195 results about "Preliminary diagnosis" patented technology

In a control method and a system for the automatic pre-processing of device malfunctions, first data about a device malfunction are sent to a central control device by means of first terminal equipment. On the basis of the first data and on the basis of stored second data, the central control device automatically selects a group of second terminal equipment that are allocated to various service technicians. A malfunction incident signal and at least a part of the data are then sent to the selected terminal equipment and output thereat. The terminal equipment respectively acquires preliminary diagnosis data relating to the malfunction and availability data from the respective service technician. These data are communicated to the central control device, which automatically evaluates the existing data, selects a specific second terminal equipment from the group and communicates an assignment signal to the selected second terminal equipment.

The invention relates to a clinic remote service system based on artificial intelligence. The system comprises the components of a clinic terminal, a cloud server, a patient terminal, a doctor terminal, a self-service medicine selling terminal, a medicine selling user terminal and a commodity supplementing terminal. The clinic terminal, the patient terminal, the doctor terminal, the self-service medicine selling terminal, the medicine selling user terminal, the commodity supplementing terminal and the cloud server perform data transmission through a network. The clinic remote service system has advantages of establishing a novel medical service system, settling problems of preliminary diagnosis and treatment and health management of a patient, realizing a one-station medical health preserving solution which comprises medical health examination, AI disease diagnosis, inquiry consultation, electronic medicine commerce, chronic disease management, saving diagnosis time and diagnosis cost,and reducing disease risk.

The invention discloses a method for automatically detecting the longitudinal tear of a conveyor belt based on machine vision and belongs to the field of the monitoring of equipment status. The method comprises the following steps of: setting a binary threshold value according to the change of a gray histogram function of an image before and after morphological corrosion treatment so as to realize the separation of a target and a background, performing preliminary diagnosis on a longitudinal tear fault of the conveyor belt by designing a width projection function, extracting characteristic information of the longitudinal tear from a binary image after the longitudinal tear fault is preliminarily diagnosed, and further identifying the longitudinal tear fault of the conveyer belt by using the extracted characteristic information of the longitudinal tear. The method has an intelligent detection function, is suitable for on-line detection of longitudinal tear faults of conveyer belts, and is favorable for detecting the running state of the conveyer belts in real time by a machine vision technology.

The invention relates to a portable wearable monitoring system for vital sign parameters of the human body. The system is composed of a wearable collecting device for vital sign parameters of the human body, a patient monitoring and diagnosing mobile terminal and a medical expert monitoring and diagnosing server. The real-time collection of electrocardio signals, blood pressure signals and body temperature signals of patients are achieved through the wearable collecting device for vital sign parameters of the human body, the data from the wearable collecting device for vital sign parameters of the human body comprising electrocardio data, blood pressure data and body temperature data is received through the patient monitoring and diagnosing mobile terminal, preliminary diagnosis of a disease is achieved, a preliminary diagnosis and treatment suggestion is given, and meanwhile these data can be transmitted to a medical expert and the expert gives a comprehensive diagnosis and treatment suggestion. The portable wearable monitoring system for vital sign parameters of the human body has the advantages of low cost, good portability, real-time monitoring and simultaneous collection of multiple parameters, can greatly reduce the difficulty of monitoring vital sign parameters of patients and the medical cost in use, can be widely used in families, rehabilitation training centers, hospitals and other places, and has a broad application prospect.

The invention relates to a multi-domain collaborative distributed type fault diagnosis method and a system. Each domain of the system comprises a domain manager which can be communicated with other domain managers and assigns cross-domain warning/symptoms to the domain that is most probable to result in the cross-domain symptoms; and the domain managers is responsible for the fault diagnosis of the domains at which the domain managers are located. When the symptoms are exchanged, the current domain can only be communicated with partial other domains which may result in the symptoms so as to becapable of reducing communication overhead. The multi-domain collaborative distributed type fault diagnosis method and the system adopt the symptoms in the domains to carry out preliminary diagnosis,and then estimate the possibility for resulting in each cross-domain warning according to the preliminary fault assumption, improve the estimation accuracy, consider the possibility of wrongly distributed symptoms, calculate one fault symptom probability for each distributed symptom, and therefore can reduce the influence of wrong distribution on the diagnosis.

The invention discloses a medical guidance diagnosis display method and system. The display method includes five portions of a basic information input area, a preliminary diagnosis area, a preliminary diagnosis result and guidance information area, a detailed diagnosis area and a medical guidance diagnosis result area, the preliminary diagnosis area includes a symptom information selection area, an associated symptom recommendation area, a symptoms query area, a selected symptom area and a preliminary diagnosis result area, the preliminary diagnosis result and guidance information area includes an area for generation of a relevant department corresponding to preliminary retrieval diagnosis, a severity evaluation area and a disease name area, the detailed diagnosis area includes an area for detailed diagnosis for a guidance range, a detailed description area of the selected symptoms, an associated symptom recommendation area and an area for selection of other symptoms, and the medical guidance diagnosis result area includes areas of associated diseases, drugs, hospitals and doctors. After page information is input, the information is sent to a server software module to be subjected to analysis and operation of intersection, focused retrieval and the like, careful medical guidance is achieved, and a complicated medical diagnosis process is electronized and informationized. The system has the advantages that the medical guidance diagnosis process is objective, convenient to operate and rapid in reaction.

The invention relates to a power transmission line early fault detection method based on a Kalman filter. The method comprises the following steps: carrying out Kalman filter slow dynamic estimation on a monitoring current signal of a power transmission line to obtain an estimated current signal; comparing the monitoring current signal of the power transmission line with the estimated current signal, and serving the difference value between the monitoring current and the estimated current as a first standard signal; comparing the first standard signal and a fault state current threshold value of the power transmission line and making a preliminary diagnosis according to the comparison result; carrying out Kalman filter fast dynamic estimation on a monitoring voltage signal of the power transmission line to obtain an estimated voltage signal; carrying out square wave fitting on the estimated voltage signal, and serving the difference value between the absolute value and the mean value of a square wave voltage signal obtained after fitting as a second standard signal; and comparing the second standard signal with a mild early failure state voltage threshold value of the power transmission line, and confirming or denying the preliminary diagnosis according to the comparison result.

A system for preliminary diagnosis of ocular diseases is proposed, in which from capturing a plurality of images of the eyes of a person, a final image, corrected by processing the images by a computer application, is obtained. The already mentioned application calculates the percentage of the colors composing the pupillary reflex of each eye, and compares the results with previous reference pictures obtained from normal cases and clinical cases of ocular diseases.

The embodiment of the invention provides a high-speed rail driving equipment fault diagnosis method and device. The method comprises the steps: extracting fault features from one or more collected original data signals of high-speed rail driving equipment based on a signal analysis method; performing preliminary fault diagnosis on the high-speed rail driving equipment based on a classifier according to the fault feature of each original data signal, and acquiring a preliminary diagnosis result corresponding to each original data signal; and fusing the preliminary diagnosis results corresponding to all the original data signals based on a multi-sensor fusion algorithm to acquire the final diagnosis result of the high-speed rail driving equipment. According to the embodiment of the invention, automatic fault diagnosis is carried out on high-speed rail driving equipment, and the diagnosis result is accurate.

A computer-implemented mental and emotional life efficacy development system and method are disclosed which assist lay individuals to measure and improve their mental health and emotional capabilities and life skill deficits to increase the individual's ability to achieve life efficacy. A scoring subsystem measures competencies or deficits in a breadth of critical life skills. The system provides feedback concerning personality traits and mental and emotional deficits and compares the user's scores to the scores of those who are successfully navigating a particular life step. This pinpoints deficits for which the user can receive training and treatment tailored to that user's personality and individual difficulties. Through the collection and aggregating of such symptoms, a preliminary diagnosis is made of the user's mental health and life skills. This diagnosis is subsequently refined before it is used to offer emotion and/or mental health care treatment and life skills training for life efficacy competency.

In a method and a system for supporting the evaluation of a picture of a patient's eye the picture is displayed on a screen. An image-processing algorithm from a number of image processing algorithms is selected due to a preliminary diagnosis selected from a number of diagnoses which are related to diabetes. Each image-processing algorithm of the number of image processing algorithms is associated with a diagnosis of the number of diagnoses and is designed to search for a pattern which is specific to the respective diagnosis. The selected image-processing algorithm then is applied to the picture and a pattern which is detected by the selected image-processing algorithm is marked on the screen in the picture.

The invention relates to the technical field of image processing, in particular to a diabetic retina image quality detection method based on an image analysis technology. The method comprises the following steps of: preprocessing the fundus image, performing morphological treatment, equalization treatment and filtering treatment on the fundus image during the pretreatment process, performing brightness detection, optic disc detection and definition detection after the pretreatment, and finally obtaining the fundus image to be diagnosed, and diagnosing diabetic retinopathy. The preprocessing operation in the method greatly improves the visibility of the retinal fundus image, It is more advantageous for doctors to diagnose and analyze the patients' condition, and the quality inspection operation can greatly reduce the occurrence of the unqualified fundus images taken at the census spots. The fundus images which pass the quality inspection can be used for the preliminary diagnosis of diabetic retinopathy.

The invention relates to an intelligent-community-based intelligent medical system and an implementation method. The system comprises a medical service platform, a data processing platform and a medical service platform; the medical service platform includes an online disease submission system, a message service system, an appointment system and a history consultation system; the data processing platform includes a medical treatment classification database, an intelligent recommendation system and a personal condition history data system; the medical service platform includes a medical resource allocation system and a diagnosis result feedback system; the medical resource allocation system includes a medical examination project module and an expert module. Accordingly, users can submit illnesses online conveniently, and a rich medical database is adopted for conducting preliminary diagnosis on the illnesses, and a lot of time for consultation is shortened; meanwhile, the users can havea clearer understanding of their own illnesses, and communication of the doctors and patients on the illnesses is sped up; the users can make appointments for registration through the internet, hospital resources are updated in real time, unnecessary queuing time is shortened, and personalized medical needs of people are met.

The invention discloses a power battery pack fault fusion diagnosis method and system based on an improved CNN, and the method comprises the steps: carrying out the wavelet packet decomposition of a voltage change signal and an SOC change signal of a lithium battery, and obtaining an energy value to form an input feature vector; enabling the diagnosis network to perform preliminary diagnosis on the power battery pack fault; judging whether the preliminary diagnosis result meets a definite diagnosis condition or not; if yes, obtaining a diagnosis result of the power battery pack; if the diagnosis condition is not met, performing auxiliary diagnosis on the power battery pack fault by adopting a CNN network; performing fusion diagnosis on the preliminary diagnosis result and the auxiliary diagnosis result through a D-S evidence theory method; and judging the fusion diagnosis result to obtain a final diagnosis result. According to the method and system, the structure of the CNN network isimproved, the optimal convolution kernel size in the convolution layer is determined through the BIC criterion, the definite diagnosis condition is judged, and the auxiliary diagnosis network is further adopted for auxiliary and fusion diagnosis, so that the fault diagnosis accuracy of the power battery pack is improved.

The invention discloses a rotating machine fault diagnosis method based on fusion of genetic programming and a weighted evidence theory, comprising a diagnosis model, genetic programming, a weighted evidence theory and a fusion algorithm. Compared with the prior art, the method comprises the steps as follows: firstly, extracting multiple fault feature parameters through genetic programming, and preliminarily diagnosing a rotating machine; secondly, by taking the preliminary diagnosis results of the feature parameters to faults as evidence bodies and taking the diagnosis accuracy of the feature parameters to faults as the weights of the evidences, correcting the current diagnosis result based on historical data of fault diagnosis; and finally, fusing the multiple evidences based on the weighted evidence theory for decision making, thus reducing the uncertainty of fault diagnosis and realizing accurate diagnosis of faults. Experimental results show that the method improves the accuracy of fault diagnosis.

An automobile abnormal sound monitoring and diagnosing device is provided, and belongs to the technical field of automobiles. The device overcomes the problem that only sounds of different zones of automobiles are memorized, and automobiles are not provided with a preliminary diagnosis function in the prior art. The device includes an automobile storage battery supplying components with electric power, a sound collecting module for collecting automobile sound, and a controller used for respectively comparing and matching the automobile sound collected by the sound collecting module with typical abnormal sound cases memorized in advance inside. The controller is connected to an automobile CAN bus network for transmitting automobile running information signals. The sound collecting module is connected to the controller also used for memorizing abnormal sound after the collected automobile sound is successfully matched with abnormal sound parameters memorized inside and corresponding automobile conditions when the abnormal sound occurs. The device monitors and records the abnormal sound of a local part of an automobile under present automobile running conditions and performs preliminary diagnosis.

The invention discloses a citrus forest disease and insect pest intelligent identification and forest information monitoring system, which comprises a mobile identification monitoring platform for acquiring various images and forest data and sending the images and the forest data to an upper computer and a cloud platform, carrying out preliminary diagnosis and sending a preliminary diagnosis result to the upper computer; an unmanned aerial vehicle which collects a remote sensing image, sends the remote sensing image to the cloud platform and the upper computer, processes the remote sensing image, generates an aerial remote sensing diagnosis result and sends the result to the upper computer; an upper computer which receives and displays the various images, the forest data, the preliminary diagnosis result and the aerial remote sensing diagnosis result, and sends a user instruction to the mobile identification monitoring platform and the unmanned aerial vehicle; an upper computer which acquires a multi-source data fusion analysis diagnosis result and a prescription map from the cloud platform; the cloud platform performs multi-source data fusion analysis on the data obtained from themobile identification monitoring platform and the unmanned aerial vehicle, and sends a diagnosis result and a prescription map of the multi-source data fusion analysis to the upper computer, so as toeffectively reduce manpower and time investment.

The invention provides a target classification system. The target classification system can specifically be a knowledge data and artificial intelligence driven multi-disease identification system. The system can be used for: acquiring medical images and non-image medical information; using a medical expert knowledge base for matching information data, and obtaining an ophthalmic disease weight result through a medical knowledge reasoning algorithm; obtaining an ophthalmic disease weight result by using an organ tissue segmentation algorithm and a disease focus recognition algorithm in medical image diagnosis; obtaining weight results by combining knowledge reasoning and medical image recognition, and obtaining a final disease diagnosis result through weighted calculation. According to a medical clinical diagnosis thought, structured processing is carried out on medical data through big data, and automatic diagnosis of diseases is realized through the detection capability of an artificial intelligence model on disease focuses. The system improves the existing doctor seeing mode, realizes artificial intelligence preliminary diagnosis and screening of diseases, effectively relieves the current situation of shortage of medical resources, and has a wide application prospect.

The invention discloses an intelligent medical monitoring system which is characterized in that patient sign monitoring and related data sending are achieved by means of related knowledge of the Internet of things, the patient state is automatically detected to meet the patient requirement or complete alarming, and the patient medical history and in-hospital diagnosis and treatment conditions arerecorded through big data; and the system comprises a medical bed body, a monitoring module, a data detection module, a data transmission module, a control module and an auxiliary diagnosis module, and the monitoring module is a core module of the intelligent medical monitoring system; and the state of the patient is monitored in real time through a camera, some simple gestures are set to represent different appeals of the patient and are identified through an image processing technology, and the control module converts the gestures into corresponding instructions to control a motor to complete corresponding actions or alarm. The system has the greatest advantage that non-professional medical staff can be automatically helped to take preliminary diagnosis and emergency alarm measures by detecting the environment and related parameters of the body feeling of a patient.

The embodiments of the invention provide a hospital treatment management method, device and system. The method includes the following steps: a server receives a registration request sent by a first terminal; the server sends corresponding treatment prompt information to the first terminal according to the registration request, and sends corresponding patient registration information to a second terminal; the second terminal receives input preliminary diagnosis judgment information, and sends the preliminary diagnosis judgment information to the server; the server sends a corresponding preliminary diagnosis expense list to the first terminal according to the preliminary diagnosis judgment information; the first terminal receives the preliminary diagnosis expense list to prompt a patient to perform a payment operation, and sends corresponding payment information to the server after receiving a payment operation by the patient; and the server sends corresponding preliminary diagnosis prompt information to the first terminal after receiving the payment information sent by the first terminal, to prompt the patient to be preliminarily diagnosed. According to the invention, the number of treatment procedures is reduced for patients, patients can implement each treatment procedure easily, and therefore, the treatment experience of patients is improved.

The invention provides an after-sales equipment predictive maintenance cooperation system based on digital twinning, and the system comprises: a construction module which is used for constructing a cooperative maintenance seven-dimensional model based on digital twinning of an equipment manufacturer; an acquisition module which is used for acquiring and transmitting operation state parameters of the equipment based on a digital twinning collaborative maintenance seven-dimensional model; an analysis module which is used for carrying out prediction analysis on the collected equipment operation parameters based on the cooperative maintenance seven-dimensional model based on digital twinning to obtain a prediction result of the equipment; a judgment module which is used for judging whether thestate of the current equipment is normal or not according to the obtained equipment prediction result; a processing module which is used for carrying out preliminary diagnosis on an abnormal problemof the current equipment when the current equipment is judged to be in an abnormal state, so as to obtain a preliminary diagnosis result; and a display module which is used for visually displaying theoperation state, the prediction trend and the preliminary diagnosis result of the equipment to production operators and equipment maintenance participants in real time based on the cooperative maintenance seven-dimensional model based on digital twinning.

The invention relates to an equipment fault diagnosis method based on an improved D-S evidence theory. The method comprises the following steps: step S1, acquiring vibration signals collected in the operation process of the equipment, and extracting time-domain analysis characteristic parameters and time-frequency analysis characteristic parameters; step S2, obtaining the time-domain analysis characteristic parameters and time-frequency analysis characteristic parameters; step S2, based on the pre-constructed RBF, BP and Elman neural network models, taking the extracted time-domain analysis feature parameters and the time-frequency analysis feature parameters as the inputs of the three neural network models for preliminary diagnosis; step S3, fusing the output results of the three neural network models; step S4: diagnosing the fault according to the fusion result. Compared with the prior art, the method calculates the weight correction evidence source, improves the algorithm of the combination rule on the basis of reserving the advantages of the original combination rule, and fully utilizes the high-speed operation of the computer and the complementarity of the multi-source information to improve the quality of the information through the related data and information.

The invention discloses a medical service supply chain platform comprising a registration appointment module through which a registration appointment service is provided by a system APP, a telecommunication operator and a mobile communication operator; a test module through which a test service is provided by all levels of hospitals and medical scientific research institutes participating in collaboration; a preliminary diagnosis/diagnosis/treatment module through which corresponding services are provided by all levels of hospitals, including community service centers, district hospitals, municipal hospitals and provincial hospitals, and all types of specialized hospitals participating in collaboration; a medicine dispensing module through which the services are provided by all levels and all types of pharmacies and all levels and all types of hospital pharmacies; and a recovery/follow-up module through which the services are provided by the community medical service centers. The beneficial effects of the medical service supply chain platform are that the level of the medical service is enhanced and medical cost is reduced by effective collaboration between different medical service providers as for the internal part of a medical service supply chain.