85results about How to "Realize health management" patented technology

The invention discloses a health secretary comprising a central control processing module, an electrocardiogram measuring module, a body temperature measuring module, a motion detection module, a sleep monitoring module, a data storage module, a GSM / GPRS module, a GPS module, a Bluetooth module, an FM module, a lithium ion battery, a power management module, a liquid crystal display module, an audio output module, a microphone, a USB interface, function keys and a housing. The central control processing module is connected with the other modules, and biological parameters, such as body temperature, electrocardiogram and sleep quality, are conveniently detected; intervention suggestions are provided according to user information; intervention execution is fed back and acoustically prompted by means of re-detection, evaluation, prompting and the like, and health monitoring and promoting functions are achieved.

The invention discloses a cloud storage based remote equipment health management method and a system using the same, and belongs to the technical field of remote management. The cloud storage based remote equipment health management method comprises the steps of acquiring equipment information of at least a terminal device in an LAN (Local Area Netwrk) in real time; uploading the equipment information to a cloud-end server in a public network; providing a data receiving service and a data storing service by the cloud-end server; storing the equipment information intensively, wherein the coding for the equipment information has uniqueness; providing a calculation engine service for received data; and showing a result to a user terminal. Through the cloud storage based remote equipment health management method and the system using the same, remote storage of multipoint big data can be realized, diagnosis early warning can be carried out for faults which have happened or will happen, statistical summary can be carried out for health data of the terminal equipment, the purpose of remote real-time monitoring can be realized, a user can know and maintain the health condition of the terminal equipment in time and conveniently, and safe operation of the terminal equipment can be guaranteed.

The invention discloses an intelligent terminal based health management system which comprises a portable multi-parameter measuring device, an intelligent terminal health management application module and a remote service center. The portable multi-parameter measuring device monitors various physiological parameter information and transmits measuring results to an intelligent terminal in the form of sound carrier signals; the intelligent terminal health management application module is arranged on the intelligent terminal and used for storing and recording the measuring results sent by the portable multi-parameter measuring device according to the received sound carrier signals and sending the measuring results to the remote service center according to the requirements of users; and the remote service center is used for receiving the measuring results containing the various physiological parameter information of the users and giving emergency responses when physiological parameters exceed normal ranges, and the measuring results are sent by the intelligent terminal health management application module. The system can real-timely monitor the physiological parameters of the users and is convenient to carry and capable of achieving movable health management and health care assistance.

The invention discloses an electric locomotive fault prediction and health management method. Sensors arranged in and out of an electric locomotive are used for collecting and monitoring state characteristic parameter data of different components of the electric locomotive and external environmental parameter data in real time; the parameter data is arranged, compressed and coded and sent to a ground processing center through a wireless network; the ground processing center receives the data transmitted from the electric locomotive and establishes state evaluation models for the different components of the electric locomotive through fuzzy logic to analyze corresponding data obtained after extraction is carried out according to time sequence classification, and by means of estimation, the performance, states and health situations of the different components of the electric locomotive are obtained; a fault prediction model is established based on an artificial neural network through a fault foreboding and failure mechanism diagnosing and predicting method, fault prediction is carried out, the residual life is calculated, and data is recorded to form a historical database. By means of the electric locomotive fault prediction and health management method, fault prediction can be automatically carried out, the residual life of the components can be calculated, and health management of the electric locomotive can be achieved.

The invention provides a fault pre-diagnosis and health management method of a mechanical and electrical device and a system. The method comprises steps of data acquisition: acquiring data information of the mechanical and electrical device; self-diagnosis: carrying out feature extraction and model establishment on historical data information of a certain mechanical and electrical device under different operation modes and health states, and using the currently established model to compare the acquired data information with the historical data information under the current state and automatically identifying the current health state of the mechanical and electrical device; health state prediction: predicting the change of future health states of the mechanical and electrical device according to the acquired current health state after the self-diagnosis and the historical health state of the mechanical and electrical device; and clustering analysis: carrying out clustering and analysis comparison on the data information of multiple mechanical and electrical devices in a mechanical and electrical device cluster according to the current health state of the single mechanical and electrical device so as to obtain health state grades and risk distribution of multiple mechanical and electrical devices.

The invention discloses an unmanned aerial vehicle-mounted health management monitoring system. The unmanned aerial vehicle-mounted health management monitoring system comprises a vibration sensor, a noise sensor, a pressure sensor, a rotation speed sensor, an embedded control host module (1), a synchronous data acquisition module (2), a rotation speed measurement acquisition module (3) and a solid state disk (5), wherein the synchronous data acquisition module (2) is used for acquiring a vibration signal, a noise signal and a pressure signal sent by the vibration sensor, the noise sensor and the pressure sensor; the rotation speed measurement acquisition module (3) is used for acquiring a rotation speed signal sent by the rotation speed sensor; the embedded control host module (1) is used for receiving data sent by the synchronous data acquisition module (2) and the rotation speed measurement acquisition module (3) to achieve analysis and diagnosis of the health state of an unmanned aerial vehicle and performance assessment of the unmanned aerial vehicle and to form alarm information and key health information, and sending the alarm information and the key health information to a flight control system of the unmanned aerial vehicle; the solid state disk (5) is used for storing acquired and processed data information.

A charge state estimation method of a lithium-ion capacitor for an electric automobile comprises the steps that S1, a dynamic second-order model of a simplified lithium-ion capacitor is established; S2, a state equation and an observation and equation are respectively established based on the dynamic second-order model; S3, lithium-ion capacitor parameters are identified; S4, artificial immune particle filters are brought to the established second-order model of the simplified lithium-ion capacitor to perform SOC estimation, and a charge state of the lithium-ion capacitor is obtained.

The invention relates to a big-data health management platform which comprises a data acquisition module, a big data server, a mobile terminal and a wireless communication module. The data acquisition module acquires physical sign data of users, the physical sign data are sent to the big data server, the big data server analyzes the physical sign data received presently according to the data saved in the data storage module to generate health management report finally, and users check the health management report through a mobile terminal, and can communicate with doctors through an online consultation module at the same time. The big-data health management platform can detect environmental parameters of current locations of the users and then provides environmental management suggestions to the users. Besides, by the big-data health management platform, the users can do individual self-monitoring through the mobile internet, can contact with the doctors online anytime anywhere, and can learn the symptom trends clearly through the health management report and obtain corresponding suggestions on treatment, diet and exercises.

The invention provides a combined dynamic energy storage real-time safety monitoring system. The combined dynamic energy storage real-time safety monitoring system comprises a lithium battery pack, asuper-capacitor bank, a data collection module, a combined dynamic energy storage system main control module, a safety management module, a communication module and an upper computer. The data collection module is respectively and electrically connected with the lithium battery pack and the super-capacitor bank and is used for collecting various running state information of lithium batteries and super-capacitor banks; the data collection module comprise a total voltage collection module, a charging and discharging current collection module, a monomer voltage collection module and a temperaturecollection module. The combined dynamic energy storage real-time safety monitoring system improves sampling precision as a sensor is utilized for collecting state parameters of the energy storage system; and the energy storage system is protected by adopting corresponding protection measures.

The invention discloses a machinery device fault combination prediction system and a method, which are stable and reliable, can perform early warning and has high prediction accuracy and realizes the scientific planning and healthy management on the device. The method comprises steps of conditioning and amplifying collected signal through installing a sensor at a suitable test point in the machinery device to collect parameter information of frequency, amplitude and temperature of a spindle and a bearing bush, converting the collected signal to suitable digital signals through A / D conversion and then transmitting the signal to an upper computer, establishing a man-machine interaction interface through an Labviw software, performing collection, processing and storage on data, and integrating with polynomial regression prediction, small wave gray prediction, small wave neural network prediction and the prediction method of the particle cluster optimization support vector machine to perform combination prediction. The invention can not only analyze and predict the machine fault in order to provide feasible solution, but also provide theoretical meaning and application significance to improvement of the monitoring analysis function and the fault diagnosis accuracy of the rotation machinery device and to the enrichment and development of the fault prediction theory.

The invention provides a hierarchical software health management method and system for microsatellites. The hierarchical software health management system performs hierarchical health management on satellites according to different subsystems, and comprises a sensor layer, a state monitoring layer, a health evaluation layer, a prediction layer, a decision processing layer and the like. Through software sensors deployed at operation key nodes of all sub-components of satellite housekeeping, the hierarchical software health management method collects running state data of each sub-component andanalyzes the running state data to obtain fault information, hierarchically evaluates the health condition of the current satellite system in combination with satellite historical telemetry data, software system knowledge and the fault information, pre-judges the health condition of the system at the future moment, and finally makes a health hazard strategy.

The invention provides an autonomous sensing system using change of a parameter monitoring load of a motor system and a system abnormity. The autonomous sensing system comprises a motor system parameter detecting and sensing module, a motor system information processing and analyzing module, an electrical transmission motion system position, speed and torque calculation-analysis module, an electrical transmission motion system abnormity feature library module and a motion system abnormity display and alarm unit. Parameters of the motor system include the DC voltage and current of a motor driver, temperatures of an inverter and a radiator, and the three-phase voltage, the three-phase current, output torque and output rotating speed of the motor. The parameters of the motor system are collected and analyzed to detect impact condition of the load, mechanical faults in a system transmission link and system operation abnormity and system stability lead by transmission skid and system load change, so that the system can monitor the health and operation states of itself in real time, and operational safety and reliability of the system are ensured.

A five-sense-organ and surgical medical data acquisition, analysis and intelligent diagnosis integrated robot is a physical examination medical data acquisition and analysis robot platform system built by combining an artificial intelligence robot system with various data acquisition devices and other nodes. The functions of robot voice interaction, arm action interaction, data acquisition, data analysis and the like are achieved in combination with a robot node communication principle by applying a data acquisition technology and data analysis; data such as height, weight, electrocardiogram,blood pressure, oral cavity, ears and eyes are monitored through a camera and various sensors carried on a robot platform; and medical indexes are intelligently detected, wherein the medical indexes include the problems of common oral diseases, ear abnormality, eye vision, ametropia classification and the like. The medical data are classified by using a neural network algorithm, and the problems of eye, ear and oral abnormality and the like are analyzed by using the data analysis technology. The body temperature is measured by using infrared sensing. The medical data are efficiently collectedunder robot voice guidance. Abnormal symptoms are effectively checked and regularly detected, and intelligent physical examination result feedback and regular examination prompting are achieved according to physical standards, analyzed data, intelligent inquiry, remote health inquiry and the like.

A B-ultrasonic data acquisition, analysis and diagnosis integrated robot platform is a medical big data platform system built by using an artificial intelligence robot system and a robot acquisition node such as a B acquisition device. By utilizing a robot arm and B-ultrasonic equipment carrying acquisition technology and data analysis, and in combination with a robot node communication principle,the functions of performing robot voice interaction, remotely controlling a robot arm to act for acquiring B-ultrasonic data and performing autonomous acquisition, analyzing the B-ultrasonic data andintelligently diagnosing diseases are realized; through a camera and the B-ultrasonic acquisition device carried on the robot platform, in-vitro organ position recognition is combined with internal B-ultrasonic image based internal organ recognition, so that the internal organs are positioned in a double-precision manner, B-ultrasonic images are acquired in a high-precision manner, the B-ultrasonic organ images are classified, the B-ultrasonic image abnormality is intelligently recognized, and the common diseases in the organs are intelligently diagnosed; the B-ultrasonic images are classified by using a deep neural network method and a machine learning method, and the internal organ diseases are analyzed and intelligently diagnosed; under the guidance of robot voice, medical data are acquired remotely and autonomously; and the common diseases in the organs are remotely inquired, acquired, analyzed and intelligently diagnosed, so that abnormal symptoms are effectively checked.

The invention provides an airborne flight parameter data health diagnosis method based on deep learning. The method is based on a deep learning algorithm, data collected by aviation airborne flight parameter equipment can be analyzed in real time, the health condition of an airplane in the flight process is obtained, the fault risk of the airplane is predicted in advance, and a decision for avoiding faults is given to be adopted by pilots. According to the method, specific acquired data is required to be trained, and particularly, analysis and learning of relevance of various characteristic parameters before an airplane has a fault are the key to effectively predict and avoid the fault. And the problems of data real-time analysis, fault diagnosis and risk decision in the field of aviationairborne flight parameter equipment are effectively solved.

The invention discloses a health management method and device. The health management method can comprise the steps of: a terminal obtaining physiological and biochemical data of a user acquired by data acquisition hardware in an image recognition or voice recognition mode, wherein the data acquisition hardware comprises a physiological parameter monitoring device and a biochemical parameter detection instrument; uploading the physiological and biochemical data to a cloud server for storing and analyzing; and receiving the analysis result returned by the cloud server. By adopting the technical solution, the physiological and biochemical data acquired by the data acquisition hardware can be quickly and accurately obtained in the image recognition or voice recognition mode, so that a user does not need to consider whether the data acquisition hardware has a data transmission function, the appropriate data acquisition hardware can be flexibly selected according to needs, health management is realized, and the user experience is improved.

The invention discloses a coupled river water quality and sediment health monitoring system and method. The coupled river water quality and sediment health monitoring system comprises an unmanned ship, a data transmission center and a terminal user application platform, wherein river sediment deposition on-line monitoring equipment, a river water quality on-line monitoring module and river water quality sampling equipment are carried on the unmanned ship, topographic data and part of water quality health index data at a measurement point are collected in real time, and the measurement data istransmitted to the terminal user application platform through the data transmission center; the data transmission center comprises a satellite system and a satellite system network management center,and is used for navigating the unmanned ship according to the physical coordinates of each measurement point provided by the terminal user application platform and transmitting the measurement data tothe terminal user application platform; and the terminal user application platform is used for determining the number of measurement points and the physical coordinates of each measurement point according to the known width and length of a river, and providing the measurement data collected in time by the unmanned ship and the measurement data obtained by analysis to the unmanned ship through thedata transmission module to perform early warning prompt.

The invention relates to the technical field of biological rhythm health management and discloses a human biological rhythm health management method. The method comprises the steps of acquiring basicfeature information of a user and biological rhythm data within a first preset time, and calculating biological rhythm state information of the user; calculating biological rhythm disorder degree information according to the biological rhythm data and the biological rhythm state information; outputting a standard biological rhythm health intervention scheme within a second preset time according tothe biological rhythm disorder degree information; obtaining target and willingness information of the user for executing the standard biological rhythm health intervention scheme; and outputting a personalized biological rhythm health intervention scheme within a third preset time according to the target and willingness information of the user for executing the standard biological rhythm healthintervention scheme. Therefore, calculation and judgment errors caused by different ideal biological rhythm modes of groups with different basic feature information are avoided, the biological rhythmcondition of the user can be accurately reflected, and the biological rhythm management effect is improved.

The invention relates to an intelligent prediction method and system for the residual life of a main speed reducer, equipment and a medium. The method comprises the steps of obtaining vibration data of multiple monitoring data channels of target equipment; performing local feature extraction on the vibration data of each monitoring data channel by using a preset multi-size convolutional neural network to obtain corresponding equipment failure feature information; extracting the equipment failure feature information of each monitoring data channel by using a preset deep feature extraction network to obtain corresponding deep time sequence feature information; inputting the deep time sequence characteristic information of the plurality of monitoring data channels into a preset residual life prediction initial model for training to obtain a residual life prediction target model; and based on a linear regression smoothing strategy, predicting the residual life of the target equipment at the current moment by using the residual life prediction target model. The method and system can effectively detect the fault characteristics of the automobile main speed reducer, and can accurately predict the residual life of the automobile main speed reducer.

The invention relates to the technical field of aero-engine overall performance detection, particularly to an aero-engine complete machine residual performance extraction method based on after-flightdata. The method comprises the following steps: S100, preprocessing an EGTM time sequence provided by a take-off report, wherein the preprocessing specifically comprises abnormal value processing, missing value processing and noise reduction processing of the EGTM time sequence; S200, establishing a recoverable performance model based on the preprocessed EGTM time sequence and the washing maintenance record; and S300, in combination with the recoverable performance model in the step S200, extracting residual performance from the preprocessed EGTM sequence. The aero-engine complete machine residual performance extraction method based on the after-flight data, provided by the invention, is beneficial to researching an aero-engine recession rule; the method has important significance for further mastering the performance degradation rule of the aero-engine, and realizes state monitoring and health management of an aero-engine, thereby improving the flight safety and reducing the use costof the aero-engine.

The invention provides an intelligent water cup capable of hinting water drinkability, and belongs to the field of intelligent water cups. The cup solves the problem that when people drink water with cup, the people cannot determine if the water is drinkable. The cup comprises a cup body, a power supply module, a processor, a first temperature sensor, a second temperature sensor, a wireless transmission module (6) and a hinting device. According to the intelligent water cup and method capable of hinting the water drinkability, the environment temperature and the liquid temperature are collected to achieve the accurate determination of the liquid drinkability. At the same time a pressure sensor is arranged to achieve that only after the cup is put up does the hinting start, so the work duration is prolonged in the condition of the battery power supply. The pressure data is uploaded to a cloud platform to help clients make reasonable plan of water drinking in a healthy manner.

The invention discloses a remote wireless control device of a side-pulling carriage moving mechanism of a carriage-type vehicle. The device comprises a leveling module, a carriage concentration control module and an electro-hydraulic control module; the leveling module utilizes level sensors on a vehicle body to detect the leveling state of the vehicle body to obtain leveling information and output the leveling information; the carriage concentration control module is used for collecting the state of each moving mechanism in a carriage to obtain state information and output the state information through a CAN bus; the electro-hydraulic control module is used for reading the state information and leveling information of each moving mechanism, judging whether or not the state information andthe leveling information are abnormal and outputting a judging result, the state information and the leveling information. The invention further provides a remote wireless control method of the side-pulling carriage moving mechanism of the carriage-type vehicle. The device and the method can achieve real-time detection of in-vehicle state and ensure the safety of operators.

The invention discloses a combat vehicle unit health management device which comprises a plurality of sensor modules, a plurality of chassis vehicle health management systems, a plurality of weapon health management systems, a plurality of task health management systems, a portable maintenance auxiliary device and a command node monitoring management module. Each combat vehicle in a combat vehicle unit is provided with a sensor module, a chassis vehicle health management system, a weapon health management system and a task health management system, wherein the chassis vehicle health management systems is connected with the sensor module and a combat vehicle first component group, the weapon health management system is connected with a combat vehicle second component group, and the task health management system is connected with the chassis vehicle health management system, the weapon health management system and the portable maintenance auxiliary device; and the command node monitoring management module is connected with the portable maintenance auxiliary device. According to the invention, a complete combat vehicle unit health management architecture is constructed for a whole combat vehicle unit, so that health management of a combat vehicle unit is realized.

An aero-engine health management is disclosed, comprising: acquiring engine-related data relating to the aero-engine to be managed; reading engine related data; extracting first valid data adapted toa predicted state of the engine and second valid data adapted to a predicted fault; performing feature analysis on the first valid data and the second valid data, respectively; the first valid data after the feature analysis is used as the theoretical input of the engine to complete the judgment of the engine to be predicted state, and the second valid data after the feature analysis is used as the theoretical input of the engine to be predicted fault to complete the judgment of the engine to be predicted fault. An aero-engine health management apparatus and system are also disclosed. The aeroengine health management method, apparatus, and system of the present invention can analyze engine related data, determine predetermined engine states and faults, and thereby realize engine health management.

The invention discloses a rail transit vehicle brake shoe remaining life evaluation method and device. The method comprises the following steps: calculating the total acting force applied to a brake shoe group by a bogie brake cylinder in a unit period, and calculating the friction power applied to the brake shoe group of the bogie in the unit period according to the total acting force, calculating the energy acting on the brake shoe group in a unit period, accumulating the energy in a historical period, calculating the total brake energy acting on the brake shoe group by a bogie brake cylinder, calculating the current wear loss evaluation value of each brake shoe according to the wear loss of the brake shoe in unit energy and the total brake energy, and calculating the residual life of the brake shoe; a device corresponding to the method comprises a processor and a rail transit vehicle brake shoe remaining life evaluation program, and the processor executes the rail transit vehicle brake shoe remaining life evaluation program to achieve the rail transit vehicle brake shoe remaining life evaluation method. Therefore, the residual life of the brake shoe of the rail transit vehicle is reasonably and efficiently evaluated.

The invention discloses a prawn product real-time information active pushing system based on big data analysis and mining, and the system comprises a transaction controller which is connected with a prawn farmer and a prawn purchase user. Wherein a transaction module and a prawn product real-time information pushing module are arranged on the transaction controller, a prawn breeding pond unit is connected to the transaction controller, and the prawn breeding pond unit is in one-to-one correspondence with a prawn farmer; The transaction module is used for receiving the sales request and the purchase request; the transaction module is used for transaction operation; The prawn product real-time information pushing module is used for acquiring net cage prawn selling information and box-sealedprawn selling information; Pushing corresponding prawn selling information to the prawn purchasing user according to a reservation request and a purchasing request sent by the prawn purchasing user; and the transaction controller is connected with a transaction database. The method has the advantages that selling and purchasing messages are pushed bidirectionally and intelligently, and prawn breeding experience is enhanced.

The invention discloses a new energy power generation active power smoothing control method, a system and a storage medium. The method comprises the following steps obtaining the total power generation active power of all new energy field stations in a regional power network and the rated power of the new energy energy storage system; according to the active power smoothing target of new energy generation, the active power smoothing target deviation of new energy generation station is calculated. According to the target deviation of active power smoothing and the rated power of new energy energy storage system, the control target of new energy energy storage system which can meet the demand of active power smoothing control of new energy generation is determined. The active power of new energy is smoothly controlled according to the determined control objective of the new energy storage system. The invention can realize the complementation and offset of the active power fluctuation between the new energy field stations, effectively reduce the active power fluctuation of the new energy source, and reduce the matching construction cost of the new energy field stations.

The invention provides a small-sized double-rotor fault simulation experiment table and a fault diagnosis simulation method. The experiment table comprises two main components. The first part is an upper mechanical part, and the second part is a lower embedded control part. The mechanical part is mainly composed of double rotors rotating independently and a case simulating faults, and the embedded control part is composed of a vibration acceleration sensor, a rotating speed sensor and all modules. Wherein the lower embedded control part can realize communication with a matched upper computer, and data processing and analysis are realized by adopting a fault diagnosis method based on variational embedded multi-scale permutation entropy (veMPE).

The invention discloses a health management service platform based on big data, which comprises a data acquisition terminal, a big data server and a mobile terminal, wherein the data acquisition terminal and the mobile terminal are wirelessly connected with the big data server, the data acquisition terminal is used for acquiring physical sign data of a user and transmitting the physical sign datato the mobile terminal and the big data server through a wireless communication module, the big data sever is used for performing comparison, analysis and management on the received physical sign data, provides a health management report for the user finally and feeds back the health management report to the mobile terminal through the wireless communication module, the mobile terminal is used forchecking the original physical sign data or the user and the health management report fed back by the big data server, the data acquisition terminal comprises a bracelet, and the bracelet is worn onthe wrist of the user. Compared with the prior art, the platform disclosed by the invention can enable the user to perform individual self-monitoring through the mobile Internet, so that the user cancontact a doctor online at any time and place, and self-management for the health of the user is achieved.