94results about How to "Improve diagnostic efficiency" patented technology

The invention discloses a power grid monitoring alarm event handling method and system based on a large model, and relates to the technical field of power systems, and the method comprises the steps: obtaining a historical sample of a power grid monitoring alarm event, and carrying out the preprocessing; constructing a balance training sample set based on a first large language model and resampling combined sample enhancement method; based on the pre-training language model, utilizing the balance training sample set to carry out fine tuning training, and constructing a power grid monitoring alarm event diagnosis model; on the basis of a second large language model, a power grid monitoring alarm event disposal model is constructed, and the disposal model is used for retrieving reference information from a historical disposal knowledge base in combination with an event type diagnosis result output by the diagnosis model and generating an auxiliary disposal suggestion for an input alarm event text; and integrating the diagnosis model and the disposal model to form a power grid monitoring alarm event disposal framework. According to the invention, intelligent diagnosis and auxiliary disposal of the power grid monitoring alarm are realized, and the diagnosis accuracy and the disposal efficiency are improved.

The invention is suitable for the technical field of data processing, and provides a data processing method and device, terminal equipment and a computer readable storage medium, and the method comprises the steps: obtaining a plurality of pieces of first feature information of a patient; obtaining second feature information matched with each piece of first feature information in a feature database; generating first text information according to the multiple pieces of second feature information and the first feature information; and generating a first diagnosis result corresponding to the patient according to the first text information. The method can improve the diagnosis efficiency of the auxiliary diagnosis and treatment system.

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.

The invention discloses a lung disease diagnosis and grading method based on quantitative CT image omics and deep learning. According to the method, through multi-dimensional information integration and model optimization, two core tasks of disease judgment and disease condition grading can be completed at the same time, and comprehensive support is provided for clinical diagnosis and treatment. The model sets feature priorities by referring to clinical diagnosis logic in the training process, the judgment result and the grading standard are completely matched with clinical general specifications, and the model can be directly applied to diagnosis and treatment decision-making without secondary conversion of doctors. After multi-center clinical verification and iterative optimization, the stability and the accuracy of the model are fully guaranteed, subjective errors caused by manual film reading can be effectively reduced, the diagnosis efficiency can be improved, the chronic obstructive pulmonary disease screening capability of primary medical institutions can be remarkably improved, early diagnosis and early treatment of more patients can be helped, and the clinical application prospect is wide. Therefore, the morbidity and disability rate of diseases are reduced, a clinical management path is optimized, and the overall disease burden is relieved.

This disclosure provides a method, apparatus, and storage medium for anomaly diagnosis of wind turbine generator sets. The anomaly diagnosis method includes: acquiring operating data of the wind turbine generator set within a first time period as reference operating data; determining the actual modal frequency of a target component of the wind turbine generator set based on the reference operating data; acquiring operating data of the wind turbine generator set within a second time period as diagnostic operating data; and determining the cause of the anomaly and maintenance plan for the target component based on the diagnostic operating data and the actual modal frequency of the target component. This disclosure, by first determining a reliable actual modal frequency based on the reference operating data and then combining the diagnostic operating data and the actual modal frequency for anomaly diagnosis, can identify a clear cause of the anomaly and a corresponding maintenance plan, achieving more refined anomaly diagnosis. This allows for shutdown for inspection only when necessary, thereby improving diagnostic efficiency and reducing losses caused by frequent shutdowns.

The application provides a kind of laser physical experiment ultrafast image measurement system and method based on end side coordination architecture, it is related to laser physical experiment image measurement technical field, comprising the following steps: laser target shooting generates plasma, captures the X-ray signal image of plasma, generates original data stream;Through lightweight convolutional neural network model, the original data stream is handled, and the feedback control signal for adjusting the laser parameter of the next laser is generated;Obtain the laser parameter related to each physical parameter characteristic value, adjust the laser parameter related to the physical parameter characteristic value;Store the adjustment data each time, periodically train lightweight convolutional neural network model based on adjustment data.The advantage of the application is to realize the instant, reliable experimental parameter control in the experiment process.

The application relates to a liver cancer CT image segmentation method based on channel attention, a segmentation network for liver and tumor regions is constructed, residual modules are used to replace ordinary convolution layers on the basis of a U-Net encoding-decoding structure, the training speed and precision of the network are improved, an attention mechanism is added in the decoder stage, the importance and mutual dependence of different channels in a feature map are fully considered, feature maps output by each stage of the decoder are up-sampled to the size of an original image through a deep supervision module and are spliced with the feature map output in the last stage, feature loss is prevented, two same segmentation networks are used for segmenting a liver region of interest and segmenting tumor cells respectively. The method has good segmentation effect without significantly increasing the parameter amount; from the clinical point of view, liver and tumor regions in an abdominal CT image are accurately segmented, a good auxiliary role is provided for doctor diagnosis, and the diagnosis efficiency is improved.

The application provides a frozen frame data dynamic analysis method and system, the method comprises the following steps: when a vehicle breaks down, obtaining the frozen frame stored by a control module in real time; obtaining the ODX file corresponding to the control module and establishing a query database for the frozen frame, the query database comprises a standard DID file and the data length and data analysis method corresponding to the standard DID file; reading the internal data of the frozen frame and obtaining the diagnostic DID file in real time; comparing the diagnostic DID file with the standard DID file to obtain the data length and data analysis method corresponding to the diagnostic DID file. Through the above method, the data length and corresponding analysis method corresponding to each frozen frame can be obtained in real time and dynamically, so that the analysis of the frozen frame can be dynamically completed, and the parameters of the diagnostic instrument do not need to be modified, thereby greatly improving the diagnostic efficiency of the frozen frame, and facilitating wide promotion and use.

The application provides a joint diagnosis data processing method and system based on Hall electric propulsion data, and relates to the field of space electric propulsion.The method measures the sensor resistance of an erosion sensor through a four-wire method resistance measurement circuit, and calculates the plume erosion rate based on the change rate of the sensor resistance, the material type of the erosion sensor and the geometric size; according to the material sputtering yield of the erosion sensor, the working medium and working voltage of the thruster, the bias voltage of the Faraday probe and the measurement interference data of the bias voltage on the plume erosion rate, the sensor resistance is measured through the four-wire method in a time sequence in a specified period, and the ion current density is measured through the Faraday probe in another time sequence in the specified period; and the plume power density distribution is calculated according to the plume erosion rate, the ion current density, the linearly fitted material sputtering rate function and the position of the joint collection structure in the plume.

The application provides a method and device for detecting the root cause of antenna feeder abnormalities, electronic equipment and a computer readable storage medium, and relates to the field of wireless communication. The detection method comprises: obtaining an abnormal event in a wireless call record, and extracting target index data of an abnormal event occurrence area, wherein the target index data comprises coverage and interference type index data; determining whether the occurrence of the abnormal event is attributed to coverage interference according to the target index data; if yes, detecting whether there is an abnormality in the lobe angle and / or the antenna azimuth angle according to a root cause detection algorithm. At least the problems of diagnosis and analysis of antenna feeder problems in the related art relying on manual operation of experts, time and manpower consumption, and insufficient experience solidification of first-line personnel in positioning problems, which leads to low problem positioning accuracy, are solved.

The present application relates to the technical field of multi-modal data fusion, and particularly relates to an auxiliary judgment method for heart failure based on multi-modal data fusion, which firstly constructs image-text pair data of all data, then constructs the architecture of a joint model, and then respectively trains an image encoder and a parameterized text encoder based on a ranking standard to learn the features of images and texts, and then uses two fully connected layers (with the same neural network structure) on the joint embedding space to respectively evaluate the severity of edema from images and reports, which is used as a reference for doctors, thereby further improving the efficiency of doctors in diagnosing patients with acute congestive heart failure.

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.

The invention relates to the technical field of medical image and artificial intelligence technology crossing, in particular to an intelligent auxiliary diagnosis method and system for a frozen section in a thyroid cancer operation. According to the technical scheme, the method comprises the following steps that a thyroid tissue intraoperative frozen section to be diagnosed is prepared, and the frozen section does not need to be dyed or subjected to section sealing treatment; and scanning the frozen section by using a terahertz near-field imaging system to obtain terahertz near-field image data of the frozen section. According to the method, the terahertz near-field imaging technology is adopted, the situation that cell nucleus morphology is difficult to observe due to ice crystal artifacts in a traditional frozen section is avoided, cancer cells are automatically recognized in combination with an AI model trained based on large-scale labeling data, and the objectivity and consistency of diagnosis are remarkably improved; meanwhile, the steps of dyeing and mounting are omitted, and rapid imaging and analysis are realized, so that the intraoperative diagnosis time is greatly shortened, and a generated visual contour report also provides visual decision support for pathologists.

The present application relates to a kind of high temperature lens cooling protection device of universal adjustment, including cast-off component, cooling system, lens, lens fixed assembly and adjusting component;The lens fixed assembly includes fixed sleeve and moving piece, the lens is located inside fixed sleeve, the moving piece is connected fixed sleeve, for driving fixed sleeve moves along axial direction;The lens fixed assembly connects cast-off component, and the cast-off component is used to adjust the height and inclination angle of lens fixed assembly;The cooling system is connected with fixed sleeve by adjusting component, and the cooling system is used to transport cooling gas into fixed sleeve, and the adjusting component is used to adjust the rate of cooling cooling system to transport cooling gas.Compared with prior art, the present application has the advantages of convenient operation, high diagnostic efficiency etc..

The application belongs to the technical field of pathological grading, and particularly relates to a lymphoma histological grading method, which comprises the following steps: screening a plurality of regions of interest rich in diagnostic information in a lymphoma pathological section image; inputting the regions of interest into a trained Hover-Net nuclear segmentation model to output a cell separation image; the cell separation image can mark the regions of different cell types, the positions of each cell and the classification information thereof; inputting the cell separation image into a classifier to output a histological grading result and the number of each cell type. The application improves the diagnostic accuracy and efficiency of follicular lymphoma pathological grading by efficiently recognizing lymphoma pathological section images of different pathological grades through a Hover-Net nuclear segmentation model.

The invention discloses a lower limb ischemia assessment system based on thermal imaging key area positioning and temperature difference analysis. A human body target is captured and thermal images of different parts are collected through an automatic moving thermal imaging collection unit; carrying out key point detection and region segmentation by utilizing a deep learning model through preprocessing, and accurately dividing an anatomical region; extracting the calibrated area temperature and calculating the temperature difference; finally, lower limb ischemia is evaluated, a result is output, thermal imaging automation and high-precision collection are achieved, and temperature measurement accuracy and standardization are improved. Objective and reliable evaluation is provided, diagnosis efficiency and accuracy are improved, and personal errors are reduced.

The invention discloses a rapid detection method for echinococcus granulosus based on RPA-CRISPR / Cas, and belongs to the technical field of molecular diagnosis. The method comprises the following steps: extracting DNA of a sample to be detected; the specific primers are used for RPA isothermal amplification, a reaction system contains a buffer solution, upstream and downstream primers, RPA enzyme, magnesium acetate and template DNA, and reaction is carried out at 39 DEG C for 20 minutes; and mixing the amplification product with LbCas12a protein, sgRNA and a fluorescence report probe, carrying out CRISPR / Cas reaction at 37 DEG C for 30 minutes, and judging the result through a real-time fluorescence signal. Constant-temperature rapid amplification is achieved through RPA, high-sensitivity and high-specificity detection of the echinococcus granulosus can be achieved under the site condition by combining high-specificity recognition and trans-cleavage activity of CRISPR / Cas12a, the sensitivity reaches the single-site copy number, operation is easy and convenient, complex temperature control equipment is not needed, and the method is suitable for primary medical treatment and site quarantine.

The embodiment of the invention discloses a vehicle diagnosis method and device based on voice interaction and electronic equipment, the method is applied to diagnosis equipment, and the diagnosis equipment is provided with a pre-trained vehicle diagnosis voice interaction model; the method comprises the following steps: acquiring first voice data of a user and first vehicle data of a target vehicle; determining a diagnosis scene type and an initial diagnosis node of the target vehicle according to the first vehicle data; determining a target diagnosis instruction of the target vehicle according to the first voice data and the diagnosis scene type; according to the target diagnosis instruction and the initial diagnosis node, matching to obtain a target diagnosis node path of the target vehicle; the target vehicle is diagnosed according to the target diagnosis node path, and a diagnosis result is obtained; performing information extraction and semantic conversion on the diagnosis result to obtain a diagnosis broadcast text; and broadcasting the diagnosis broadcast text to the user. According to the embodiment of the invention, when the vehicle is diagnosed, the diagnosis efficiency can be improved.

The application discloses a pathological feature recognition and negative exclusion method based on microscopic imaging, comprising the following steps: S1, collecting pathological section images and digitally generating original microscopic image data; S2, preprocessing the original microscopic image; S3, constructing a pathological image recognition network that fuses a transformation coder and a gated dynamic receptive field mechanism, and outputting a pathological feature vector; S4, modeling the context through attention guidance and category perception decoder, and outputting an image classification result; S5, constructing a discriminant boundary separation model based on positive and negative sample embedding, and judging the negative exclusion; S6, combining uncertainty and boundary distance for confidence weighted evaluation, setting a dynamic threshold, and screening out low-confidence samples; S7, encoding the classification result and the negative label into structured data, and sending to a diagnosis assistance system. The application fuses multi-scale modeling and negative screening mechanism, realizes intelligent recognition of pathological images and reliable diagnosis output.

The invention relates to a fault recording and intelligent diagnosis method for a pumped storage hydropower station. The method is suitable for the technical field of water conservancy and hydropower engineering. According to the technical scheme, the method comprises the following steps: receiving operation condition parameters of a hydropower station, determining a condition identifier based on the operation condition parameters, and respectively determining an electrical quantity sampling frequency and a mechanical quantity sampling frequency based on the condition identifier; based on the electrical quantity sampling frequency and the mechanical quantity sampling frequency, recording data corresponding to the working condition identification are obtained, and the recording data comprise electrical quantity sampling signals and mechanical quantity sampling signals of the hydropower station; feature extraction and fusion are carried out on the recording data corresponding to the working condition identification, a trained basic diagnosis model is input, and a diagnosis result including the probability of various preset faults is output; if a fault with the probability greater than a preset value exists in the probabilities of various preset faults, outputting a diagnosis result; on the contrary, based on hydropower station operation condition parameters, a fault rule base of the auxiliary diagnosis model is reasoned according to rules, and the diagnosis result is corrected.

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.

This invention discloses a device and method for testing the airtightness of a power battery pack for new energy vehicles, belonging to the field of battery testing technology. The device includes a gantry frame with a translation mechanism on its inner side and a lifting mechanism at its bottom. An openable and sealable top cover is mounted on the lifting end. A sealed box is located below the top cover, and the two are interlocked to form a closed chamber. A vacuum pump and a barometer are connected to the outside of the box. A clamping mechanism at the bottom of the top cover adaptively clamps the battery pack. A booster water pump is mounted on the top, connected to the battery pack's medium injection port via a water pipe. A valved exhaust pipe is located on the top of the pack. During operation: first, the booster water pump fills the battery pack with anti-leakage liquid, and then exhausts the gas until there are no air cavities inside the pack. The translation mechanism moves the pack above the sealed box, and the lifting mechanism descends to press the top cover and box together to form a sealed chamber. The vacuum pump is activated to create a negative pressure, resulting in a pressure difference between the inside and outside of the pack. If there are pores in the shell, anti-leakage liquid seeps out, forming a wet stain. A monitoring camera on the inner wall of the chamber captures images to locate the leakage area, and a barometer assists in monitoring pressure changes. This invention eliminates the need for prolonged pressure drop monitoring, enabling rapid and visual localization of leaks in large-capacity battery packs. It can also be connected in series with conventional dry-filling detection, balancing efficiency and location capabilities.

According to the multi-label automatic diagnosis and evaluation system and method for ankylosing spondylitis, pelvic X-ray film images and clinical data are combined, automatic diagnosis of ankylosing spondylitis is achieved through a multi-label classification model, and the severity degree of injury of sacroiliac joints and hip joints is evaluated. The system adopts a prior attention mechanism, can automatically focus a key area in an image, and further improves the diagnosis efficiency. Experimental results show that the system still has relatively high diagnosis accuracy in an environment with limited resources, and can be comparable with diagnosis results of experienced doctors. The multi-label classification model with the priori attention mechanism provided by the invention provides a promising and cost-effective tool for diagnosis and evaluation of ankylosing spondylitis, and has important practical application value.

The application provides an intelligent diagnosis method and system for chemical enterprise safety, relates to the technical field of data processing, and constructs a multistage safety element index library; extracts original business data and judges index compliance, determines index parameters and index scores of four-stage control indexes in the safety element index library; carries out multistage weighted summary processing according to the index scores, and generates safety evaluation results of each stage in sequence, can automatically diagnose the safety of the chemical enterprise, and outputs enterprise safety management states and missing items. Compared with traditional chemical safety element evaluation, the application can improve safety diagnosis efficiency and accuracy.

The invention discloses a lesion discrimination method and system based on an internal image of a digestive tract, and relates to the technical field of medical image processing, and the method comprises the steps: collecting an endoscopic image containing intracavity pressure change, and carrying out the illumination standardization and polar coordinate system mapping of the image, so as to eliminate the illumination difference and the shooting angle influence. And through low-amplitude step inflation stimulation, obtaining a response frame of an inflation stage, and substituting the response frame into the health potential energy model to generate an anti-fact health image. Thirdly, deformation increment and non-conservation residual errors are calculated based on optimization of a joint energy function, topological difference analysis is conducted through multi-stage pressure trajectory analysis, and a focus area is judged according to structural connectivity changes. And in combination with persistence and abnormal intensity analysis, outputting a lesion judgment result. The problem that illumination change and image quality are inconsistent in a traditional method is solved, accurate judgment of the lesion area is achieved, accuracy and stability of lesion detection are improved, and the method has high clinical application value.

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.

This invention provides a method, apparatus, and system for monitoring gas component polymerization curves in enclosed switchgear, belonging to the field of monitoring technology. It includes: collecting SF6 gas decomposition product samples and feeding them into the monitoring device; the samples are mixed gases collected after simulating a fault event in a fault-generating device filled with SF6 gas and conventional internal insulating media; analyzing the decomposition components of each sample from a single fault event to determine the concentration values ​​of SO2, H2S, and CO, obtaining a time-series sequence of decomposition component data; performing curve polymerization to obtain SO2 polymerization curves, H2S polymerization curves, and CO polymerization curves; establishing the correspondence between the slope k of the curves changing over time and fault characteristics, storing it as a diagnostic reference model; collecting gas samples from the equipment under test and feeding them into the monitoring device, analyzing the decomposition components and performing curve polymerization, comparing the obtained polymerization curves with the stored diagnostic reference models to determine the fault type of the equipment under test.

The application provides a polysilicon production equipment fault diagnosis method and device, a server and a storage medium, and relates to the field of equipment fault diagnosis. The method comprises the following steps: acquiring target equipment collected sample data and first historical sample data within a preset time window; determining a first set composed of equipment parameters and a second set composed of equipment parameters and corresponding upstream and downstream process parameters; further determining the first robust distance of the equipment parameters according to the equipment mean vector and the equipment covariance matrix; determining the second robust distance of the equipment parameters and the corresponding upstream and downstream process parameters according to the overall mean vector and the overall covariance matrix; if the first robust distance is greater than the first robust distance threshold and / or the second robust distance is greater than the second robust distance threshold, the running state is determined to be an abnormal state. The comprehensiveness of the diagnosis and the accuracy of the diagnosis result are ensured, the stability of the benchmark during diagnosis is ensured, and the diagnosis efficiency is improved.