19results about How to "Improve diagnostic efficiency" patented technology

The utility model relates to connecting pipe technical field discloses a high quality visual electronic cystoscope catheter, including operation handle, the outside fixed connection of operation handle has endoscope catheter, the inside slide connection of endoscope catheter has connecting pipe, the outside fixed connection of connecting pipe has curved end, the outside of operation handle is equipped with a plurality of limit slot, the outside slide connection of limit slot has telescopic mechanism, the outside fixed connection of operation handle has quick -witted mechanism, telescopic mechanism includes follow -up block no.

This application relates to the field of intelligent healthcare, specifically to a data processing method, device, and program product for renal artery FFR assessment. The method includes acquiring continuous temporal frame data from renal artery angiography; selecting frames from the temporal frame data of the renal artery angiography to obtain multiple time series, the multiple time series including keyframes and N valid frames, where N is a natural number greater than 1; inputting the multiple time series into a segmentation model to perform renal artery segmentation to obtain renal artery vessel masks for keyframes and valid frames; registering the keyframes and keyframe masks with the N valid frames and valid frame masks sequentially based on L scales to obtain L*N registered valid frame data; and constructing 2.5D data based on the registered valid frame data and keyframe data to obtain 2.5D data for renal artery FFR assessment. This application has significant clinical value.

This application discloses a method, apparatus, and storage medium for diagnosing traffic congestion nodes, relating to the field of chip verification technology. The method includes: acquiring traffic information from each cache unit; analyzing the traffic information to obtain diagnostic sequences; analyzing the diagnostic sequences to obtain abnormal sequence values ​​indicating a mismatch between state signals and data flow; identifying abnormal cache units based on the abnormal sequence values; and determining traffic congestion nodes based on the data interaction between the abnormal cache unit and upstream / downstream nodes and / or its own storage occupancy rate. This application eliminates the need for manual frame-by-frame analysis of massive waveform files, replacing traditional manual tracking with time-series and quantitative analysis to achieve automated and accurate diagnosis of congestion nodes. This ensures comprehensive coverage while reducing diagnostic workload and improving diagnostic efficiency. It can adapt to the traffic congestion diagnosis needs of complex designs under test and shorten the verification cycle.

The application discloses an intelligent diagnosis auxiliary system based on a medical knowledge graph and belongs to the medical diagnosis field. In order to solve the problem that a traditional medical diagnosis system is difficult to integrate information, a large amount of medical information is wasted, effective auxiliary diagnosis cannot be performed, the diagnosis efficiency is low, effective intelligent triage prediction cannot be performed, the patients are easily caused to have a burden of diagnosis, the hospital is easily caused to have a burden of operation, the work efficiency is low, corresponding diagnosis results cannot be given in time and quickly, and the diagnosis accuracy is influenced. The existing medical data island problem is overcome, the intelligent auxiliary diagnosis of common diseases is realized, the diagnosis efficiency is improved, the doctors are provided with high-credibility auxiliary diagnosis, the intelligent triage prediction is realized, the probability of wrong triage is reduced, the patients are provided with efficient and convenient pre-diagnosis consultation, the burden of the patients and the operation load of the hospital are reduced, the diagnosis accuracy of the diagnosis auxiliary system for comprehensive data analysis of patient information is gradually improved through human error correction.

The application discloses an acute large blood vessel occlusion identification and reperfusion decision method and system, and the method comprises the following steps: S1, obtaining medical record text and head multi-modal CT image data of a patient to be diagnosed; S2, performing natural language processing on the medical record text to obtain text features; S3, processing the multi-modal CT image data, calculating the ASPECTS score of non-enhanced CT, and positioning the blood vessel occlusion position and evaluating the collateral circulation from the CTA image to obtain image features; S4, performing weighted fusion on the text features and the image features to generate fusion features; and S5, inputting the fusion features into a pre-constructed guideline knowledge graph and a decision model to generate an individualized acute large blood vessel occlusion identification result and a reperfusion treatment suggestion. The application can automatically, quickly and accurately generate an individualized identification result and a treatment suggestion by fusing the features of the medical record text and the multi-modal CT image and combining a real-time updated guideline knowledge graph.

The utility model discloses a kind of embedding boxes for congenital megacolon specimen, it is related to the field of tissue embedding technology.It includes box body with filter hole, circular ring, tight knob column, helical segmentation strip and box cover.Circular ring is fixed along inside of side plate, helical segmentation strip is coiled in spiral, one end is connected with the tight knob column of center, the other end is fixed in circular ring, can adjust interval with knob column rotation to hold or release specimen.Tight knob column contains shaft sleeve, shaft cap and split type knob spoon, through hexagonal structure cooperation and the bistable clamping of arc spring and clamping groove, the clutch and stable rotation of knob spoon and shaft cap are realized, control helical strip tightness.Box body, circular ring, helical strip etc.are provided with round hole to ensure reagent penetration, box cover buckle sealing, label area is provided in front.The design realizes specimen integration fixation, maintains morphological continuity, simplifies material taking, embedding process, improves pathological diagnosis efficiency and accuracy.

The application discloses a kind of low-dose CT image denoising and multi-dimensional auxiliary diagnosis system based on large model belongs to medical image intelligent processing and man-machine interaction technical field.The system fuses large language model and double space alignment denoising module, lightweight multilevel anomaly detection module, single Agent-oriented medical scene man-machine interaction architecture, and integrates three major subsystems such as front-end, background algorithm and database management and communication interface, realizes from low-dose CT image denoising, anomaly detection to intelligent interactive diagnosis end-to-end integrated processing.Through introducing LLM semantic injection, double space constraint denoising, cross-modal feature alignment and dynamic iterative optimization etc., effectively overcome the imbalance of noise suppression and detail preservation in traditional method, weak model generalization ability, diagnosis process fragmentation and interaction unfriendly etc., significantly improve the diagnosis efficiency and accuracy, reduce the clinical use threshold, suitable for low-dose CT intelligent auxiliary diagnosis scene of medical institutions at all levels.

The present invention belongs to the field of biomedical technology, and particularly relates to a kit for early diagnosis of lung cancer and its application. The kit includes an enzyme-labeled plate coated with a capture antibody, a working solution of an HRP-labeled antibody, an antigen standard, a washing solution, a chromogenic solution, and a termination solution; the heavy-chain amino acid sequence of the monoclonal antibody F6C-1 against the MAPRE2 protein in the capture antibody is as shown in SEQ ID NO.1, and the light-chain amino acid sequence is as shown in SEQ ID NO.2; the heavy-chain amino acid sequence of the monoclonal antibody H3A-2 against the MAPRE2 protein in the HRP-labeled antibody is as shown in SEQ ID NO.3, and the light-chain amino acid sequence is as shown in SEQ ID NO.4. The kit for early diagnosis of lung cancer provided by the present invention has strong specificity, can be used as an auxiliary diagnostic method for lung cancer with high expression of the MAPRE2 protein, and has high diagnostic value.

The application discloses a PCIE device state detection method and system, electronic equipment and medium, relates to the technical field of PCIE device, and comprises the following steps: scanning a PCIE device tree on a server mainboard, and obtaining a PCIE device identifier on the PCIE device tree; disabling and enabling the connection state of the PCIE device and a central processor according to the device identifier; after the connection state of the PCIE device is disabled and enabled each time, performing PCIE link training once within a preset time; and after each link training is completed, the register results associated with the PCIE device are read and traversed to analyze the PCIE device state. The application can perform pressure test on the PCIE device, effectively improves the detection and diagnosis efficiency of the PCIE device, and if the PCIE device fails, the register information can be inquired, and the failure cause can be analyzed and located.

This invention provides a method, device, and computer storage medium for obtaining cerebrovascular function compensation indicators. The method includes: acquiring four-dimensional image data of a subject's brain, including dynamic magnetic susceptibility contrast-enhanced MRI data or CT perfusion imaging data; inputting the four-dimensional image data into a physical information neural network model, which outputs cerebrovascular function compensation indicators, including cerebrovascular reactivity as a first indicator and oxygen extraction fraction as a second indicator; wherein the physical information neural network model includes a compensation mapping component, which generates the first and second indicators and introduces a nonlinear inverse proportional relationship to correct the first and second indicators. This invention provides a safe, efficient, accurate, and comprehensive method for obtaining cerebrovascular function compensation indicators.

The utility model discloses a kind of operation fault self-diagnosis devices of distributed energy storage system. The main body of device is equipped with rotatable photovoltaic module, and lower end is provided with battery compartment. Photovoltaic module is powered for battery compartment, and battery compartment is powered for detection unit. Battery compartment is connected with main body through the through hole of distributed energy storage system upper cover, so that the device is stably installed on energy storage system. When installing, install battery compartment and connect each component in the top hole of energy storage container;When debugging and operating, adjust the angle of photovoltaic module and collect data through internal detection unit, data processing module analyzes operating state, and triggers alarm when abnormal. The device can monitor the operating state of battery pack in energy storage system in real time, so as to discover fault in time, widely applicable, effectively improve the stability and safety of energy storage system operation.

This invention provides an electrical system and fault detection method for paddy field harvesting machinery, belonging to the field of agricultural machinery technology. It includes: a data acquisition module acquiring electrical operation data and sending the data to an edge computing module; the edge computing module performing incremental clustering and dynamic updating based on the electrical operation data to obtain data ownership status, performing multi-level anomaly judgment processing based on the data ownership status to obtain fault diagnosis results, and sending the fault diagnosis results to a wireless communication module; and the wireless communication module sending the electrical operation data and fault diagnosis results to a remote information platform. This invention utilizes an edge computing module for incremental clustering, dynamic updating, and multi-level anomaly judgment processing of electrical operation data, and leverages a wireless communication module to remotely upload diagnostic results and data, enabling remote online fault diagnosis and early warning. This effectively adapts to dynamic operating conditions, significantly reduces false alarm rates, accurately locates deep-seated fault causes, and improves diagnostic efficiency.

This invention relates to the field of cooking control technology, and discloses a fault diagnosis method, device, gas equipment, and readable storage medium. Applied to gas equipment, the method includes: in response to a triggered ignition operation, acquiring a wind speed signal at the ignition location; when ignition failure is detected, determining whether the wind speed signal is greater than a preset wind speed threshold; if the wind speed signal is greater than the preset wind speed threshold, determining the fault type of the gas equipment as "wind-blown out." This invention, by accurately acquiring the wind speed signal at the ignition location, enables rapid localization of wind-blown faults after ignition failure. It overcomes the limitations of traditional ignition fault diagnosis, which ignores wind speed factors or cannot accurately determine wind-blown out faults. It can clearly identify the root cause of ignition failure due to wind without manual investigation, improving the accuracy and efficiency of wind-blown fault diagnosis. Simultaneously, it reduces the risk of gas leakage caused by repeated ignition due to wind, improving the safety of gas equipment use and user experience.

The application discloses an intelligent film image evaluation system and method adopting cloud detection technology, relates to the technical field of medical image processing, and comprises a cloud server, which is communicatively connected with the following modules, wherein: an image acquisition and uploading module is used for acquiring original film image data from various film image devices and uploading the acquired original film image data to the cloud server for preprocessing. The application realizes intelligent analysis of the film image through the cloud detection technology, gives a quality score in combination with a preset quality standard, and judges whether the image meets diagnostic requirements, so that evaluation differences caused by human factors are reduced. Meanwhile, according to extracted interference features, a pattern classification algorithm is used to perform quality analysis on the film image meeting the diagnostic requirements after quality evaluation, a quality classification coefficient is calculated, and the image is divided into different quality grades, so that accurate classification of the image quality is realized, and a reference basis is provided for medical diagnosis.

The utility model discloses a kind of negative pressure suction type puncture biopsy apparatus, it is related to medical instrument field.The biopsy apparatus includes needle tube, needle core and pressure device, pressure device is detachably connected with needle tube, is fixed by clamping limiting portion.It has beneficial effect including improving sample quality, convenient operation and improving diagnostic efficiency.Needle tube has scale outside, bottom and needle core cooperate closely.Handle has antiskid line, connecting port and pressure device connecting pipe are closely adhered through conical structure.Needle core surface is smooth, and needle tube is closely adhered.Pressure device can be balloon expansion pressure pump etc., clamping limiting portion improves the stability of connecting portion.Under image guidance, puncture is operated as needed, and sample is obtained by adjusting negative pressure.The biopsy apparatus overcomes the problem that existing puncture biopsy mode is difficult to balance sample efficiency and tissue protection, and the problem of inconvenient operation, can adapt to different texture tissue, improve sample quality and representativeness, reduce operation difficulty and risk.

This invention relates to the field of power equipment monitoring technology, and in particular to an intelligent transformer fault early warning system. The technical solution includes an intelligent data acquisition module, a multi-dimensional fault analysis module, a dynamic early warning module, an intelligent execution control module, a remote communication module, and a human-machine interface module. The invention achieves three-dimensional perception through the multi-physics sensor unit of the intelligent data acquisition module. A fiber optic temperature sensor accurately captures the temperature rise of winding hotspots, a piezoelectric vibration sensor identifies abnormal mechanical stress, and an ultrasonic partial discharge probe detects early signs of insulation defects. An electrical parameter monitoring unit simultaneously collects electrical indicators such as voltage, current, and power factor, forming an electrical-physical dual-dimensional data stream. An environmental perception unit dynamically monitors changes in temperature, humidity, air pressure, and oil chromatography, significantly improving the early warning system's ability to detect complex faults and reducing the probability of missed or false alarms.

This invention discloses a fault diagnosis method for wind farm equipment based on neural networks and digital twins. First, multimodal data from multiple equipment units and their sub-units during normal operation are collected and preprocessed. Then, physical-driven and data-driven models are constructed separately and combined to form a digital twin model. Through reinforcement learning, model parameters are dynamically adjusted based on real-time equipment operation and external environmental data. Fault simulation data is injected to construct a fault database, which is used to match fault locations and assign confidence levels when equipment malfunctions. A confidence threshold is set based on the matching probability, and the digital twin model parameters are dynamically adjusted to determine the diagnostic scheme. The multimodal data covers various aspects, including structural health monitoring, and preprocessing includes noise reduction and feature extraction. This method integrates multi-source data, improves diagnostic accuracy and real-time performance, effectively addresses complex fault scenarios, reduces operation and maintenance costs, and ensures stable operation of the wind farm.

This invention relates to the field of clinical virus detection, and more particularly to a protein dot blot detection kit, detection method, and application for varicella-zoster virus (VZV). The kit includes: a solid-phase carrier membrane for binding and immobilizing proteins in the sample; a blocking agent for blocking non-specific binding sites on the solid-phase carrier membrane; a first antibody that specifically recognizes the VZV antigen; an enzyme-labeled second antibody that binds to the first antibody; and a chromogenic solution for generating a detection signal. This kit is based on protein dot blot technology, detecting proteins with high specificity and a low false-positive rate. The experimental cycle is short, taking only 2-3 hours from sample pretreatment to signal detection, much faster than virus culture (requiring 4-7 days) and PCR detection (although fast, prone to false positives or false negatives), providing rapid evidence for clinical treatment, and is particularly suitable for the diagnosis of acute herpes zoster.