893 results about "Diagnostic information" patented technology

The invention relates to the technical field of power distribution protection cabinet fault diagnosis, and discloses an intelligent fault diagnosis and rapid isolation method for a power distribution protection cabinet, and the method comprises the steps: synchronously collecting electrical parameters, environment states and equipment motion signals in the cabinet as multi-source monitoring data when the power distribution protection cabinet operates; parameter fluctuation, state change and action time sequence features are extracted after preprocessing, cross-data association analysis is carried out through a multi-dimensional association model to generate an association feature set, a fault diagnosis information set is formed based on the identified abnormal types, fault positions and development trends, an isolation strategy is formulated according to severity levels and influence ranges, and a fault diagnosis result is obtained. And generating a rapid isolation instruction and dynamically adjusting along with fault evolution. According to the method, multi-source data fusion analysis is realized, faults can be accurately diagnosed and rapidly isolated, the operation safety and reliability of the power distribution protection cabinet are improved, and the method is suitable for fault processing of the power distribution protection cabinet in a power system.

The invention provides a trauma severity assessment method and system based on artificial intelligence and big data analysis, and relates to the technical field of AI.The trauma severity assessment method comprises the steps that an AI model is guided through cue word engineering to extract trauma information from structured medical record data of a trauma patient, and JSON format data containing diagnosis information, trauma identification and trauma description is generated; and a vector retrieval technology is adopted to retrieve a scoring rule related to a single injury condition from the modified ISS scoring rule knowledge base, and the AI model determines the area, the part and the score of each injury condition based on the JSON data and the scoring rule. And after checking and combining the injury condition scores, finally outputting an ISS total score and a specific score result of each wound area. The method can automatically extract and score the injury condition information, improves the ISS scoring efficiency and accuracy, and assists a doctor in rapidly evaluating the injury condition of a patient.

The invention relates to an LLM-based power distribution network knowledge graph construction and fault intelligent diagnosis method. The method comprises the steps of constructing a knowledge system, constructing a normal operation state model and an anomaly detection threshold value, performing fault diagnosis through an LSTM-BP cascade network, and performing fault classification. The abnormal parameters and the fault classification result are input into an LLM model, the fault type is judged, corresponding diagnosis information is obtained, and the system generates a scheduling instruction and an emergency decision suggestion according to the diagnosis information; and generating a detailed diagnosis report. According to the system, statistical analysis and deep learning algorithms are adopted for operation parameters such as voltage and current after preprocessing, and automatic detection and fault classification of abnormal parameters are realized in combination with a CNN recognition module; a preset rule base in the knowledge system is combined to accurately determine a fault type, generate a scheduling instruction and an emergency decision suggestion, and assist operation and maintenance personnel in rapid response and dynamic management; finally, a detailed diagnosis report is generated, thereby improving the fault handling efficiency and the guarantee capability of safe and stable operation of a power grid.

The invention provides a customized microscopic examination case grading retrieval system based on multi-modal learning, which is characterized in that a CLIP model is utilized to process a retrieval demand input in an image form, and after the retrieval demand is fused with a retrieval demand input in a text form, feature representation information of the retrieval demand is extracted; comparing with the feature representation of the image in the historical data after CLIP coding to obtain a historical image closest to the retrieval demand after fusion; according to the characteristic that medical images have many fine granularity differences, an existing feature matching mode is improved, hierarchical retrieval design for historical data feature representation is introduced, and the accuracy of customized retrieval is improved. According to the method, in combination with microscopic examination images of patients and specific requirements in diagnosis of doctors, historical related case images and diagnosis information are retrieved and presented, the doctors are assisted in making judgment, and the diagnosis accuracy is improved; the method is particularly beneficial to diagnosis of disputed cases, rare cases and the like in microscopic examination, and reliable basis and reference are provided for diagnosis and treatment of doctors.

The invention discloses a multi-department medical sample collaborative management system and method, and relates to the technical field of medical information, and the system comprises an integrated information interaction platform which is used for achieving the real-time sharing of basic information, clinical diagnosis information and inspection reports of patients; the unique identification module is used for carrying out full-life-cycle tracking on a patient sample through a bar code or a radio frequency tag and dynamically associating the patient sample with patient information; the Internet of Things monitoring module is used for collecting and uploading temperature and humidity parameters of the sample storage environment in real time and triggering abnormal early warning; the automatic storage equipment executes sample classified storage and calling based on a platform instruction, and synchronously updates a sample state with the information system; and the data security module is used for realizing data encryption, dynamic authority management and operation traceability by adopting role-based access control and a block chain technology. According to the technical scheme, the core problems of data islands, low operation efficiency, weak safety and the like in traditional medical management can be systematically solved.

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 relates to a fault judgment system, method and device based on a knowledge graph and a medium, and belongs to the technical field of reliability analysis, and the system comprises a data collection module which is used for obtaining to-be-diagnosed information, the to-be-diagnosed information comprises a device type, a working condition and real-time monitoring data of a target device, the real-time monitoring data comprises operation parameters and state information; the fault diagnosis reasoning module is used for inputting the to-be-diagnosed information into a pre-constructed knowledge graph and performing fault diagnosis on the to-be-diagnosed information by adopting a graph reasoning algorithm to obtain a reasoning result of the target equipment, and the reasoning result comprises a fault type, a fault reason and an influence factor of the target equipment; and the diagnosis result analysis module is used for evaluating the reasoning result to obtain an evaluation result, displaying the reasoning result and the evaluation result based on a visual tool, and generating a fault diagnosis report. According to the method, the accuracy of fault judgment is effectively improved, and misjudgment caused by insufficient experience in a traditional method is reduced.

The embodiment of the invention relates to the technical field of Internet of Vehicles, and particularly provides a network fault diagnosis method and system applied to Internet of Vehicles services, which can capture network state information in various communication modes by acquiring a network state operation log of a target Internet of Vehicles service system from a multi-protocol network communication server, and lay a foundation for accurate diagnosis. By mining the network fault diagnosis embedded representation set, multiple rounds of network fault diagnosis can be presented in a specific representation form, and network fault related characteristics are effectively integrated. Linkage fault diagnosis knowledge characterization is obtained in combination with a diagnosis event distribution quantification label, and the incidence relation among all rounds of diagnosis is fully considered, so that diagnosis information is more comprehensive and accurate. According to the method, the target fault diagnosis analysis algorithm is utilized to obtain the fault judgment confidence coefficient, fault features can be analyzed in a hierarchical and multi-dimensional mode, the accuracy of fault diagnosis is improved, the fault car networking service system can be reliably determined, and the efficiency and precision of network fault diagnosis of the car networking service system are integrally improved.

Understanding a health status and expected remaining lifetime of an EV (electric vehicle) battery is important before repurposing the battery for second life applications. A device for connecting to an unopened EV battery pack via operable coupling to signal and power wiring is disclosed. The device enables access to diagnostic information from the unopened EV battery. The device measures cell and / or module voltages and currents within the battery pack for several different depths of discharge. A self-learning algorithm implemented by the diagnostic device, which uses historical data and diagnostic information from the battery pack, determines a condition of the battery and provide recommended operational conditions for future use of the battery. For example, a degradation slope and expected capacity loss over time can be determined based on measured variations of cell and / or modular voltages and subsequently used, with cell impedance data, to recommend an operational C-rate for the battery pack.

The invention discloses an IT system fault diagnosis method and system based on a large language model, and the method comprises the steps: carrying out the vectorization of a troubleshooting guide TSG through the LLM technology, generating a corresponding TSG vector, and defining an automatic troubleshooting process according to the TSG; according to the received alarm event information, determining an associated IT component and an occurring alarm event by using an IT topological graph; performing vectorization on the alarm event by using an LLM technology to generate an alarm event vector, performing similarity comparison with a TSG vector, and determining a TSG of a similar fault; and executing an automatic troubleshooting process of similar fault TSG, determining a fault root cause according to the obtained diagnosis information, and generating a fault diagnosis report by using an LLM technology. Through the technical scheme of the invention, the fault root cause can be accurately and quickly positioned, automatic fault diagnosis is realized, and the fault handling efficiency is improved.

The invention relates to the field of radiology department medicine and computers, and discloses a radiology report automatic generation method and system based on radiology priori knowledge, which comprises the following steps: constructing a radiology priori knowledge performing document dicing on the priori knowledge of a radiology report through a sliding window so as to construct the radiology priori knowledge base; data set reconstruction and fine tuning: constructing text data of a radiation report, and performing instruction fine tuning on the text data of the radiation report by using a large language model; construction of a comprehensive retrieval vectorization module: constructing the comprehensive retrieval vectorization module in combination with semantic similarity retrieval and full-text retrieval technologies; and automatically generating a radiation report by combining the matched radiation priori knowledge after passing through the comprehensive retrieval vectorization module. According to the system, the radiation prior knowledge base, the comprehensive retrieval vectorization module and the report generation module are integrated in advance, so that a doctor can generate a high-quality radiation image report according with a standard format only by inputting radiation diagnosis information.

Techniques include a method, apparatus, system and computer-readable medium to detect, quantify and localize attacks to enhance security for time-synchronized networking. Embodiments include a diagnostic stream producer to produce diagnostic information providing evidence of a timing attack on a node of a time-synchronized network. Embodiments include a diagnostic stream consumer to consume diagnostic information, analyze the diagnostic information, and determine whether a node is under a timing attack. Other embodiments are described and claimed.

Understanding a health status and expected remaining lifetime of an EV (electric vehicle) battery is important before repurposing the battery for second life applications. A device for connecting to an unopened EV battery pack via operable coupling to signal and power wiring is disclosed. The device enables access to diagnostic information from the unopened EV battery. The device measures cell and / or module voltages and currents within the battery pack for several different depths of discharge. A self-learning algorithm implemented by the diagnostic device, which uses historical data and diagnostic information from the battery pack, determines a condition of the battery and provide recommended operational conditions for future use of the battery. For example, a degradation slope and expected capacity loss over time can be determined based on measured variations of cell and / or modular voltages and subsequently used, with cell impedance data, to recommend an operational C-rate for the battery pack.

The invention belongs to the technical field of kidney histopathology and immunological evaluation, and particularly relates to a kidney pathology detection kit, a detection method and application, the detection kit comprises 37 rare metal element labeled monoclonal antibodies combined with antigens of in-situ tissue cells, and the detection method comprises the following steps: (1) collecting a sample to be detected, preparing a pre-dyed paraffin section; (2) mixing 37 monoclonal antibodies labeled by rare metal elements, and carrying out antibody staining on various antigen targets on the surfaces of cells in the in-situ tissue; (3) carrying out nucleus DNA staining on the stained tissue sample; and (4) detecting the stained tissue sample through an imaging mass spectrum flow system, and collecting original information data through laser spot firing. The method is an analysis method for fine grouping identification and relative quantification of structural cells, immune cells and pathological injury cells in the clinical nephropathy sample in-situ tissue. And auxiliary diagnosis information is provided for diagnosis, grading, treatment and prognosis detection of various clinical nephropathy.

The invention discloses a diagnostic information-driven multi-modal medical record similarity retrieval system, which comprises a data acquisition module, a data processing module and a similarity retrieval module, the feature extraction module is used for performing feature extraction on the electronic medical record data; the multi-modal semantic generation module is used for generating multi-modal semantic representation; the diagnosis code processing module is used for constructing a tree structure based on diagnosis codes in medical records, performing low-dimensional representation learning on the diagnosis codes by utilizing a Poincare embedding method, and realizing similarity measurement among diagnosis sets in combination with self-supervised contrast learning; the migration module is used for migrating the similarity knowledge of the diagnosis set to the learning process of the multi-modal semantic representation, and optimizing the multi-modal semantic representation to form medical record similarity representation driven by diagnosis information; and the retrieval module is used for performing similarity matching among the medical records in the shared semantic space and completing a retrieval task of the multi-modal medical records. According to the method, the problems of multi-mode isomerism and annotation data scarcity of the electronic medical record are effectively solved.

The invention provides a blood transfusion treatment scheme recommendation system and method based on historical information, and relates to the field of medical information.The blood transfusion treatment scheme recommendation method comprises the steps that firstly, basic information and diagnosis information of a patient, real-time blood test information and historical blood transfusion medical record information are extracted from HIS, LIS, EMR and TMIS systems; performing joint semantic analysis on the basic information, the diagnosis information and the real-time blood test information, and converting the historical blood transfusion medical record information into a dynamic vector sequence; then, the current blood index and the historical blood transfusion time sequence are dynamically matched in a frequency domain space through nonlinear spectrum analysis, and a personalized recommendation scheme is generated in combination with the implicit reasoning ability of a pre-trained medical large model, so that the limitation of a traditional system in the aspects of static parameter setting, individual adaptability and semantic mining is broken through; and the accuracy and timeliness of blood transfusion treatment decision making are remarkably improved.

The invention relates to the technical field of medical data processing, in particular to a lung cancer risk early warning system based on remote four diagnosis information, which comprises an acquisition module, a generation module, a calculation module, an early warning module and a correction module. According to the method, through multi-modal time sequence fusion and causal map driven closed-loop regulation and control, the more stable, interpretable and adaptive early warning capability for the lung cancer risk is realized, heterogeneous evidences from observation, listening and interrogation are aggregated into a dynamic alarm index according to time and causal paths, instantaneous noise is filtered by using trend consistency and adversarial test, and the early warning effect is improved. Therefore, false alarms caused by short-time fluctuation are remarkably reduced while the sensitivity to real abnormity is kept; the method achieves balance among false alarm reduction, missed diagnosis risk reduction, positive prediction value improvement and user sampling burden optimization, and effectively solves the problem of insufficient early warning accuracy caused by risk identification lag due to dependence on single static medical record information.

The invention discloses a deep learning-based valve opening and closing state recognition and diagnosis method, which comprises the following steps of: acquiring and synchronizing multi-source signals, and generating a standardized multi-mode time sequence sample; extracting modal features by multiple branches and fusing the modal features into a joint feature vector sequence; the combined features are input into a phase change layered decoder, and a layered recognition result is output; constructing a plurality of types of abnormal events in a point process layer modeling phase change stage, and outputting an event modeling result; constructing a condition reversible generation diagnostor, and outputting a consistency checking result; and fusing a result output state and diagnosis information, and executing alarming and filing. Through multi-mode deep learning feature fusion, phase change hierarchical decoding and conditional modeling, accurate recognition of the opening and closing state of the valve, fine division of the phase change stage and intelligent diagnosis of early faults are achieved.

The invention relates to a cloud-edge collaborative medical image intelligent diagnosis method and device, equipment and a medium. The method comprises the following steps: firstly, obtaining original image data, and carrying out calibration processing on the original image data through a preset multi-modal image standardization model to obtain a standard image data set; performing feature extraction and grading processing on the standard image data set at an edge end to obtain a grading feature set, transmitting the grading feature set to a cloud end through a bandwidth allocation strategy, and generating a cloud end receiving feature subset; constructing a feature expression matrix based on the cloud receiving feature subset, using a network topology structure to carry out graph analysis, and extracting a path to generate a diagnosis path set; and finally, feedback diagnosis information is formed based on the diagnosis path set, and a task allocation optimization result is obtained by dynamically adjusting a cloud side task allocation proportion. According to the method, efficient processing and diagnosis optimization of medical image data are realized, efficient collaboration of cloud edge resources is ensured, timeliness and accuracy of medical image diagnosis are effectively improved, and resource scheduling requirements in different scenes are met at the same time.

The invention discloses a non-tracking freestyle three-dimensional ultrasonic image reconstruction method based on deep learning. The method comprises the following steps: obtaining an image data set; constructing an ultrasonic sequence coding module; constructing a wavelet transform convolution module; constructing a mixed attention module; constructing a loss function; constructing a deep learning model by combining an ultrasonic sequence coding module, a wavelet transform convolution module and a mixed attention module, and training the deep learning module by using the training set to obtain a three-dimensional ultrasonic image reconstruction model; scanning a diagnosis and treatment area of a patient by using an image acquisition system and adopting a non-linear scanning track to obtain two-dimensional ultrasonic image sequence data; and inputting the two-dimensional ultrasonic image sequence data as input data into a three-dimensional ultrasonic image reconstruction model for image reconstruction, and outputting a high-quality three-dimensional ultrasonic image. According to the method, the efficiency and the accuracy of three-dimensional reconstruction of the ultrasonic image can be remarkably improved, the adaptability and the robustness of processing the ultrasonic image under different conditions can be improved, and more accurate diagnosis information can be provided for clinic.

The invention provides an intelligent medical multi-round dialogue diagnosis reasoning method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: constructing time sequence features through employing an attention mechanism for time sequence symptom description, and carrying out bidirectional sequence modeling to generate comprehensive symptom features; using the medical knowledge graph to detect logic contradictions and information loss to generate standardized features; calculating information gain of an inquiry direction based on a deep neural network to generate optimal inquiry content; and iteratively updating according to user feedback until the diagnosis information entropy is lower than a threshold value, and outputting a diagnosis result. According to the invention, the accuracy and efficiency of medical diagnosis are improved.

A system and method for an innovative approach to medical imaging compression and encryption, specifically designed for tomosynthesis data. It transforms input data to a dyadic distribution, optimizing it for Huffman encoding while preserving critical diagnostic information. The compressed data is processed through a Large Codeword Model (LCM) incorporating a latent transformer, which learns complex patterns and relationships within the imaging data. This process not only achieves high compression ratios but also provides inherent encryption. A neural upsampler, trained to invert the dyadic transformation, reconstructs the original image with high fidelity. The system is particularly effective for handling the redundancies inherent in tomosynthesis datasets, where adjacent slices often contain similar information. By balancing compression efficiency with diagnostic accuracy, this method enables secure storage and transmission of large medical imaging datasets while maintaining the ability to reconstruct high-quality images for accurate diagnosis.

The invention belongs to the technical field of artificial intelligence, and relates to a distributed photovoltaic system abnormity monitoring and intelligent early warning method. Key parameters of the distributed photovoltaic system are collected at high frequency, noise filtering and signal normalization processing are carried out on data, then multi-domain feature extraction is carried out, anomaly detection is carried out through auto-encoder reconstruction error analysis, convolutional neural network feature classification and a single-class support vector machine, and an anomaly detection result is output. Establishing a multi-dimensional anomaly evaluation model, and synthesizing obtained evaluation results of all dimensions to obtain a final evaluation result; the method comprises the following steps: carrying out hierarchical classification to identify specific anomaly types, carrying out diagnosis of different types of anomalies, identification of typical anomalies and evaluation of fault severity, outputting the specific anomaly types and severity, and providing detailed diagnosis information for early warning decision making. The system state is comprehensively evaluated, corresponding measures are taken according to the grading early warning mechanism and the abnormity severity degree, and complete distributed photovoltaic system abnormity monitoring and intelligent early warning are formed.

The embodiment of the invention discloses an electronic medical record generation method and device, electronic equipment and a storage medium. The method comprises the following steps: in response to an electronic medical record generation instruction for a skin inquiry service, obtaining interaction content between a provider and an acquirer of the skin inquiry service and a deep learning model for realizing electronic medical record generation; acquiring skin data of the skin lesion part inquired by the acquirer; wherein the skin data comprises at least one of a skin image and a skin medical report; and generating an electronic medical record corresponding to the skin inquiry service according to the deep learning model, the interaction content and the skin data. According to the technical scheme provided by the embodiment of the invention, on the basis of the interactive content, the skin data capable of providing main auxiliary diagnosis information for a doctor is combined, so that the deep learning model can be utilized to process the multi-modal information (namely, the interactive content and the skin data) to generate the electronic medical record; therefore, the generation quality of the electronic medical record for the skin inquiry service is improved.

The invention discloses a visual obstetrical image examination processing method, which belongs to the technical field of obstetrical image examination processing, and comprises an image acquisition module, an image processing module, a cyclic influence evaluation module and an output and diagnosis module, the image acquisition module is utilized to acquire and preliminarily process a current obstetrical image, and the current obstetrical image is transmitted to the image processing module; an image processing module is utilized to sequentially calculate and output an image quality evaluation index V, a processing effect enhancement index CZ and a cyclic influence evaluation index CF, and based on the result of the cyclic influence evaluation index CF, the cyclic influence evaluation module is utilized to trigger adjustment of an image processing strategy according to the result of the processing effect enhancement index CZ. According to the method, the image quality and the diagnosis accuracy are improved by comprehensively and quantitatively evaluating the image quality, adjusting the processing method in a personalized manner and introducing a cyclic influence mechanism, so that richer diagnosis information and more accurate decision support are provided.

The present invention relates to a method of providing diagnostic information for brain diseases classification, which can classify brain diseases in an improved and automated manner through magnetic resonance image pre-processing, steps of contourlet transform, steps of feature extraction and selection, and steps of cross-validation. The present invention relates to a diagnostic information providing method capable of providing an optimal diagnostic means. The present invention relates to a method for providing diagnostic information for brain diseases classification, and relates to a method for providing an optimal diagnostic means for classifying brain diseases in an improved and automated manner through the steps of the magnetic resonance imaging pre-processing, contourlet transform, feature extraction and selection, and cross-validation.

The invention discloses a multi-modal medical image fusion system based on deep learning, and relates to the technical field of medical images, and the system comprises an image collection module which obtains medical image data from different modalities; the data preprocessing module is used for carrying out denoising, standardization, image enhancement and registration on the acquired image data; the deep learning fusion module is used for carrying out feature extraction and information fusion on the image data of different modalities based on a convolutional neural network; the diagnosis reasoning module is used for evaluating potential disease information in the medical image data through a trained classification and regression model based on the fused image data; and the visualization module is used for visually displaying the processed image data and the diagnosis result and providing an image and a diagnosis report. According to the method, the limitation of a single mode is overcome by fusing the multi-mode images, more comprehensive and accurate diagnosis information is provided, feature extraction and fusion are performed by adopting the convolutional neural network, and the disease recognition precision is improved.

The invention discloses a medical report generation method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining patient information, inputting the patient information into a report generation model, obtaining diagnosis features, determining historical diagnosis information related to the patient information in a historical diagnosis record as associated information according to the diagnosis features, and generating a medical report according to the associated information. And determining prompt information, inputting the prompt information and the diagnosis features into a report generation model, and generating a medical report. According to the method, a doctor only needs to input the patient information into the report generation model, the medical report generated by the model can be obtained, the doctor does not need to manually write the medical report, and the determination efficiency of the medical report is improved.

The invention relates to the technical field of hepatitis diagnosis and treatment management, and discloses a block chain-based hepatitis patient full-period management method. The method comprises the following steps: creating a patient identity chain in a block chain network, and storing patient registration data and initial diagnosis information; full-cycle diagnosis and treatment data of a patient are obtained and divided into clinical examination, iconography, medication records and life sign monitoring data. Performing multi-dimensional cleaning on the clinical examination data to generate a standardized data set; inputting the iconography data into a pre-trained hepatitis feature extraction model, and outputting a liver injury feature set; verifying the integrity of the medication record, and generating a non-tampering medication time sequence chain; comparing the life sign monitoring data with preset health reference parameters to generate a sign deviation coefficient set; according to the method, data safety and traceability are guaranteed through the block chain, diagnosis and treatment data quality is improved through multi-dimensional data processing, patient privacy is protected, and reliable support is provided for full-period management of hepatitis patients.

The present technology includes systems and methods for generating and implementing patient-specific diagnostic plans. The diagnostic plans include one or more specific diagnostic tests that should be performed on a particular patient based on the patient's demographics and / or health complaints. The diagnostic tests can be selected to determine specific diagnostic information that is expected to be useful in determining patient-specific surgical plans for the particular patient.