34 results about "Diagnosis management" patented technology

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 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 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.

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.

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.

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.

The application discloses a train vehicle fault diagnosis management method and device, computer equipment and a storage medium. Wherein, the method comprises: receiving the fault data and process data of each system of the train, and displaying the state page in a graphical manner; judging the permission information of the current account; according to the preset condition met by the permission information, obtaining the display device information and the fault information; and rendering and displaying the vehicle fault information on the state page. Through the above manner, the application can collect each item of data of each device system of the vehicle during the train running process and input into the database table. For different systems and requirements, the massive original data is integrated and classified to form obvious indexes, and is displayed in a graphical manner. The user logs in the system through the account management, checks the current state of the train vehicle, and quickly finds and locates the fault.

Provided is a smart system for vehicle fault prediction, including: an in-vehicle diagnosis system, a client to-be-diagnosed device set, a diagnosis management platform, a database, and a smart analysis engine. A diagnostic task script of the management platform is parsed, and a host / client interaction module, a safety module, and a diagnostic module perform the diagnostic task. The in-vehicle diagnostic engine transmits the diagnostic result to the diagnostic management platform, which sends the diagnostic result to the database, which then stores the diagnostic result and transmits to the smart analysis engine, which analyzes the diagnostic results and infers the potential risk of vehicle failure. Furthermore, the present invention also provides a smart method for vehicle fault prediction.

The invention belongs to the technical field of vehicle diagnosis, and provides an automobile diagnosis management method and application based on scene perception and authority self-adaption. Synchronously reporting a scene identifier and diagnosis equipment identity information, and sending an authentication and permission request; executing a two-stage verification strategy, and generating a temporary dynamic permission token; performing real-time verification on all diagnosis instructions based on the dynamic permission token, and releasing compliance diagnosis operation instructions based on a minimum permission principle; performing risk analysis based on comparison of the real-time operation behavior and the baseline behavior model, triggering an instant adjustment strategy of the authority, and making a hierarchical adaptive diagnosis operation response; and when the dynamic permission token fails, the diagnosis is terminated, and the risk analysis method and the immediate adjustment strategy are optimized based on all diagnosis records. According to the invention, an appropriate diagnosis authority can be granted in a correct scene, so that the flexibility and efficiency of diagnosis operation are improved on the premise of ensuring the safety of the vehicle.

The invention provides an online intervention system for a chronic insomnia accompanying anxiety state based on a traditional Chinese and western medicine combined theory system, relates to the technical field of chronic insomnia intervention, and is a supplement for an offline insomnia outpatient service to facilitate post-diagnosis guidance and management of a patient with the chronic insomnia accompanying anxiety state by a doctor. The system is presented in an APP mode and comprises four modules which are a neuropsychological scale evaluation module, a tongue picture evaluation module, a five-star clock-in module and a doctor-patient interaction module. The neuropsychological scale evaluation module is used for evaluating the cognitive function, the emotional state and the sleep state of a patient, and the tongue picture evaluation module is used for judging the tongue picture characteristics of the patient by intelligently extracting and recognizing tongue quality and tongue coating. The'five-star 'clock-in module requires the patient to upload daily treatment contents, including daily transcribed scripture pictures, played music audio, videos for exercising tendons and meridians and oral traditional Chinese medicine or western medicine pictures. When a patient is diagnosed offline for the first time, a doctor can propagandize and teach the patient in detail and teach the patient about the content of'five-star 'card punching. The doctor-patient interaction module provides a doctor-patient online real-time interaction platform, and a doctor can guide the five-star clock-in content uploaded by the patient in combination with the neuropsychological assessment and tongue picture assessment results of the patient, so that post-diagnosis management of the patient is realized, and help is provided for the patient to reduce and stop sleeping drugs and recover natural sleep.

A medical data and diagnostics management system for processing classified EGM datasets includes a server system that receives transmissions of classified EGM datasets, each corresponding to an arrhythmic episode detected by an IMD, and applies a machine-learning model to each classified EGM dataset stored in a database, thereby determining for each of the classified EGM datasets to which the model is applied a respective indicator of whether the IMD classification is a false positive or a true positive. The system is further configured to remove from the database one or more of the classified EGM data sets for which the respective IMD classification is identified using the machine-learning model as being a false positive, thereby creating a plurality of machine-adjudicated patient database records stored in the database. The system may also provide for display a selected one of the machine-adjudicated EGM datasets and / or facilitate reprograming the IMD.

The application provides a federated learning-based end-side AI agent cooperative fault diagnosis method and device, relates to the technical field of federated learning, and the method comprises the following steps: generating a task requirement description according to a diagnosis task cluster received by a server; each end side extracts a knowledge skeleton from a local agent and performs topology reconstruction through knowledge distillation; matching the local diagnosis target by matching with the task requirement description to determine a cognitive data packet; the server receives the cognitive data packet, activates an analysis engine, and performs double-path operation management; driving an extended group to expand a global knowledge base; and matching and fusing the local diagnosis target through the local cognitive topology and the global knowledge base, and then updating learning and task diagnosis management are performed on the agent. Through the application, the problem that end-side diagnosis cognition is difficult to be structured and aligned in the prior art can be solved, federated cooperative fusion driven by tasks is realized, and the technical effect of improving the global diagnosis reliability is achieved.

The invention discloses a vehicle-mounted safety diagnosis data management method and device and electronic equipment. The method comprises the steps that a diagnosis data management request and diagnosis data sent by a diagnosis application module of a vehicle end are received; the diagnostic data management request is generated by the diagnostic application module based on the generation of the diagnostic data; sending a data encryption request and diagnosis data to a diagnosis encryption module; the diagnosis encryption module is used for encrypting diagnosis data based on the data encryption request to obtain encrypted diagnosis data; receiving encrypted diagnosis data returned by the diagnosis encryption module; sending a storage request and encrypted diagnosis data to a storage module; the storage module is used for storing the encrypted diagnosis data; receiving a storage response returned by the storage module; and sending a diagnosis management response to the diagnosis application module. In the embodiment of the application, the service-oriented diagnosis encryption module is used to realize decoupling with the diagnosis management module, and maintenance and expansion are easier.

This application discloses a diagnostic management method, device, system-on-a-chip, and vehicle based on a System-on-Chip (SOC). The method is applied to an SOC and includes: when a first diagnostic instrument switches from an MCU to the SOC, or when a second diagnostic instrument connects to the SOC while the first diagnostic instrument is connected to the MCU, obtaining an automatically generated diagnostic questionnaire from an automated diagnostic configuration generation tool; receiving a diagnostic instruction sent by a target diagnostic instrument according to the diagnostic questionnaire; when the diagnostic instruction is used to instruct the diagnosis of a first intelligent driving module managed by the SOC, sending the diagnostic instruction to the first intelligent driving module, receiving the diagnostic response reported by the first intelligent driving module, and transmitting the diagnostic response reported by the first intelligent driving module to the target diagnostic instrument; when the diagnostic instruction is used to instruct the diagnosis of a second intelligent driving module managed by the MCU, sending the diagnostic instruction to the MCU, receiving the diagnostic response reported by the second intelligent driving module through the MCU, and transmitting the diagnostic response reported by the second intelligent driving module to the target diagnostic instrument.

The application provides a vehicle fault intelligent prediction system and a method thereof, and the vehicle fault intelligent prediction system comprises an in-vehicle diagnosis system, a client to-be-diagnosed equipment group, a diagnosis management platform, a database and an intelligent analysis engine, wherein the in-vehicle diagnosis engine of the in-vehicle diagnosis system receives a diagnosis task script from the diagnosis management platform and analyzes the diagnosis task script, and a vehicle host interaction module, a safety module and a diagnosis module execute the diagnosis task, the in-vehicle diagnosis engine transmits a diagnosis result to the diagnosis management platform, the diagnosis management platform transmits the diagnosis result to the database, the database stores the diagnosis result and transmits the diagnosis result to the intelligent analysis engine, the intelligent analysis engine analyzes the diagnosis result and infers a potential fault risk of the vehicle, so that a user and / or a manager can obtain the potential fault risk of the vehicle in a short time and prevent the fault in advance. Furthermore, the application also provides a vehicle fault intelligent prediction method.

The present application relates to the technical field of oil and gas pipeline valve internal leakage online diagnosis, and discloses an oil and gas pipeline valve internal leakage online diagnosis method based on voiceprint analysis. The method acquires valve operation voiceprint data through a distributed interface to construct a feature database, designs a diagnosis framework according to the database, and determines a multi-source voiceprint collection scheme. A voiceprint collection terminal acquires multi-dimensional voiceprint signals according to the scheme, inputs a model after feature extraction to complete internal leakage level determination and abnormal pattern recognition, and outputs diagnosis results. The diagnosis results are transmitted to a management optimization module to realize diagnosis strategy adjustment through timestamp calibration, mapping table construction and the like. A three-level evaluation sub-framework is also constructed to assist diagnosis. The method realizes accurate diagnosis and strategy optimization of oil and gas pipeline valve internal leakage, and improves diagnosis management level.

The application provides a vibration intelligent diagnosis management method based on data analysis processing and belongs to the technical field of data analysis, and specifically comprises the following steps: determining the vibration diagnosis management method of a vibration diagnosis management target based on the vibration diagnosis management target data and in combination with the correlation degree between the pressure fluctuation operation condition of the vibration diagnosis management target and other vibration diagnosis management targets; determining the similarity degree between simulation data and self-excited vibration data under different operation conditions based on the vibration diagnosis management method; determining the matching diagnosis condition by using the similarity degree; determining the matching degree between the operation condition of the superheated pipeline of the boiler and the matching diagnosis condition; and determining the management method of the vibration diagnosis processing target of the superheated pipeline of the boiler in combination with the pressure fluctuation data under different operation conditions and the matching diagnosis condition data, so that the reliability of fault diagnosis processing is improved.

The invention discloses an enterprise data import diagnosis management system based on big data, and relates to the field of big data. The system comprises a to-be-diagnosed enterprise detail information acquisition and examination module, a diagnosis and examination qualified identification code generation and verification module, a multi-class diagnosis material uploading and pre-screening module, a single-scale index data deviation value class module, a multi-class diagnosis material entry rectification and feedback module and an enterprise data diagnosis platform. According to the method, detailed information of an enterprise is collected for basic review, a corresponding anti-counterfeiting review identification code is constructed in combination with enterprise identity authentication data, and meanwhile, preliminary logic format verification and threshold standard pre-screening are carried out on diagnosis material data uploaded by the enterprise according to a preset diagnosis material uploading rule; the diagnosis material uploaded by the enterprise is subjected to qualified screening in advance, the to-be-diagnosed material information uploaded by the enterprise is accurately compared with the single-item champion benchmark data extracted from the big data platform, and the enterprise development short board is accurately positioned.

An intelligent transformer integrated management system includes at least one data collection terminal, an analysis and diagnosis management platform, and a maintainer terminal. The at least one detection member of a data detection module of the at least one data collection terminal is configured to obtain operation data. The analysis and diagnosis management platform is communicatively connected to the data detection module of the at least one data collection terminal. The analysis and diagnosis management platform includes a data integration diagnostic device, a maintenance module, a report generation module, a monitoring module, and a record database. The data integration diagnostic device is configured to receive, integrate, and diagnose the operation data of a transformer from the data detection module, and generate a diagnostic result related to health and lifespan of the transformer. The maintainer terminal is communicatively connected to the analysis and diagnosis management platform.

An energy-saving diagnosis management method and apparatus for a smart park, and an electronic device. The method comprises: when an energy-saving diagnosis module detects that energy consumption per unit time in any space is greater than target energy consumption in the corresponding space, a communication module sending an acquisition instruction for control data of energy consumption devices in the current space to a control module for the energy consumption devices, wherein the corresponding control data after each startup of the energy consumption devices is generated by a user performing a setting operation by means of an interaction unit of the control module; on the basis of current outdoor environment data and the acquired control data of the energy consumption devices in the current space, determining whether there is a target energy consumption device of which the control data exceeds a preset target control data range among the currently started energy consumption devices, wherein the target control data range is an energy-saving control recommendation data range preset correspondingly on the basis of different outdoor environment data; and when there is a target energy consumption device, sending a new control instruction to the control module, so that the control data of the target energy consumption device is within the target control data range.

To enable the recommendation of a different skin diagnosis from the one used to obtain the skin evaluation information if there are any problems with the skin evaluation information included in the user's skin diagnosis results, or with the changes in that information over time. [Solution] The management server 100 of the skin diagnosis management system includes an acquisition unit 1121 that acquires a first skin diagnosis result, which is the result of a first skin diagnosis including one or more skin evaluation pieces of information, based on an image captured of the user's skin; a storage unit 120 that stores the first skin diagnosis result for each user, associated with the time information of when the skin diagnosis was performed; and a notification unit 1122 that, if the first evaluation piece among the one or more evaluation pieces of information included in the stored first skin diagnosis result satisfies predetermined conditions for at least one of the evaluation piece and its temporal changes, notifies the user of second skin diagnosis information that can diagnose the first skin index indicated by the first evaluation piece.

The invention discloses an accompanying diagnosis management system based on an artificial intelligence technology, and belongs to the technical field of medical accompanying diagnosis. The system comprises a user management and health archive module, an intelligent reservation module, a health suggestion and diagnosis and treatment assistance module, an accompanying diagnosis and emotion support module, a data analysis and feedback module and a safety and privacy protection module. The user management and health archive module realizes archive management by using biological recognition, an NLP technology and a block chain; the intelligent appointment module recommends departments and optimizes appointment reminding based on an ICD-11 standard; the health suggestion and diagnosis and treatment auxiliary module is combined with medical knowledge retrieval and interpretability AI to provide diagnosis and treatment suggestions; the accompanying diagnosis and emotion support module evaluates emotion and intervenes through multi-modal data acquisition; the data analysis and feedback module performs deep analysis and provides decision support; the security and privacy protection module uses various encryption and detection technologies to guarantee data security. The system effectively solves the problem of traditional doctor-seeing accompanying diagnosis, and achieves the functions of accurate registration, personalized diagnosis and treatment, emotion care, data safety management and the like.

A module level power electronics (MLPE) according to an aspect may comprise a processor that: executes a self-diagnosis mode on the basis of a self-diagnosis request signal received during operation or stop of an inverter connected to the module level power electronics; detects, ion the self-diagnosis mode, whether there is an installation error between a photovoltaic module and the module level power electronics or a component error within the module level power electronics; and transmits, to a diagnosis management system, diagnosis result information regarding whether or not the installation error and the component error are detected.