint features according to the first voiceprint data; constructing a transformer abnormal state diagnosis model, and generating voiceprint abnormal state evaluation result data of the second voiceprint data through analysis; according to the transformer abnormal state diagnosis method, edge calculation and cloud calculation are combined, a complex voiceprint diagnosis analysis task is processed by utilizing powerful calculation and storage capabilities of the cloud, and gain equalization processing of transformer voiceprint sample smoothing is carried out by utilizing an edge calculation end; the communication burden and time delay of the edge node are reduced, and the processing effect of the transformer voiceprint sample data is improved.

The invention provides a disease diagnosis result evaluation method and device, equipment and a storage medium. The method comprises the steps of obtaining at least one to-be-evaluated disease diagnosis result determined based on a target medical record; for each to-be-evaluated disease diagnosis result, inputting the to-be-evaluated disease diagnosis result and the target medical record into a large language model to obtain at least two diagnosis roles output by the large language model and diagnosis dimensions of each diagnosis role; for each diagnosis role, based on the diagnosis role, the diagnosis dimension corresponding to the diagnosis role, the to-be-evaluated illness state diagnosis result and the target medical record, determining first prompt information, and inputting the first prompt information into a large language model to obtain a diagnosis analysis report corresponding to the diagnosis role output by the large language model; and based on the diagnosis analysis report corresponding to each diagnosis role, determining a target evaluation result of the to-be-evaluated illness state diagnosis result, the target evaluation result being used for representing whether the to-be-evaluated illness state diagnosis result is correct or not. The evaluation accuracy of the illness state diagnosis result can be improved.

The invention relates to the technical field of report optimization, in particular to a user medical history-based physical examination report conclusion priority adjustment method, which comprises the following steps of: acquiring multi-source data, and preprocessing the multi-source data to obtain a structured multi-source data set; based on the structured multi-source data set, mining an association relationship between health data and diseases through association analysis, constructing a health trend prediction model in combination with a deep Q network, and obtaining a multi-dimensional diagnosis analysis result; determining a diagnosis conclusion priority in combination with a multi-dimensional diagnosis analysis result and a priority calculation formula; calculating a comprehensive risk score through the data fusion score, and dividing risk levels according to the comprehensive risk score to match corresponding stage intervention strategies; and inputting a multi-dimensional diagnosis analysis result, intervention strategy execution feedback data and user health preference into a comprehensive diagnosis decision model, and obtaining a differentiated health association strategy in combination with a diagnosis conclusion priority. According to the scheme, the severity of the disease is accurately identified by dynamically adjusting the priority.

The invention relates to the technical field of medical information systems, in particular to a breast cancer multi-mode image collaborative diagnosis system and method, and the system comprises an image collection module which collects the image data of a mammary gland molybdenum target mode, an ultrasonic mode and a magnetic resonance mode of a patient; the feature extraction module is in communication connection with the image acquisition module; the data processing module is in communication connection with the feature extraction module; the data analysis module is in communication connection with the data processing module and receives the initial diagnosis result sent by the data processing module; analyzing the initial diagnosis result to generate a diagnosis analysis result; the judgment module is in communication connection with the data analysis module and receives the diagnosis analysis result sent by the data analysis module; determining a treatment suggestion according to the diagnosis analysis result; the automatic examination module is in communication connection with the judgment module and triggers re-examination according to the treatment suggestions; and outputting a final diagnosis result and a treatment suggestion, making full use of the advantages of each mode, and significantly improving the feature extraction effect.

The invention discloses a sample concentration detection method, a standard curve group establishment method, a matrix specificity standard curve optimization method, a standard curve group establishment device and an immunity analyzer, and belongs to the field of diagnostic analysis. Acquiring matrix information of a to-be-detected sample; selecting a matrix specificity standard curve corresponding to the matrix of the sample to be detected; and detecting the sample and calculating the concentration of the analyte in the sample to be detected by using a matrix specificity standard curve selected from a plurality of standard curve groups of which the specificity corresponds to different matrixes. The corresponding substrate specificity standard curves are configured for various types of substrates in advance, and when a detection task is carried out, the corresponding substrate specificity standard curves can be selected and called, so that the detection is more targeted, the substrate effect caused by using a general standard curve can be avoided, the substrate effect can be reduced, the implementation operation is simpler, and the detection efficiency is improved. And the working time of detection personnel can be remarkably saved, and the detection cost is effectively reduced.

The invention relates to the technical field of medical assistant systems, and discloses a comprehensive intelligent medical assistant system, which comprises an information acquisition unit used for acquiring user state information; the user state information comprises user static data, dynamic data and feedback data; the model construction unit is used for performing feature extraction according to the user state information and establishing data association indexes of physiology, environment and behaviors; the information processing unit is used for automatically capturing symptom characteristic information and carrying out diagnostic analysis on the input information in combination with user state information; the pushing unit is used for performing local decision processing on the analysis result, forming a diagnosis path diagram and pushing the diagnosis path diagram to a user side; and the learning optimization unit is used for optimizing a recommendation strategy according to the feedback data and integrating long-term health features to carry out optimization suggestions. According to the invention, the service mode of medical health is remodeled, the unique health characteristics of each user can be more accurately understood, the service quality and credibility of a medical assistant system are improved, and the occupancy rate of medical resources is reduced.

The invention particularly relates to an engineering wire harness testing method and system, and relates to the technical field of engineering wire harness testing. A measurement and self-calibration module; a test channel health degree real-time monitoring module; and a data processing and diagnosis analysis module. According to the invention, the standard gold wire harness and an automatic self-calibration mechanism solve the problems of long period and low efficiency of traditional manual calibration; the system can automatically trigger self-calibration according to multiple conditions such as time, events and the like, the measurement deviation is captured through multi-dimensional reference measurement, real-time deviation compensation is achieved by means of software closed-loop calibration, meanwhile, fault components can be accurately positioned according to hardware deviation, a maintenance report can be generated, and reference drift caused by environment fluctuation and component aging in a calibration interval is avoided.

Systems and methods for analyzing multimodal data using one or more large language machine learning models may include inputting medical image data to a trained image-based machine learning model and generating medical image data embeddings; inputting genomic data to a trained genomics machine learning model and generating genomic data embeddings; inputting text data to a trained large language machine learning model, and generating a mapping of extruded data; and feeding the medical image data embeddings, the genomic data embeddings, and the mapping of extruded data to trained large language machine learning model at a foundation layer, the trained large language machine learning model at the foundation layer being trained to generate one or more of a data and analytics report, a diagnostic analysis report, a large language model agent, or user data for display in a user interface at an output device.