56 results about "Diagnosis management" patented technology

The invention discloses a ship-shore cooperation-based cabin intelligent diagnosis management system, which comprises a ship-end intelligent sensing layer, a ship-shore cooperation communication layer and a shore-based intelligent decision-making layer, and is characterized in that the ship-end intelligent sensing layer comprises a ship-end data acquisition module, and the ship-shore cooperation communication layer comprises a data transmission module, a shore-end data center and a cloud server; the shore-based intelligent decision-making layer comprises an intelligent diagnosis module, a predictive maintenance module and a ship-shore cooperation module. The invention belongs to the technical field of cabin intelligent management, and particularly provides an intelligent cabin diagnosis management system based on ship-shore cooperation and a predictive maintenance method, which can reduce the equipment failure rate, optimize the maintenance cost and prolong the equipment life cycle through real-time data acquisition, cloud intelligent analysis and dynamic maintenance decision.

The invention provides an intelligent management system for chronic diseases and common diseases of old people. The intelligent management system comprises an old people comprehensive evaluation module, a multidisciplinary collaborative diagnosis and treatment module, a multi-medication risk prediction management module, an intelligent chronic disease follow-up diagnosis management module and a health science popularization knowledge base module, the senile comprehensive assessment module inputs the personal chronic disease map of the patient into a risk factor prediction model for prediction, and outputs a comprehensive assessment result; the multidisciplinary cooperative diagnosis and treatment module is used for the first diagnosis / receiving diagnosis doctor to coordinate multidisciplinary doctors to perform cooperative diagnosis and treatment; the multi-medication risk prediction management module is used for giving comprehensive diagnosis, multi-medication management and risk prediction to the patient through collaborative diagnosis and treatment; the chronic disease intelligent follow-up diagnosis management module carries out follow-up visit tracking, health risk prediction and review prompting on the patient; and the health science knowledge base module matches and pushes corresponding health education knowledge. The accuracy and timeliness of senile chronic disease co-disease risk assessment prediction and management are improved, and a good auxiliary effect is achieved on improvement of the life quality of patients.

The invention provides an intelligent dust remover fault and health state diagnosis management method, device and equipment and a storage medium, relates to the field of dust remover management, and aims to improve the timeliness and accuracy of bag-type dust remover maintenance. The method comprises the following steps: acquiring real-time operation data of key equipment of the dust remover, wherein the real-time operation data at least comprises dust removal fan vibration data, dust removal fan rotating speed, dust removal fan current, dust removal motor vibration data, dust removal motor cooling water flow and dust removal motor current; dividing the real-time operation data to obtain first operation data and second operation data; inputting the first operation data into a trained health state evaluation model to obtain a health index; inputting the second operation data into a trained equipment fault classification model to obtain a classification result; and when the health index is lower than a first threshold value, alarm information is sent out, and fault processing information is generated according to a classification result.

The invention discloses an active diagnosis and treatment cooperation system and method based on artificial intelligence full-process dominance, and belongs to the technical field of medical artificial intelligence, and the system comprises a pre-diagnosis management module which actively carries out the medical care team distribution of a registered patient and completes chronic disease signing, performing intelligent triage after judging the disease progress and the onset risk of the signed patient, generating a pre-diagnosis examination list for the triage patient, and completing the diagnosis and treatment process of the pre-diagnosis stage; the in-diagnosis management module judges the health state of the patient and actively pushes a standardized diagnosis and treatment path to form a chronic disease prescription to be provided for a doctor, and the chronic disease prescription is pushed to the patient after being signed by the system and the doctor; and the post-diagnosis management module is used for automatically generating a dynamic tracking plan, establishing a grading follow-up visit mechanism, generating a follow-up visit scheme and automatically returning prognosis data of the patient to guide system optimization training. The whole process of chronic disease prevention and treatment is dominated through AI, the chronic disease management quality can be improved, the chronic disease prevention and treatment homogenization level is improved, and whole-process high-quality public service supply is achieved.

The invention discloses a children cough variant asthma diagnosis system based on a decision tree, and relates to the technical field of asthma diagnosis, the children cough variant asthma diagnosis system comprises a diagnosis management platform, the diagnosis management platform is in communication connection with a data acquisition module, a diagnosis model construction module, a diagnosis reasoning module and an intervention suggestion module, and the modules are in electric signal connection; the data acquisition module is used for collecting and preprocessing child patient clinical data including symptoms, signs, medical history and multi-dimensional examination data. By integrating the multi-dimensional examination data and the medical history, symptom change trend and family history information of the child patient, the condition of the child patient can be comprehensively and deeply analyzed, reasoning and judgment are performed by applying a branch structure of a decision tree and accurately combining various characteristics, so that subjective and empirical deviations in a traditional diagnosis method are effectively avoided, and the diagnosis accuracy is improved. The accuracy of CVA diagnosis is remarkably improved, a child patient is helped to obtain correct treatment in time, and misdiagnosis and missed diagnosis can be effectively reduced.

The present invention relates to a method for obtaining front, rear, and occlusal surface images of each tooth using a Lens-less camera and a mouthpart provided with a light source, tracking and analyzing consecutive images in a time series using a learned AI algorithm, detecting, diagnosing, managing, and predicting information, and determining whether or not the tooth is a tooth or not. Therefore, not only can the fineness, reliability and effectiveness of tooth diagnosis, treatment and management be improved, but also images of different teeth under the same conditions (imaging direction, magnification, position and the like) can be obtained regardless of the skill of a photographer, and the accuracy and effectiveness of diagnosis and treatment can be further improved. The present invention relates to a mouthpart-type oral cavity camera using a lens-free camera and a dental diagnosis and management system using the same, and can significantly reduce unnecessary time and manpower consumption caused by imaging teeth.

The invention relates to the technical field of oil and gas pipeline valve inner leakage online diagnosis, and discloses an oil and gas pipeline valve inner leakage online diagnosis method based on voiceprint analysis. According to the method, valve operation voiceprint data are obtained through a distributed interface to construct a feature database, a diagnosis framework is designed according to the database, and a multi-source voiceprint collection scheme is determined. And the voiceprint acquisition terminal acquires a multi-dimensional voiceprint signal according to the scheme, inputs the multi-dimensional voiceprint signal into a model after feature extraction to complete inner leakage level judgment and abnormal mode recognition, and outputs a diagnosis result. A diagnosis result is transmitted to the management optimization module, and diagnosis strategy adjustment is achieved through timestamp calibration, mapping table construction and the like. And a three-level evaluation sub-frame is also constructed to assist diagnosis. According to the method, accurate diagnosis and strategy optimization of oil and gas pipeline valve inner leakage are realized, and the diagnosis management level is improved.

The invention discloses a pump station fault diagnosis management system which comprises a data acquisition module, a fault diagnosis module, a fault alarm module and a data management and analysis module. The data acquisition module acquires data through various sensors and preprocesses the data; the fault diagnosis module extracts characteristic parameters, and adopts an artificial intelligence algorithm to diagnose and position a fault; the fault alarm module executes a corresponding strategy according to the fault degree; the data management and analysis module uses relational and non-relational databases to store data, and can perform query, statistics, mining and prediction. The system improves the operation reliability and stability of the pump station, accurately diagnoses faults, optimizes data management and decision, provides guarantee for intelligent management of the pump station, and is suitable for the pump station fields of water conservancy, water supply and drainage and the like.

The invention belongs to the technical field of digital diagnosis, and discloses an enterprise digital diagnosis platform and a use method thereof, and the platform comprises a diagnosis management user unit which is used for carrying out the comprehensive management of a management user based on the enterprise diagnosis information; the enterprise user diagnosis unit is used for analyzing the diagnosis task of the enterprise user and providing diagnosis service and personalized support for the enterprise user according to the diagnosis task; the business authority management unit is used for defining the function access range of the enterprise user and the management user, and performing unified management on the function authority of the enterprise user and the management user based on the function access range; and the user role management unit is used for constructing a diagnosis management role and a diagnosis enterprise role. The method focuses on maintenance of global data, distribution of diagnosis tasks and comprehensive analysis, so that efficient organization and coordination of platform resources are facilitated, and stable operation of the platform and smooth development of enterprise diagnosis work are ensured.

The invention provides a vehicle diagnosis management method for a UDS server, and the method comprises the steps: enabling the UDS server to execute the steps: respectively receiving connection requests from different clients; a corresponding logic address is allocated to each client, and different logic addresses are allocated to different clients; for each client, establishing a diagnostic connection between the client and a plurality of diagnostic session instances under the corresponding logical address; and for each client, receiving and analyzing a diagnosis request sent by the client, processing the diagnosis request according to the diagnosis connection under the logic address, obtaining diagnosis response data, and feeding back the diagnosis response data to the client. The diagnosis requests of different clients are processed under different logic addresses, so that the UDS server isolates diagnosis sessions among different clients, and the processing performance of the UDS server is improved.

The invention relates to the technical field of teaching evaluation and diagnosis management, in particular to an intelligent teaching evaluation and diagnosis system and method based on multi-mode audio and video analysis. Carrying out classroom teaching event identification on the teaching state feature set extracted at the current time; in combination with an MTES technology, classroom teaching event evolution process analysis based on a time sequence model is carried out, and classroom audios and videos are dynamically segmented based on an obtained classroom teaching event evolution process analysis result. According to the method, a mode of comprehensively quantifying the index set bound with each classroom teaching event based on the MTES technology can effectively solve a modal splitting phenomenon among modal acquisition data in a teaching evaluation and diagnosis process; according to the invention, the dynamic segmentation points of the classroom audio and video are screened, locked and calibrated from multiple angles, and the accuracy of the classroom audio and video segmentation segments is ensured.

A method for managing vehicle servicing may capture at least one of a user concern / input related to a requested service or observed vehicle operational parameters. A method for managing vehicle servicing may conduct diagnosis and filtering based on the user input to identify a set of corrective actions to be performed and pre-service steps that may be required prior to service, wherein the diagnosis and filtering correspond to a suite of diagnostic services to be deployed and implemented. A method for managing vehicle servicing may transmit at least one processing result associated with the execution of the suite of diagnostic services and may obtain approval of the service request by a user and schedule the service.

The invention provides an enterprise brand reputation intelligent diagnosis management system, electronic equipment and a medium, and the system comprises a data collection module which is configured to collect basic semantic data which comprises a plurality of first text vocabularies; the data processing module is configured to receive the basic semantic data and label the first text vocabulary based on a preset labeling strategy; the data processing module is further configured to extract a first entity of the basic semantic data; the analysis module is configured to receive the labels, obtain semantics of the first text vocabularies corresponding to the labels on the basis of a semantic model, determine whether the semantics are positive or negative, and analyze the labels according to the number of the first text vocabularies corresponding to the negative and on the basis of a preset model; obtaining the increment of the quantity of the basic semantic data corresponding to the negative values in a future preset time period; and the diagnosis decision module is configured to output a decision strategy based on a preset case library according to the increment and the first entity.

The present application relates to the technical field of dynamic evaluation of spinal morphology, and particularly relates to a child spinal morphology intelligent evaluation method and system based on big data analysis. The method comprises the following steps: S1: obtaining static data and dynamic data, dividing the static data into a high-priority evaluation data set and a basic reference data set, and dividing the dynamic data into a key change data set and an auxiliary monitoring data set; taking the complete time period between the time point when the child spinal morphology first receives medical evaluation and the time point when the system confirms that the child spinal function and morphology reach a steady state as an evaluation and diagnosis cycle, and distributing the static data and the dynamic data to the evaluation and diagnosis cycle to obtain a data-stage distribution result. The present application constructs an intelligent evaluation system, accurately tracks child spinal development, dynamically optimizes data collection and stage distribution through error analysis and similarity comparison, and realizes individualized, efficient and self-adaptive closed-loop diagnosis management.

The invention discloses a diagnosis processing method for a driving chip, and the method comprises the following steps: enabling the driving chip to enter different diagnosis state machines according to the driving state of the driving chip after the driving chip is powered on; the diagnosis state machine performs sub-state switching in the diagnosis state machine according to the fault content and the fault frequency; and the sub-state in the diagnosis state machine determines the fault state of the drive through the numerical value of the fault register and the reset frequency of the drive chip. In order to ensure the safety of the vehicle in any scene, the driving chip self-diagnosis faults in different driving states are monitored in an independent state, fault confirmation and recovery of the driving assembly are managed through temporary faults, confirmation faults and permanent faults, and compared with an existing diagnosis management method, the safety damage of the vehicle can be avoided, and the safety of the vehicle can be ensured. And the normal use of the vehicle can be ensured to the greatest extent.

The present application relates to the technical field of teaching evaluation diagnosis management, in particular to an intelligent teaching evaluation and diagnosis system and method based on multi-modal audio and video analysis, wherein the audio and video dynamic segmentation module combines the teaching state feature data in the historical data to perform classroom teaching event identification on the teaching state feature set extracted at the current time; and combines the MTES technology to perform time sequence model-based classroom teaching event evolution process analysis, and based on the obtained classroom teaching event evolution process analysis result, the classroom audio and video are dynamically segmented. The comprehensive quantification mode of the index set bound to each classroom teaching event based on the MTES technology can effectively solve the modal fragmentation phenomenon between the multi-modal collected data in the teaching evaluation and diagnosis process; the dynamic segmentation points of the classroom audio and video are screened, locked and calibrated from multiple angles, so that the accuracy of the classroom audio and video segmentation segments is ensured.

The application relates to the technical field of internet medical treatment and artificial intelligence, and discloses an internet hospital whole-process intelligent diagnosis and treatment service management system based on artificial intelligence. The method collects multi-source digital phenotype data through a double-granularity aggregation strategy, generates continuous representation of patient behavior state by using a time sequence representation learning model based on a mask autoencoder, separates a disease activity score and a compliance probability score through a causally decoupled double-branch network, calculates a crisis emergency degree score by using a mental and psychological crisis early warning network and triggers a fuse mechanism, realizes post-diagnosis management path switching, emergency resource matching and crisis handover information package generation, and solves the problems of insufficient time resolution, data loss vulnerability, cause coupling confusion and response channel loss in mental and psychological department post-diagnosis acute crisis detection and emergency response.

The present invention discloses a method and system for judging elderly chronic diseases based on data analysis, which belongs to the technical field of elderly chronic diseases. The method includes: collecting real-time data of elderly patients and determining characteristic data of elderly patients after processing; training and optimizing a model for judging elderly chronic diseases, analyzing and judging the characteristic data of elderly patients, predicting the risk of elderly chronic diseases, determining the results of judgment of elderly chronic diseases, and performing early warning and health management on elderly patients. The present invention solves the problem that the existing system cannot judge elderly chronic diseases and timely manage health, resulting in poor diagnosis and management effects of elderly chronic diseases. The present invention constructs a model for judging elderly chronic diseases that predicts the risk of elderly chronic diseases and judges elderly chronic diseases, analyzes and judges the characteristic data of elderly patients, predicts the risk of elderly chronic diseases, and determines the results of judgment of elderly chronic diseases. It can effectively judge elderly chronic diseases and timely manage health and treat them, thereby improving the diagnosis and management effects of elderly chronic diseases.

The invention relates to the technical field of medical informatization management, in particular to a medical unit patient full-process management system based on a block chain, which comprises a data aggregation module, a grading decision module and a feedback synchronization module. According to the method, original physiological parameters of home wearable equipment are periodically retrieved, a clinical rehabilitation scale stored in diagnosis is matched to align a rehabilitation time sequence of a patient, and stage dynamic physiological data blocks are output; injecting the data into a block chain multi-stage physiological sign analysis model, quantitatively evaluating a clinical rehabilitation deviation value of a patient, and dynamically triggering a limited retrieval permission of a health file according to the clinical rehabilitation deviation value; executing clinical early warning according to the rehabilitation deviation value, receiving follow-up intervention suggestions, packaging the suggestions into encrypted receipts, synchronizing the receipts to the originating mechanism, and updating the post-diagnosis management state. According to the invention, closed-loop management of medical data from acquisition, early warning, authorization to feedback is realized, the technical problems of controlled opening of private data and dynamic verification of rehabilitation reference in a distributed environment are solved, and the real-time performance and accuracy of medical intervention behaviors are ensured.

The invention discloses an intelligent medical system based on big data, which comprises a data collection module, a diagnostic analysis module and an evaluation management module, and is characterized in that the data collection module is used for collecting facial features of doctors, disease information uploaded by users and medical record data of the users; the diagnosis management module is used for analyzing the fatigue degree of a doctor, adjusting and analyzing whether medicines used by a user interact or not, the evaluation management module is used for reminding the user to take the medicines on time, the user evaluates the doctor, and the data collection module, the diagnosis management module and the evaluation management module are in communication connection with one another. The data collection module comprises a visual module, an information input module and a medical record collection module, the visual module is used for collecting facial features of a doctor, the information input module is used for inputting focus information into the system by a user, and the system has the advantages that the safety of medicine use is improved, and the fatigue degree of the diagnosis doctor is reduced.

The application discloses a kind of diagnostic data subscription method, device and storage medium and electronic device, which comprises: based on diagnostic configuration table, first diagnostic configuration information is issued to kernel diagnosis management center, and second diagnostic configuration information is issued to application diagnosis management center;First unit diagnostic data reported by kernel diagnosis management center based on first diagnostic configuration information is obtained, and second unit diagnostic data reported by application diagnosis management center based on second diagnostic configuration information is obtained;In the case where the target unit diagnostic data of electronic control unit is obtained based on first unit diagnostic data and second unit diagnostic data, according to diagnostic configuration table, target unit diagnostic data is forwarded to at least one other electronic control unit.The application solves the technical problem that the acquisition efficiency of diagnostic data in the prior art is low.

Techniques for calculating or managing pupillary curve data identified based on data associated with computing devices are discussed herein. For example, a machine learning (ML) model can be utilized to analyze sensor data collected by a camera associated with a computing device. The ML model can perform a comparison between a pupillary (e.g., a pupil response curve and previous pupillary curves (e.g., previous pupil response curves) utilizing classification information associated with the previous pupil response curves. The comparison can be utilized to identify a physiological condition associated with the pupil response curve. Information identifying the physiological condition can be presented by a display or transmitted to the computing device.

The invention relates to the technical field of vehicle data processing, and discloses an automobile diagnosis data processing method and device, equipment and a storage medium. The method comprises the following steps: collecting a diagnosis communication message of a target vehicle and generating an original data file; locally converting the original data file into an ODX file in a standard format in the equipment; carrying out field correction on the ODX file through an integrated human-computer interaction interface and generating a correction record; and uploading the original data file, the corrected ODX file and the correction record to a cloud diagnosis management platform. According to the method, whole-process field integrated processing of diagnosis data from collection to generation is realized, the problem of low efficiency caused by link splitting in a traditional process is overcome, rework caused by a data quality problem is avoided through a real-time verification mechanism, continuous optimization of diagnosis knowledge is realized by means of a cloud-edge collaborative architecture, and the diagnosis efficiency is improved. And the efficiency and the quality of diagnosis data development are obviously improved.

To provide a frailty diagnosis support device capable of supporting frailty diagnosis.SOLUTION: A frailty diagnosis support device 100 comprises: a data extraction unit 11 that extracts data from a National Health Insurance general system 210, health checkup management system 220, Long-Term Care Insurance system 230, and a National Health Insurance database (KDB 240); a data processing unit 12 that compares the extracted data with personal information across insurance systems to process it into individual medical information; a dental receipt data processing unit 13 that obtains dental receipt information from the National Health Insurance General system 210 and calculates individual remaining tooth count information; an analysis data construction unit 14 that adds the individual remaining tooth count information to the individual medical information to process it into information usable for frailty diagnosis; and a data display processing unit 15 that processes the individual data received from the analysis data construction unit 14 in a time-series manner.SELECTED DRAWING: Figure 1

The application provides a train brake tester test monitoring diagnosis management method and system, first, a train brake tester test whole-process correlation information set is acquired, covering the running state, test working condition and historical test fault correlation information of each component test stage of the brake; then, a brake test monitoring diagnosis correlation chain is established based on the set, taking the test stage as a node, the component state transmission relationship between stages as an edge and determining the correlation attribute of the edge; then, the test state information collected in real time is analyzed for abnormal transmission between stages through the correlation chain, and an abnormal diagnosis result containing the abnormal occurrence stage, correlation components and transmission path is generated; finally, based on the abnormal diagnosis result and the historical fault correlation information, a test monitoring management strategy containing abnormal treatment measures, test process adjustment suggestions and component maintenance priority is generated. The application improves the accuracy and reliability of the train brake tester test.

The invention provides a backup power supply charging and diagnosis management system and method, and relates to the field of wind driven generator control systems.The backup power supply charging and diagnosis management system comprises a rectifier, a backup power supply, a circuit control module and a controller, and the rectifier is used for rectifying alternating current into direct current and transmitting the direct current to the circuit control module; the circuit control module is used for acquiring the dispatching instruction and changing the control switch combination based on the control instruction so as to limit the charging current and the discharging current of the back-up power supply; the backup power supply is used for storing the current or outputting the stored current; and the controller is used for acquiring the control instruction, the state data of the back-up power supply and the state data of the circuit control module in real time, and performing abnormity diagnosis and charging control on the back-up power supply by generating a scheduling instruction of the circuit control module in combination with a preset charging trigger condition. The reliability of the backup power supply system can be improved, and the operation and maintenance difficulty is reduced.

The invention discloses a full-life-cycle intelligent diagnosis management system for fan equipment. The system comprises a data acquisition and management module, an intelligent diagnosis and analysis module, a business process and operation and maintenance management module, an intelligent report management module, a user interaction module and a problem library and expert library module. By optimizing the existing working process, data can be efficiently collected and managed, the operation and maintenance efficiency can be remarkably improved through intelligent diagnosis and an automatic business process, the downtime of equipment is shortened, and the maintenance cost is reduced. Meanwhile, the system realizes the whole process optimization from data acquisition to fault processing, report generation and user interaction by constructing a targeted management system, and provides a more efficient, reliable and friendly solution for the whole life cycle management of the fan equipment.

The present invention relates to a dental clinic cloud system. The dental clinic cloud system includes: a plurality of dental systems arranged in individual dental clinics, collecting a dental image acquired by capturing teeth of a patient, and generating an examination report in which the dental image and examination information are recorded; and a dental cloud server which is connected to each of the dental systems through a communication network and in which the examination report generated by each of the dental systems is uploaded and stored, wherein the dental cloud server allows access to the examination report when an authorized user accesses the dental cloud server. Accordingly, the patient may be provided with his / her teeth diagnosis management information.

The invention provides an intelligent diagnosis management method and device for a regional boiler room cluster pipe network, and is applied to the technical field of data processing, and the method comprises the steps: monitoring the operation state of a heat supply pipe network of a regional boiler room cluster through a sensor, and obtaining the operation state data; preprocessing the running state data to obtain preprocessed running state data; based on the preprocessed operation state data, the probability corresponding to the fault point of the heat supply pipe network of the regional boiler room cluster is obtained; and generating a fault processing strategy according to the probability corresponding to the fault point, and executing intelligent diagnosis management of the regional boiler room cluster pipe network based on the fault processing strategy, so that the fault can be accurately positioned, the accuracy of the fault processing strategy can be improved, and the diagnosis management efficiency and effect can be improved.

A medical data and diagnostics management system for processing classified electrogram (EGM) datasets includes a server system that receives transmissions of classified EGM datasets, each corresponding to an arrhythmic episode detected by an implantable medical device (IMD), and applies a machine-learning model to each classified EGM dataset, thereby determining confidence indicator(s) relating to the IMD classification for each arrhythmic episode. Based upon the confidence indicator(s), the server system generates a set of machine-adjudicated EGM datasets, assigns a ranking score to each machine-adjudicated EGM dataset, and selects for display (for clinical analysis) a subset of the machine-adjudicated EGM datasets based upon their ranking scores. The machine-adjudicated EGM datasets are also stored in a database and further processed to generate diagnostic information and / or diagnostic alerts relating to the arrhythmic episodes detected by the IMD over time. The server system may also facilitate reprogramming of the IMD to improve its performance.