25results about How to "Avoid misdiagnosis" patented technology

The invention discloses a belt conveyor fault intelligent inspection and diagnosis method and system based on digital twinning, and the method comprises the steps: building a digital twinning model of a belt conveyor, and fusing a three-dimensional geometric model and a physical behavior model, constructing a fault simulation model for simulating the incidence relation between different fault types and physical parameters based on a digital twinborn model; collecting and preprocessing multi-source data, loading a fault detection model to recognize an image, and fusing physical parameter data; performing fault reasoning by the intelligent reasoning model; the digital twin model reproduces a fault scene based on the fault simulation model and loads solution simulation; if the result is valid, issuing an instruction; according to the diagnosis method, a digital twin model associated with equipment behaviors is built, comprehensive diagnosis is performed by combining image data and physical parameter data, fault and behavior association is realized, and faults are accurately positioned; the fault simulation model cooperates with the digital twin model to reproduce a fault scene, and loads reasoning result simulation and a virtual-real combined mechanism control instruction to guarantee operation and maintenance safety.

The invention relates to the technical field of data processing and state monitoring, and discloses an intelligent ring main unit fault real-time diagnosis method and system, and the method comprises the steps: constructing a fault feature over-complete dictionary; collecting and preprocessing high-frequency operation data streams of the ring main unit in real time; carrying out online sparse decomposition and manifold mapping based on the dictionary, and converting a high-dimensional signal into a low-dimensional sparse coefficient and manifold features; and a lightweight classification model is used for rapid reasoning, and fault early warning and data uploading are triggered when abnormity is detected. The system comprises a data acquisition module, a preprocessing unit, an edge computing engine, an intelligent diagnosis module and a communication gateway. According to the method, depth dimension reduction and feature enhancement are realized through a manifold sparse coding technology, the bandwidth pressure and the calculation overhead are reduced on the premise of ensuring the fault feature integrity, and the real-time performance and the accuracy of diagnosis are improved.

The utility model relates to the technical field of livestock medical equipment, and disclose livestock veterinarian multifunctional animal holding frame, including base, the surface fixedly connected with support frame at the four corners of base upper end, and the outer surface hinged connection of base one side is connected with the treading plate, the upper end of support frame is provided with the holding frame, and one end of holding frame is provided with the open -and -shut structure. The utility model discloses by pulling down the pull rod, make first hinged link and second hinged link hinged rotation, and drive first positioning frame hinged rotation and reset, when the other end of first hinged link and hinged block hinged rotation, and drive the other end of hinged block and go up and hinged rotation, one end of hinged block drives second positioning frame hinged rotation and reset simultaneously, thereby can bind the head of animal, avoid the condition that animal struggles or escapes because of discomfort or panic, and the practicability of equipment is improved to some extent.

The invention relates to the technical field of power system intelligent maintenance, and discloses a high-voltage circuit breaker fault intelligent diagnosis method and system based on deep learning, and the method comprises the following steps: 1) data collection: collecting multi-dimensional operation parameters of a high-voltage circuit breaker through a sensor system; 2) data preprocessing: carrying out noise filtering, abnormal point elimination, data normalization and other operations on the collected data; 3) deep learning model training: inputting historical fault data and normal operation data into a deep learning network for training, and optimizing model weight through back propagation; and 4) real-time diagnosis: inputting the data acquired in real time into the trained deep learning model, and analyzing the data by the model to obtain a fault diagnosis result. According to the method, technologies such as noise filtering (such as wavelet analysis) and abnormal point elimination (such as an isolated forest algorithm) are adopted, so that the data quality can be effectively improved, interference in model training is reduced, and the diagnosis accuracy is enhanced.

The invention discloses a cerebrospinal fluid sampling and detecting device, and belongs to the technical field of neurology. According to the cerebrospinal fluid sampling and detecting device, a detecting and distributing head, a puncture needle, a control cabinet and a detecting box are arranged on a device base, a sealing sleeve is arranged in the detecting and distributing head, a preliminary checking disc is installed in the sealing sleeve, and a three-way valve is installed between the detecting box and the detecting and distributing head. The cerebrospinal fluid sampling and detecting device solves the problems that an existing cerebrospinal fluid sampling and detecting device cannot carry out split charging to achieve automatic adjusting operation, and the detection effect is affected, according to detection data, the electric actuator is adjusted through the plc module, the three-way valve and the adjusting valve are controlled, and therefore according to the detection result, the cerebrospinal fluid can be firstly split charged, and then split charging is carried out. All optical and electrochemical signals are captured by an optical fiber sensing array plate and a detection box which are arranged in the detection distribution head, and are transmitted to a control cabinet through Bluetooth, so that different detection processing work is carried out in time, treatment schemes are effectively switched, and the detection effect is improved.

The invention discloses a lithium battery micro internal short circuit fault diagnosis method based on dynamic mode decomposition and a radial basis function neural network. Comprising the following steps: S1, preprocessing original voltage time sequence data collected in an operation process of a lithium battery, and constructing an input matrix suitable for dynamic mode decomposition; s2, decomposing the input matrix based on a dynamic mode decomposition method, extracting a dominant dynamic mode, screening and recombining a key mode based on Pearson correlation analysis, and generating a low-dimensional and high-sensitivity fault feature vector; s3, taking the feature vector as an input of a radial basis function neural network, taking an internal short circuit equivalent resistance value as a label, and constructing a nonlinear mapping relation between the feature space and an internal short circuit state; and S4, comparing an internal resistance prediction value output by the radial basis function neural network with a set threshold value or a reference value, calculating a prediction error and evaluating the diagnosis precision. According to the invention, misdiagnosis caused by noise and aging effects is avoided.

The invention discloses a multi-modal intelligent osteoarthritis detection method based on loop optimization, a storage medium and electronic equipment. The multi-modal intelligent osteoarthritis detection method comprises the following steps of multi-modal data acquisition and preprocessing, modal-by-modal alternate training, test set division and uncertainty-based multi-modal fusion diagnosis; and finally, evaluating and optimizing the detection model. According to the method, a weighted aggregation mechanism based on uncertainty is introduced. In clinical scenarios, some modalities may have noise or missing. The model can automatically calculate the entropy value (uncertainty) of each modal prediction result, allocate a higher weight for a high-quality modal and allocate a low weight for a low-quality modal, so that misdiagnosis caused by poor quality of a single data source is avoided, and the diagnosis stability in a complex clinical environment is remarkably improved.

A BMS having a master BMS receiving power from an electric vehicle and at least one slave BMS, in which each of the master BMS and the slave BMS has a first ground terminal and a second ground terminal, the first ground terminal of the master BMS and the slave BMS being connected to a ground terminal of the electric vehicle, the second ground terminal of the master BMS being connected to the first ground terminal of the master BMS, and the second ground terminal of the slave BMS being connected to the second ground terminal of the slave BMS. And a second ground terminal of the slave BMS is connected to a second ground terminal of the master BMS.

The application discloses an ELISA kit for detecting anti-human laminin gamma 1 IgA autoantibody and application thereof. The kit comprises an enzyme-labeled plate coated with human laminin gamma 1 recombinant protein. Preferably, the human laminin gamma 1 recombinant protein is deglycosylated human laminin gamma 1 recombinant protein. Further, the application also provides application of the kit in preparation of a reagent for diagnosing mucous membrane pemphigoid, in particular Laminin gamma 1 IgA type pemphigoid. The kit is the first kit for detecting human laminin gamma 1 IgA antibody by using the ELISA method, has the advantages of simple operation, short time consumption and low cost compared with traditional immunoblotting method, and adopts full-length deglycosylated laminin gamma 1 recombinant protein as an antigen, thereby solving the problem of incomplete antigen epitope coverage in the prior art, and early detection of diseases can be realized, early treatment is achieved, and the life quality of patients is improved.

This invention relates to a machine vision-based system and method for recognizing and assessing gait abnormalities in children, belonging to the field of action recognition, and more specifically to the field of recognizing human actions or behaviors in image or video data. The system includes: a model building device for building an AI gait abnormality recognition and assessment model corresponding to a side-view imaging device; and an intelligent assessment device for intelligently assessing, based on selectively chosen multi-source basic data, whether the child currently being assessed has gait / crawling abnormalities and the number of months of such abnormalities. This invention addresses the technical problem of simultaneously failing to balance the quality and efficiency of gait / crawling abnormality assessment and the inability to simultaneously provide the number of months of abnormalities. It employs a custom-designed AI gait abnormality recognition and assessment model, based on selectively chosen multi-source basic data, to simultaneously complete both the status assessment and the assessment of the number of months of abnormalities, thereby solving the aforementioned technical problems.

This application discloses an ultrasound image processing method, apparatus, electronic device, and storage medium. The method includes: acquiring multiple types of data signals during ultrasound imaging; wherein the multiple types of data signals include at least two of the following: ultrasound echo signal after beamforming, initial wave image before digital scan conversion, and ultrasound image after digital scan conversion; performing no-load detection on each of the multiple types of data signals to obtain corresponding no-load detection results; and combining multiple no-load detection results to determine the final no-load detection result of the ultrasound image. This application performs multiple no-load detections on multiple types of data signals in multiple stages of ultrasound imaging, and combines the results of multiple no-load detections to obtain the final no-load detection result of the ultrasound image, improving the accuracy of no-load image identification, avoiding subsequent functions from making incorrect diagnoses or measurements of the no-load image, and ensuring the reliability and accuracy of ultrasound diagnostic and measurement functions.

The invention provides a relay state diagnosis method and device and a battery management system, and relates to the technical field of relay detection, a first end of a to-be-detected relay is connected with a positive electrode end or a negative electrode end of a battery pack, and a second end of the to-be-detected relay is connected with a load side; the method comprises the steps that after it is detected that a to-be-detected relay executes an action instruction, detection voltage data of a sampling point of the to-be-detected relay are detected in real time, and the sampling point is located at the second end of the to-be-detected relay; acquiring a ratio range and a ratio change rate of the detection voltage of the second end of the relay to be detected to the voltage of the battery pack according to the detection voltage data; determining the actual state of the relay to be tested according to the ratio range and the ratio change rate; and state diagnosis is carried out on the relay to be tested according to the actual state and the action instruction, so that the state of the relay can be diagnosed timely and accurately, and misdiagnosis caused by whole vehicle interference or hardware interference can be prevented.

This application proposes a method and system for aquatic seedling production and cultivation management, relating to the field of aquaculture management technology. The method includes acquiring aquaculture operation information executed by automated equipment in the aquaculture environment and visual information of aquatic seedlings; analyzing the activity status, group aggregation, and feeding response of the aquatic seedlings based on the visual information to identify abnormal behaviors; querying aquaculture operation information associated with the occurrence time of the abnormal behavior to preliminarily identify physiological reactions; continuously acquiring behavioral information of the aquatic seedlings within a preset observation period; determining whether the behavior of the aquatic seedlings has returned to a normal state based on the behavioral information; confirming the behavioral information as a physiological reaction when the behavior of the aquatic seedlings has returned to a normal state; and issuing a health status warning when the behavior of the aquatic seedlings has not returned to a normal state. This application can improve the accuracy and effectiveness of aquatic seedling cultivation management.

The invention provides a multi-phase motor IGBT open-circuit fault identification method and system and a storage medium, and relates to the technical field of motor control, and the method comprises the steps: obtaining the actual rotating speed of a multi-phase motor; when the actual rotating speed is larger than a set rotating speed threshold value, a first diagnosis strategy is started, and the first diagnosis strategy is used for conducting harmonic analysis based on all phases of currents of the multi-phase motor to locate a fault phase where an IGBT open-circuit fault occurs; when the actual rotating speed is smaller than a set rotating speed threshold value, a second diagnosis strategy is started, and the second diagnosis strategy is used for positioning a fault phase with an IGBT open-circuit fault based on the duty ratio and the current of each phase of the multi-phase motor; and generating an IGBT open-circuit fault identification result containing the fault phase. According to the method, different diagnosis strategies are dynamically selected according to the rotating speed of the motor, the fault positioning accuracy can be effectively improved, the misdiagnosis situation is avoided, and accurate identification of the IGBT open-circuit fault under all working conditions is achieved.

This utility model discloses a fully automated biochemical analyzer, including a biochemical analyzer body. A top cover is rotatably connected to the top of the analyzer body. Mounting plates are symmetrically fixed to one side of the top cover. A collection tube is fixedly installed between the two mounting plates. A water inlet pipe is fixedly installed to one side of the collection tube. Multiple diverter heads are installed on one side of each collection tube, and a spray head is fixedly installed at the bottom of each collection tube. This design, by setting up a collection tube, water inlet pipe, diverter heads, spray heads, and ultraviolet disinfection lamp, uses the water inlet pipe to transmit water through the collection tube to the interior of the diverter heads. The spray heads spray disinfectant water onto the top of the partition, disinfecting the interior of the biochemical analyzer body. The ultraviolet disinfection lamp then dries and disinfects the disinfectant water, effectively reducing cross-contamination between samples, ensuring the independence of each sample, and avoiding misdiagnosis or missed diagnosis due to contamination.

The application discloses an intelligent clinical decision support system based on a knowledge graph and voice interaction and an implementation method thereof.The system comprises a voice interaction terminal, a knowledge graph construction module, a natural language understanding module, a dialogue management module, a graph query and reasoning module and a clinical knowledge base.The application converts the dialogue content of doctors and patients collected through voice into structured medical entities and intentions, and generates a diagnosis prompt, an examination suggestion or a drug safety reminder based on path reasoning and probability calculation of a large-scale medical knowledge graph.The system supports active questioning type interaction, makes up for missing key information and provides a clinical auxiliary suggestion with a confidence level.The application can improve the clinical decision efficiency and accuracy, reduce medical errors and is especially suitable for voice interaction scenes such as ward rounds, emergency treatment and primary medical treatment.

A pulse collection and reproduction system, comprising a reproduction terminal (1), a collection terminal (2), and a server terminal, and the technical key points are that a data transmission channel is directly established between the collection terminal (2) and the reproduction terminal (1) in a C / S architecture or a data transmission channel is established through the server terminal as a transfer, and the data of the collection terminal (2) or the server terminal is transmitted to the reproduction terminal (1); the server terminal comprises a communication module for receiving the pulse data uploaded by the collection terminal (2) or sending the diagnosis result to the reproduction terminal (1); a database module for storing the two-dimensional or three-dimensional pulse data uploaded by the collection terminal (2); a deep learning model module for loading the pulse diagnosis model and returning the pulse data uploaded by the collection terminal (2) to the diagnosis result. It has the advantages of convenient use, accurate radial artery positioning, high pulse reproduction accuracy, etc. Better simulate the clinical pulse taking, and improve the accuracy of pulse diagnosis instrument pulse taking.

This invention provides a low-dose CT image reconstruction method, belonging to the field of medical image processing technology. The method is characterized by the following steps: Step A, obtaining two different low-dose, high-noise CT image data, one as input image I and the other as a self-supervised image T; Step B, randomly dividing input image I into proportionally sized image blocks; Step C, randomly removing 60% of the proportionally sized image blocks from Step B, randomly sorting the remaining 40% of the image blocks, and inputting the sorted image block sequence position information into the algorithm model; Step D, after reconstruction by the algorithm model, obtaining the reconstructed image pairs and calculating the L1 loss with T. This reconstruction method requires convenient image data acquisition, allows for active adjustment of the image noise reduction ratio, and the reconstructed image does not reduce the image signal-to-noise ratio, nor weaken details and textures, resulting in high image quality.

The invention provides a multi-instance learning-based grape tire risk hierarchical prediction method. The method comprises the steps of obtaining suspected grape tire pathological image data and clinical parameters; preprocessing the image and cutting the image into image blocks; extracting image block features by using a feature extraction network, and aggregating to generate packet-level features; high-discrimination features are obtained through a multi-instance feature aggregation module; coding the coding network into features, and generating multi-source fusion features with fusion; inputting into the classification network, outputting prediction probabilities of non-grape tyres, partial grape tyres and complete grape tyres, and calculating disease risk scores according to the prediction probabilities of the non-grape tyres, the partial grape tyres and the complete grape tyres; dividing into four risk levels of low, medium, medium and high and high according to the scores, and automatically triggering an auxiliary diagnosis prompt for cases falling into classified easy-to-confuse levels. Pixel-level labeling is not needed, clinical safety and interpretability are considered while classification accuracy is improved, and the method is suitable for grape tire intelligent auxiliary diagnosis of primary and superior medical institutions.

A SiC MOSFET gate oxide layer degradation comprehensive diagnosis method and system, the method comprises the following steps: constructing a data set, and training to obtain a junction temperature estimation function; obtaining actual precursor parameters under actual operating conditions through a measurement circuit, substituting the actual precursor parameters into the junction temperature estimation function to obtain a current actual junction temperature estimation value, substituting the current actual junction temperature estimation value into a linear relationship between the precursor parameters and the junction temperature before the gate oxide layer degrades to obtain the flat band voltage and the threshold voltage of the gate oxide layer without degradation at the junction temperature; calculating the difference between the actual precursor parameters and the precursor parameters at the junction temperature without degradation of the gate oxide layer to obtain a precursor parameter drift amount, and performing comprehensive diagnosis of the gate oxide layer degradation according to the precursor parameter drift amount to obtain a comprehensive diagnosis result. The present application solves the misdiagnosis problem caused by temperature drift in the prior art, improves the accuracy and robustness of the degradation diagnosis, and realizes real-time monitoring and thermal state management of the device working junction temperature state.

The application discloses a kind of based on digital pathology section local high-precision magnifying glass system, comprising: main view module, for loading and display digital pathology whole section image;Magnifying glass module, including nested sub-viewport, for specified local area in the current field of view of the main view module is carried out high-power display;Wherein, the magnifying glass module is shared with the main view module by cloning mechanism Image resources;Display area layer, which is overlaid on the display interface of the main view module, is used to visually mark the corresponding position of the local area in the main view module;Control handle layer, integrated in the display area layer, is used to receive user input to interactively adjust the local area, realize local magnification based on original high-resolution data;Non-repeated loading of image resources significantly reduces server and network pressure;Main view and local view are highly consistent, to avoid misdiagnosis.

The invention belongs to the technical field of molecular diagnosis and enzyme engineering, and particularly discloses a primer group, a kit and a method for detecting CGG repeated amplification diseases. The multiplex PCR system provided by the invention comprises a PCR primer group of which the sequence is as shown in SEQ ID NO: 1 to SEQ ID NO: 7; the DNA polymerase takes Pfu polymerase as a skeleton and comprises mutations such as V93Q, E148K, G150A, S152A or N155A and the like, and the C end of the DNA polymerase is fused with a Cren7 DNA binding protein through a short connecting peptide; and the high GC PCR system comprises LiCl, MgCl2, dNTP (deoxyribonucleoside triphosphate), 7-deaza-dGTP (7-deaza-dGTP) and the like. According to the present invention, the engineering DNA polymerase, the optimized high GC PCR system and the multiple primer group are combined, such that the detection sensitivity and the detection specificity are significantly improved, and the joint detection of a variety of clinical phenotype similar diseases is achieved.

The present application belongs to technical field, specifically relates to a kind of for nuclear power plant loose component positioning and quality estimation monitoring system and method. Including equipment, the equipment is fixed to ground by support device, and multiple acceleration sensors are respectively installed in the surface measuring point position of equipment, sensor is communicated with signal acquisition instrument by BNC connecting line, and the feedback of collision event is given by monitoring system through reading corresponding acceleration signal analysis of signal acquisition instrument.The beneficial effects of the present application are that: by introducing the variational mode decomposition of improved particle swarm algorithm, the present application can adaptively optimize the key parameters of VMD, overcome the shortcomings of empirical parameter setting in traditional method, avoid false components and misdiagnosis problems caused by improper parameter selection, and significantly improve the diagnostic accuracy. Non-stationary collision vibration signal is effectively adaptively decomposed, and the best collision modal component is judged and extracted by kurtosis value and energy value.

This invention relates to the field of material damage diagnosis technology and discloses a hierarchical diagnosis system for aircraft material damage based on transfer learning. The system includes first and second data acquisition units, a mechanism-driven bridging transformation module, a lightweight feature extraction network, a hierarchical diagnosis module, and an output module. The first unit acquires labeled source domain damage signals from the laboratory, while the second unit only acquires target aircraft health status scan data. The mechanism-driven module extracts the damage evolution invariant manifold, minimizes the Wasserstein distance between the source domain and the target domain health distribution, and trains a cross-model transferable projection matrix. The lightweight network outputs transfer features, which are then processed by a cascaded three-level sub-network for anomaly detection, type recognition, and level evaluation, combined with a physical constraint rule base to output the existence, type, and levels 1-4 of the damage. This invention achieves high-precision hierarchical diagnosis across models without requiring labeled samples of damage in the target domain and has online adaptive capabilities.