48 results about "The primary diagnosis" patented technology

The invention provides an automobile intelligent fault diagnosis and maintenance auxiliary method and system combining subjective and objective information and a cloud model. The method comprises steps of acquiring automobile objective original diagnosis data through an OBD diagnosis port; carrying out preprocessing on the data, judging whether fault codes are in the preprocessed data, and if yes, analyzing the fault codes and providing automobile maintenance assistance; if no, carrying out quantitative conversion on acquired automobile subjective uncertainty fault sign qualitative value through a cloud model method, and comparing the qualitative value with a standard value in an automobile system standard database, wherein the system automatically diagnoses automobile fault parts initially; diagnosing the automobile fault reason through a case-based reasoning method based on a normal cloud mode; and using the rule-based reasoning to finish diagnosis and elimination of automobile faults. According to the invention, automobile faults caused by a complex condition that an automobile has a fault without a fault code or by damage of non-electronic elements are solved; automobile fault diagnosis can be highly efficiently and rapidly finished; maintenance steps are normalized; and maintenance price is properly set.

The invention discloses a multi-sensor signal fusion technology-based fault diagnosis method for wind turbine blades. According to the method, the problems of lack of fault information and the like caused by the insufficiency of sensors is solved by adopting a plurality of sensors. An independent classifier is used for performing primary diagnosis on information acquired by each sensor, so as to determine the possibility that to-be-diagnosed faults belong to different faults; and the decision fusion diagnosis is performed by adopting a fuzzy integral fusion technology based on that the importance degree of information output by each classifier is adequately considered. According to the fault diagnosis method disclosed by the invention, classified results of all the classifiers are integrated, and the importance degree of each classifier is considered, thus effectively improving the accuracy of the fault diagnosis on the wind turbine blades.

The invention relates to an improved D-S evidence theory-based lithium battery fault diagnosis method, which is used for determining the state of a lithium battery. The method comprises the following steps: primarily diagnosing the fault of the lithium battery by utilizing at least two diagnosis methods; according to the primary diagnosis results, constructing the evidence body corresponding to each diagnosis method, and calculating the elementary probability distribution function of each evidence body; based on the elementary probability distribution function of each evidence body, correcting the weight of each evidence body to obtain weighed evidence body; calculating the confidence degree of each nominalized focal element, obtaining a combination rule according to the confidence degree, and fusing the evidence body and the weighed evidence body corresponding to the diagnosis method according to the combination rule, to obtain the fused diagnosis result; and according to the fused diagnosis result, determining the state of the lithium battery by applying a decision rule. Compared with the prior art, the improved D-S evidence theory-based lithium battery fault diagnosis method has the advantages of being accurate in diagnosis results, high in evidence utilization rate, high in diagnosis precision, and the like.

A plant diagnosis system friendly to a user (a plant-operating organization) for identifying an abnormality of an objective plant or machinery thereof, is provided. The plant diagnosis system includes a diagnoser's sub-system including a diagnosis computer and a terminal for a diagnosis expert and is connected via a network to a user's sub-system including a terminal for communicating with the diagnoser's sub-system. The plant diagnosis system provides a simple but repetitively available primary diagnosis and a detailed secondary diagnosis including the expert's judgement, which are selectively available on the user's initiative. The diagnosis computer receives a diagnosis request and plant state variables of the plant or machinery and executes the primary diagnosis and a part of the secondary diagnosis based on the plant state valuables.

The invention provides a novel power transformer fault combined diagnosis model building method and a fault diagnosis method; the diagnosis model building method comprises the following steps: using at least two of the following diagnosis models to primarily diagnose the oil dissolving gas analysis data: a Naive Bayes diagnosis model, a RVM diagnosis model and a matter-element diagnosis model; carrying out weighted average for the primary diagnosis result, using the secondary RVM diagnosis model to make the secondary diagnosis, thus obtaining the combined diagnosis model; using the combined diagnosis model to make fault diagnosis. The method can fully utilize advantages of various diagnosis models, thus effectively improving the diagnosis precision and validity.

The invention relates to a wireless non-invasive human physiological parameter collection, detection and intelligent diagnosis system, which consists of a wireless human physiological parameter collection system, a human and machine interaction and data processing terminal, an intelligent expert diagnosis system and a client end, wherein the wireless human physiological parameter collection system is used for monitoring multiple signals of bioelectricity and the like of a user, and transmitting the signals to the human and machine interaction and data processing terminal in a wireless way; the human and machine interaction and data processing terminal is used for packaging, storing and uploading the data to a cloud server, and distinguishing if the user has an emergency condition or not in real time; if the user has the emergency condition, an emergency alarm is sent to a user guardian and a linkage hospital; the intelligent expert diagnosis system is used for utilizing massive detection data of the user to diagnose the health condition of the user according to the typical body condition features and experience databases, and proposing health-care suggestions; if a suspected symptom of the user is found, the primary diagnosis result and related data are sent to the linkage hospital; the intelligent expert diagnosis system also has the self-learning capability; the linkage hospital is used for treating the emergency condition, and checking, verifying and treating the suspected symptom.

A method and chart for alerting a medical caregiver to undetected or developing complications associated with, or resulting from, initially diagnosed or suspected medical problems. First, a list is prepared of possible complications that might occur based upon the initial diagnosis. Signs of the complications are concisely listed on the chart. The concise chart is posted at, or is otherwise available or near the patient's location, such as at a hospital bed or nurse's station, or other easily observable locations. Thereafter, various caregiver personnel who attend to, or routinely visit, the patient for performing various services, routinely check the listed signs against the patient's condition when the patient is visited. Each caregiver person records the presence of, and any observed changes in, the listed signs that are observed giving the time of the observation. Lastly, an appropriate caregiver physician is alerted to the noted signs for diagnosing and prescribing appropriate treatments. These complications which are present or are developing in the patient can be recognized quickly and appropriate treatment may be undertaken immediately when needed.

Disclosed is a diagnostic processing system including an onboard terminal system and a server connected together via radio communication channels. The onboard terminal system is provided with a data receiving unit for receiving data from sensors arranged on a self-propelled machine and a first diagnostic unit for diagnosing an abnormality of the self-propelled machine, and the server is provided with a second diagnostic unit for diagnosing the abnormality of the self-propelled machine. The diagnostic processing system is configured such that one of the first diagnostic unit and second diagnostic unit performs a primary diagnosis as to the abnormality of the self-propelled machine based on the data received at the data receiving unit and transmits a result of the primary diagnosis to the other diagnostic unit, and upon receipt of the result of the primary diagnosis, the other diagnostic unit performs a secondary diagnosis based on the result of the primary diagnosis.

The invention discloses a mining drill fault intelligent identification method which mainly comprises a primary diagnosis based on signal processing and a secondary diagnosis based on hybrid intelligence. According to the primary diagnosis based on signal processing, signal processing methods such as time domain and frequency domain analysis, wavelet packet analysis and empirical mode decomposition are utilized to extract a drill fault feature, a characteristic value is obtained through calculating and doing statistics, and a drill fault is judged preliminarily. According to the secondary diagnosis based on hybrid intelligence, the feature of the primary judgment is taken as an input, a support vector machine, an expert system, a fault tree analysis are used to diagnose the drill fault, and a diagnosis result is obtained by using voting technology. According to the mining drill fault intelligent identification method, a plurality of signal processing technology and intelligent diagnosis methods are integrated, the accuracy of drill fault diagnosis can be effectively raised, the examination and repair times are reduced, and the drilling cost is reduced.

The invention discloses a rapid fault diagnosis method used for a microgrid, which adopts a mode of step-by-step diagnosis and comprises the following steps that firstly, primary diagnosis is carried out by utilizing the switch information of a network, i.e. newly added passive sub-networks are judged as fault regions and fault elements according to the difference of before and after topology analysis results of faults, and for simple faults, a unique fault region and fault element can be determined. For complicated faults, a plurality of suspected fault solutions can be possibly presented in the result of the primary diagnosis of the switch information. At this moment, a second-step diagnosis is entered, and the diagnosis is carried out by utilizing the protection information of the network. A new target function is established in protection information diagnosis by utilizing the protection information of the elements; the fault diagnosis problem of the microgrid is expressed as the 0-1 integer programming problem; a genetic algorithm and tabu search mixed strategy is introduced to solve the target function; and the fault elements are determined by the optimal solution. The invention can effectively improve the fault diagnosis efficiency, reduces the time of fault diagnosis and improves the fault diagnosis quality.

A telemedicine system to enable remote pre-diagnosis based on one or more messages from a user is described. The telemedicine system includes a user interface to enable a user to generate a telemedicine query requesting a preliminary diagnosis of a medical condition. The system targets the telemedicine query to an appropriate responder and stores the telemedicine query and any follow-up messages sent in response to the telemedicine query.

The invention discloses a comprehensive analysis method of multi-dimensional biology opto-acoustic image spectra. The method comprises steps as follows: establishing a disease sample library of certain biological tissue; illuminating a certain sample with laser having a certain wavelength to produce an optoacoustic effect, and collecting a two-dimension opto-acoustic image of the sample; performing two-dimension Fourier transform on the image, obtaining two-dimension opto-acoustic spectrograms, and extracting characteristic spectra of the two-dimension opto-acoustic spectrograms; continuously changing the wavelength of the illuminated laser, and combining a plurality of groups of two-dimension opto-acoustic spectrograms and characteristic spectra into three-dimension opto-acoustic spectrograms and three-dimension characteristic spectra according to different wavelengths; establishing a three-dimension opto-acoustic spectrogram library of all samples of the biological tissue; and acquiring three-dimension opto-acoustic spectrograms of to-be-diagnosed tissue, comparing and matching spectrogram data in the library, and obtaining a primary diagnosis result. According to the method, with the adoption of the optoacoustic effect of the tissue and through analysis of the three-dimension opto-acoustic spectrograms of the to-be-diagnosed tissue, disease conditions are judged in two aspects of a spatial domain and a frequency domain, so that the accuracy of the primary diagnosis is effectively improved.

A plant diagnosis system friendly to a user (a plant-operating organization) for identifying an abnormality of an objective plant or machinery thereof, is provided. The plant diagnosis system includes a diagnoser's sub-system including a diagnosis computer and a terminal for a diagnosis expert and is connected via a network to a user's sub-system including a terminal for communicating with the diagnoser's sub-system. The plant diagnosis system provides a simple but repetitively available primary diagnosis and a detailed secondary diagnosis including the expert's judgement, which are selectively available on the user's initiative. The diagnosis computer receives a diagnosis request and plant state variables of the plant or machinery and executes the primary diagnosis and a part of the secondary diagnosis based on the plant state valuables.

A preliminary pathogenesis diagnosis method based on a knowledge map includes the following steps: 1, collecting electronic medical records to construct a original dataset; 2, performing entity identification and relation extraction on the electronic medical records to construct an entity and relationship dataset in the RDF format; 3, constructing a knowledge map based on the above datasets; 4, constructing a symbiotic relationship model that predicts the relationship between diseases through the knowledge map; 5, preliminarily diagnosing the pathogenesis based on the symbiotic relationship model. The method, by using a deep learning algorithm represented by a convolutional neural network and a recurrent neural network, constructs the knowledge map, and uses the correlation of the medicalrecord data to extract high-level abstract attributes from the original information such as the medical record texts.

A computer assisted cardiogram diagnostic system, comprises a diagnosing unit, for obtaining a plurality of preliminary diagnostic results according to the cardiogram signals; and an amending unit, for judging the plurality of preliminary diagnostic results, and amending the plurality of preliminary diagnostic results using conflict eliminating rules, in order to obtain amended diagnostic results.

The invention discloses a sleep snore monitoring method and system based on the bass ratio. The method includes the following steps of recording sleep snores of a patient at the whole night, extracting each sleep snore signal in the record, conducting octave frequency band filtering on each sleep snore signal, obtaining multiple levels of octave frequency band sound pressure or multiple sound pressure level values of each sleep snore signal so as to obtain the bass ratio of each sleep snore, and monitoring the sleep snores at the whole night according to the bass ratio of each sleep snore. According to the method and the system, by calculating the bass ratio of the sleep snores of the patient, whether the snores of the patient are normal snores or abnormal snores is judged, the serious degree of the obstructive sleep apnea-hypopnea syndrome (OSAHS) is judged, and the primary diagnosis is provided for the patient; meanwhile, the selectable method is provided for the subjective evaluation on the post-operation effect.

The shape of the urine stream can be used to measure both the flow rate and orifice geometry. The measurement of the shape of the urine stream can be a useful diagnostic tool for medical practitioners since it provides a noninvasive method of measuring urine flow rate and urethral dilation. A novel diagnostic visual voiding device (VVD) measures Symptoms of Lower Urinary Tract Dysfunction (LUTD) such as urinary incontinence. The VVD consists of a lightweight foldable booth with LED lights, microphone, video cameras on left side, top, face, and container that display stream height, width, angle, distance, face grimacing, container height and flow versus time. The VVD will improve the initial diagnosis of Lower Urinary Tract Dysfunction (LUTD) and also improve LUTD by providing subsequent diagnosis after treatment. It enables the patient to video and audio record urinary voiding in privacy. The video and audio recording hardware and software enables the video and audio recording to be sent in electronic form to the urologist at a location other than where the VVD is located.

The invention discloses a steam turbine oil product online monitoring and analyzing system and method for nuclear power. The system comprises an oil product online monitoring cabinet, a DCS system anda monitoring center, wherein the oil product online monitoring cabinet is used for making an analysis test on the lubricating oil of a steam turbine oil tank so as to generate steam turbine lubricating oil product online monitoring data. The DCS system is used for sending the steam turbine lubricating oil product online monitoring data to the monitoring center. The monitoring center comprises a basic data analysis module, an early warning module and a fault diagnosis module, wherein the basic data analysis module is used for conducting calculation and analysis on the steam turbine lubricatingoil product online monitoring data. The early warning module is used for monitoring results from the basic data analysis module, and when the steam turbine lubricating oil product online monitoring data is abnormal, the early warning module can generate alarm information. The fault diagnosis module is used for generating a preliminary diagnosis result through using a fault tree according to abnormal monitoring data. According to the online monitoring and analyzing system and method, the improved risk is small, abnormal parameters can be timely informed, and the primary diagnosis result can beaccurately provided.

The invention discloses a fault diagnosis system based on the evidence theory, and the system comprises a sensor acquisition module, a data preprocessing module, a primary diagnosis module, a comprehensive diagnosis module and a result display module. The sensor acquisition module collects data of transformer parameters through a plurality of sensors. The data preprocessing module is used for carrying out the preprocessing of the data, and the primary diagnosis module is used for carrying out the primary diagnosis of a device according to the preprocessed sensor data. The comprehensive diagnosis module uses the evidence theory to carry out the comprehensive diagnosis of the device according to the primary diagnosis result. The result display module uses an LED display to display the faultdiagnosis result in real time, and performs early warning when the device is diagnosed to have a fault. The beneficial effects of the invention are that the system employs the neural network for processing the sensor data after fuzzification to obtain a primary diagnosis result of transformer fault; the system employs the evidence theory for further diagnosing the transformer fault according to the primary diagnosis result, thereby improving the accuracy of the diagnosis result.

The invention discloses an SF6 device secondary fault diagnosis method based on mass data parallel computation. According to the corresponding relationship between the SF6 decomposed gas component content and the fault type in an electrical device, firstly, a secondary fault diagnosis model of an SF6 electrical device is parallelly constructed, and the modeling efficiency is improved. Then parallel diagnosis of the SF6 electrical device is achieved, the diagnosis rapidity and accuracy in a big data environment are improved. The parallel diagnosis process of the SF6 device secondary fault diagnosis method is that each lab diagnoses different pieces of data at the same time, specifically, a decision tree model is employed to carry out primary diagnosis on the pieces of data and determine whether the device has a fault, and therefore the mass data processing speed is increased. For fault data, the fault type is accurately determined through a neural network model. By employing the SF6 device secondary fault diagnosis method, the diagnosis result can be obtained only through simple data processing, the demand on the professional skill of an operator is greatly reduced, when a lot of SF6 electrical devices are diagnosed, the diagnosis efficiency is greatly improved.

The invention discloses a code recommendation method and system, corresponding equipment and a storage medium. The method comprises the steps: obtaining a medical record home page diagnosis text as the input of a trained first sub-eye inference model, the diagnosis text at least comprises a main diagnosis text, wherein the first sub-eye inference model comprises a deep learning language model anda sub-eye projection layer linked with the output of the deep learning language model, and the deep learning language model is used for at least outputting a main diagnosis semantic vector based on aninput diagnosis text; in response to the fact that the diagnosis text does not include the secondary diagnosis text, the sub-view projection layer is used for determining the sub-view classificationprobability of the corresponding primary diagnosis text for each international disease classification ICD based on the primary diagnosis semantic vector; and taking the m sub-items with the highest classification probability for the main diagnosis text as recommended main diagnosis ICD sub-items of the main diagnosis text. According to the invention, corresponding ICD codes can be intelligently and automatically recommended based on texts, and the accuracy and the efficiency are high.

The invention provides a diagnosis device and method capable of achieving comprehensive diagnosis of multiple devices. The method comprises the steps as follows: web diagnosis substations collect datafrom an abnormal device according to multiple sensors and obtain primary diagnosis results; a web server receives remote diagnosis requests made by the web diagnosis substations and receives the datacollected by a local web diagnosis substation and the primary diagnosis results of the other remote web diagnosis substations; multiple primary diagnosis results given by various web substations arecollected according to a cooperative diagnosis instruction transmitted from a console and the final diagnosis result is determined according to the multiple primary diagnosis results and is transmitted to the console. A device interface comprises a common interface and unique interfaces related to diagnosed devices, wherein the common interface is used for achieving data transmission and connection between the web diagnosis substations and the web server; and the unique interfaces are designed according to the types of the diagnosed devices, so that a development team can achieve data collection and diagnosis of multiple types of devices only through designing specific unique interfaces and does not need to develop various diagnosis programs, thereby fundamentally controlling the overall complexity and cost.

This invention is directed to embodiments of systems and methods to identify a subgroup of ADHD at higher risk for complicating conditions that, for example, may be of concern to an ADHD evaluation, may account for attention and behavior symptoms, and may lead a clinician to exclusion of ADHD from primary diagnosis. In one embodiment, a method for identifying a subgroup of attention deficit hyperactivity disorder (ADHD) patients at higher risk for complicating conditions is provided. The method can include receiving, from a user or clinician, evaluation data associated with a plurality of patients identified with ADHD as a primary diagnosis; obtaining EEG data for each of the plurality of patients; determining, based at least in part on the EEG data, an indicator of ADHD, wherein the indicator supports a positive or negative ADHD evaluation; correlating the indicator with a user's or clinician's evaluation data; and based at least in part on the correlation, determining at least one subgroup of the plurality of patients, wherein the correlation provides positive support for diagnosis of ADHD or provides support for further testing for complicating conditions before proceeding with diagnosis of ADHD in at least one patient within the at least one subgroup.

The embodiment of the invention provides a system and method for monitoring an emergency ambulance. The system comprises a monitoring terminal and a monitoring device. The monitoring terminal is used for receiving the primary diagnosis result of an emergency patient on the emergency ambulance and sending the primary diagnosis result to a monitoring server; the monitoring device is used for monitoring the emergency ambulance and sending the monitoring result to the monitoring server; the monitoring server comprises a diagnosis result receiving module, a monitoring task creating module, an execution flow path return module and a monitoring module; the diagnosis result receiving module is used for receiving the primary diagnosis result sent by the monitoring terminal; the monitoring task creating module is used for creating monitoring tasks for the emergency ambulance; the execution flow path return module is used for extracting execution flow paths of the emergency ambulance corresponding to the primary diagnosis result to return to the emergency ambulance; the monitoring module is used for executing the monitoring tasks and judging whether the emergency ambulance executes the execution flow paths or not according to the monitoring result returned by the monitoring device.

The invention discloses an engine controller online diagnosis method and a related device, which are used for reducing the possibility of wrong diagnosis. The method comprises the steps that an engine controller obtains vehicle data of a vehicle-mounted sensor on an engine; the engine controller judges whether the vehicle data are abnormal or not; if so, the engine controller preliminarily screens the vehicle data to obtain abnormal data; the engine controller performs preliminary calculation according to the abnormal data to obtain a primary diagnosis index, the primary diagnosis index comprising a preliminary diagnosis result of the engine controller on the abnormal data and engine information; the engine controller uploads the primary diagnosis index to a cloud platform server, so that a cloud platform performs secondary diagnosis according to the primary diagnosis index to obtain a target diagnosis result; and the engine controller receives the target diagnosis result sent by the cloud platform server.

The embodiment of the present invention provides a system and method for monitoring an ambulance. The system includes: a monitoring terminal, used to receive the preliminary diagnosis result of the emergency patient on the ambulance, and send the preliminary diagnosis result to the monitoring server; a monitoring device, used to monitor the ambulance, and send the monitoring result to the monitoring server. sent to the monitoring server; the monitoring server includes a diagnosis result receiving module, a monitoring task creation module, an execution process return module and a monitoring module; a diagnosis result receiving module, used to receive the preliminary diagnosis result sent by the monitoring terminal; a monitoring task creation module, used To create a monitoring task for the ambulance; the execution flow return module is used to extract the execution flow of the ambulance corresponding to the preliminary diagnosis result and return it to the ambulance; the monitoring module is used to execute the monitoring task, according to the The monitoring result returned by the monitoring device determines whether the ambulance executes the execution procedure.

The invention relates to an improved D-S evidence theory-based lithium battery fault diagnosis method, which is used for determining the state of a lithium battery. The method comprises the following steps: primarily diagnosing the fault of the lithium battery by utilizing at least two diagnosis methods; according to the primary diagnosis results, constructing the evidence body corresponding to each diagnosis method, and calculating the elementary probability distribution function of each evidence body; based on the elementary probability distribution function of each evidence body, correcting the weight of each evidence body to obtain weighed evidence body; calculating the confidence degree of each nominalized focal element, obtaining a combination rule according to the confidence degree, and fusing the evidence body and the weighed evidence body corresponding to the diagnosis method according to the combination rule, to obtain the fused diagnosis result; and according to the fused diagnosis result, determining the state of the lithium battery by applying a decision rule. Compared with the prior art, the improved D-S evidence theory-based lithium battery fault diagnosis method has the advantages of being accurate in diagnosis results, high in evidence utilization rate, high in diagnosis precision, and the like.

The invention relates to a pre-hospital first aid auxiliary system based on mixed reality, which comprises mixed reality display equipment, a patient information sensing module, a physiological sign monitoring module, an auxiliary diagnosis module and a remote consultation module, the patient information sensing module is used for identifying identity information and inputting illness state information, the physiological sign monitoring module is connected with an ambulance monitor through Bluetooth to monitor physiological parameters of a patient, and the auxiliary diagnosis module adopts a decision-level-based multi-modal fusion algorithm to perform diagnosis on the patient. And the remote consultation module pushes audio and video contents to experts in a hospital, synchronously performs remote guidance on a first-aid site, and generates a rescue record at the same time. According to the invention, a first-aid auxiliary diagnosis algorithm is adopted for solving the problem that pre-hospital first-aid diseases are diverse and complex, the disease of a patient can be preliminarily diagnosed, and a disease first-aid process is prompted for medical staff, so that the first-aid process is more efficient and normative.

The invention provides a medical picture fusion imaging colposcopy training system and a picture reconstruction method, the medical picture fusion imaging colposcopy training system comprises an information acquisition module, an image processing module, an information matching module and an information output module, the information acquisition module comprises a picture acquisition interface and a medical record input interface; the image processing module converts the two-dimensional microscopic image into a three-dimensional reconstruction image, and the three-dimensional reconstruction image is numbered and stored; the information matching module matches the medical record information with the three-dimensional reconstruction image; and the information output module outputs the corresponding three-dimensional reconstruction image and medical record information. According to the colposcope examination training system based on medical picture fusion imaging and the picture reconstruction method, the learning curve of the colposcope can be remarkably shortened, non-clinical learning of the colposcope is realized, the combination of images and cases is realized through the establishment of the picture library and the case library, and the learning of primary diagnosis and processing is directly performed; the method is helpful for a beginner to repeatedly learn in real time, and the learning skill can be quickly improved in a short time through learning of a large number of cases.

Deep RNA sequencing is a technology that provides an initial diagnostic for sepsis that can also monitor the indicia of treatment and recovery (bacterial counts reduce, physiology returns to steady-state). The invention can be used for many other hospital conditions, particularly those needing an intensive care unit stay with the attendant risk of bacterial infection, such as trauma, stroke, myocardial infarction, or major surgery.