46results about How to "Accurate health status" patented technology

The invention discloses a wearable intelligent hand ring and method for continuously monitoring human body physiological signs. The intelligent hand ring comprises a hand ring body; a monitoring circuit is embedded in the hand ring body; the monitoring circuit comprises a main control unit and multiple monitoring units; each monitoring unit monitors the various human body physiological signs which are processed by a main control unit. The wearable intelligent hand ring and method for the continuously monitoring the human body physiological signs have the following beneficial effects of integrating multiple sensing circuits, being capable of collecting multiple human body physiological signs and realizing the parallel processing on various collected data; the collected human body physiological signs are analyzed by adopting a unique data analysis method, so the human body healthy condition can be comprehensively accurately obtained; a user can regularly start the monitoring function by triggering of one hand or controlling of the main control unit, and the need that the human body physiological signs of the user are monitored anytime anyplace for 24 hours is met; the intelligent hand ring is provided with a wireless communication unit, and data synchronization can be performed between the intelligent hand ring and a mobile terminal, so the user can learn the physiological signs and the healthy condition in real time.

The invention provides methods for determining a characteristic of a tissue sample, such as a state of health, using spectral data and / or images obtained within an optimal period of time following the application of a chemical agent to the tissue sample. The invention provides methods of determining such optimal windows of time. Similarly, the invention provides methods of determining other criteria for triggering the acquisition of an optical signal for classifying the state of health of a region of a tissue sample.

The invention relates to the technical field of power monitoring, in particular to a big data analysis-based power equipment state evaluation method, which comprises the following steps of S10, determining a basic parameter system; removing parameters with small correlation with the fault defects, and constructing a key parameter system for state evaluation; S20, checking the quality of the data in the key parameter system, and conducting data cleaning work when the quality of the data does not meet the requirement; S30, judging the equipment state and the abnormal change of the association relationship of the equipment state by utilizing the correlation, and carrying out equipment abnormality detection by analyzing the association relationship and the change rule of the multi-dimensionalstate data; and S40, screening out the abnormal key performance according to the related characteristic parameters of the power transmission and transformation equipment, and obtaining the evaluationvalue of each key performance. Fusion analysis and deep mining are performed on the multi-source data of power equipment state monitoring so that rapid detection and personalized state evaluation of an abnormal state can be realized, and the health state of the key performance can be comprehensively, timely and accurately mastered.

The invention discloses a power equipment image processing method. The method comprises the steps that a power equipment image and a sample image are acquired and processed, wherein processing comprises image preprocessing and image processing, and image processing comprises feature parameter extraction; and feature parameters of the power equipment image and feature parameters of the sample image are compared, and the state of power equipment corresponding to the power equipment image is described according to the comparison result. By use of the method to process the power equipment image, the power equipment image is converted into state description of the power equipment corresponding to the power equipment image, therefore, information valuable to power equipment monitoring is effectively extracted through computing equipment, labor cost and the storage space demand are greatly lowered, and monitoring efficiency and accuracy are greatly improved.

The invention provides a cell health state estimation method, device and system. The method comprises the following steps of acquiring a cell parameter; calculating calendar life decay and cycle life decay of a cell according to the cell parameter; and estimating a current health state of the cell according to the calendar life decay and the cycle life decay of the cell. In the invention, the current health state of the cell can be accurately and conveniently estimated, the method can be applied to a cell management system, and the health state of the cell can be accurately estimated in real time.

The invention discloses a valve-regulated lead-acid storage battery health state prediction method based on an improved LSTM neural network. The method comprises the steps: obtaining the floating charge voltage, the average charge current, the average charge duration, the discharge cut-off voltage and the discharge duration input data of a storage battery through daily measurement of an online monitoring device, the capacity of the storage battery being measured through check equalizing charge every two months; with n days being taken as a time span, establishing n-dimensional sample input x(ti); establishing a neural network model comprising a plurality of LSTM neural network units by taking a storage battery capacity data sequence h(ti) as output and the x(ti) as input; in an initial state, assigning a weight matrix W and an offset matrix b in the network by randomly generating a decimal between 0 and 1; and introducing a Dropout algorithm to improve the LSTM neural network model, and improving the training process of the LSTM neural network model. According to the method, the problems of over-low prediction precision and under-fitting caused by insufficient data samples can be reduced, the health state of the transformer substation storage battery is accurately predicted, and the utilization rate of the storage battery is improved.

The invention discloses an MOA condition management and fault prediction method and an MOA condition management and fault prediction system. The method comprises that an MOA condition management and fault prediction model is established, and takes MOA characteristic parameter data as input and the MOA condition as output; operation data related to different MOA conditions is calculated, and a training knowledge base is established based on the calculated operation data; the MOA condition management and fault prediction model is trained by utilizing data stored in the training knowledge base; and the MOA characteristic parameter data measured in real time is input to the trained MOA condition management and fault prediction model to obtain the MOA condition. According to the technical scheme, the operation data that reflects the different MOA conditions is calculated to train the MOA condition management and fault prediction model, and then the real-time MOA characteristic parameter data is input to the trained MOA condition management and fault prediction model to obtain the accurate MOA condition, and basis is provided for MOA maintenance.

The invention discloses a method used for determining a human body health state and a mobile terminal. The method comprises the following steps of acquiring the gait parameter of a mobile terminal user and acquiring a standard gait parameter, wherein the standard gait parameter is the gait parameter of a normal gait; comparing the gait parameter of the user with the standard gait parameter, and acquiring a comparison result; and according to the comparison result, determining the health state of the user. In the technical scheme, the gait parameter of the mobile terminal user is combined withthe health state; a mobile terminal is used to collect the gait parameter of the mobile terminal user; and according to the gait parameter of the mobile terminal user, the health state of the mobile terminal user is determined. Because the gait parameter of the user can reflect the health state of the user to a great extent, by using the method and the terminal, the health state of the user can beaccurately determined so that a purpose of providing a health service for the user through the mobile terminal is reached.

The invention relates to the field of electronics, and discloses a health information monitoring method and health information monitoring equipment. According to the health information monitoring equipment disclosed by the invention, body temperature, sleep duration and the number of walking steps of a monitored object are acquired by virtue of a sensor which is arranged in the health information monitoring equipment; the health status of the monitored object is acquired in accordance with the acquired body temperature, sleep duration and the number of walking steps; and in accordance with the acquired health status of the monitored object, a warning mechanism is triggered, so that a user, by virtue of carry-on electronic equipment, can simultaneously detect the body temperature, the sleep duration and the number of walking steps, so as to master own health status at all times and places. In addition, the user, by triggering the warning mechanism in accordance with detection equipment, can judge whether own physical health is under a good state or not.

The invention belongs to the field of battery related technologies, and specifically discloses a neural network-based lithium battery ultrasonic information characteristic extraction method. The method comprises the following steps of constructing two neural networks, taking ultrasonic information as input vectors and respectively taking health states and charge states as output vectors; acquiringultrasonic information as a training sample and training the two neural networks to obtain two corresponding neural network models; and inputting the acquired ultrasonic information of a lithium battery into the two neural network models so as to obtain a health state and a charge state of the lithium battery. According to the method, the two neural networks are constructed and then trained to obtain the two neural network models, so that the health state and the charge state of the lithium battery can be obtained at the same time by utilizing the ultrasonic information, thereby overcoming the bottleneck that the users cannot judge which states cause the changes of the ultrasonic signal amplitudes.

Disclosed is a detection method for a health status of an energy storage device. The detection method comprises the steps of detecting and obtaining influencing parameters of the number of charging and discharging on the health status of the energy storage device under conditions of the constant temperature and current by taking a ratio of the current capacity to the nominal capacity as a reference; detecting and obtaining influencing parameters of a charging temperature value on the health status of the energy storage device under constant temperature through a plurality of charging and discharging currents of different volatility; and fitting the number of charging and discharging, a charging temperature average value, charging and discharging current half volatility endpoint and average values and the corresponding influencing parameters, and obtaining an influence function of the number of charging and discharging, the charging temperature average value, the charging and discharging half volatility endpoint and average values on the health status of the energy storage device. Through the above method, the influences of the temperature and current are considered comprehensively and thus the more accurate health status can be obtained.

The present invention provides a temperature measurement device and a temperature measurement method. The temperature measurement device includes a first sensor, used for collecting a body surface temperature of an organism; a second sensor, used for collecting an ambient temperature; and a processing unit, which is electrically connected with the first sensor and the second sensor respectively, and is used for performing data processing on the body surface temperature according to the ambient temperature to obtain an actual body temperature of the organism. Due to the arrangement of the first sensor, the second sensor and the processing unit, the temperature measurement device can perform data processing on the collected body surface temperature according to the ambient temperature to make the obtained actual body temperature of the organism more accurate, and further to facilitate accurately judging the health status of the organism.

The invention relates to a ship generator part influence identification method and system based on data driving, wherein the method comprises the steps of obtaining the operation parameter informationof a ship generator and the state parameter information of a part, and obtaining a first data sample set; preprocessing the sample data in the first data sample set to obtain the normal sample data of the ship generator under a stable working condition, and further obtaining a second data sample set; based on the second sample data set, dividing the working conditions, and obtaining a reference deviation value of different part state parameters under the analysis working condition ; obtaining the state characterization information of the part state parameters under the analysis working condition based on the reference deviation value; and visually displaying the state representation information of each part parameter under the analysis working condition. By adopting the method or the system provided by the invention, the health state of the ship generator part can be accurately evaluated, analyzed and displayed, and the problems that measurement and judgment only depend on experiences, quantitative analysis and evaluation are lacked and a graphic result cannot be clearly displayed, in the prior art, are solved.

The invention provides a rail transit vehicle health diagnosis method and device, and the method comprises the steps: collecting the full-life-cycle data of a rail transit vehicle based on a workshop service system, and carrying out the preprocessing, and forming a tag data set; constructing a digital twinborn health analysis model by using an adaptive ant lion strategy and a virtual modeling technology, and inputting the label data set into the data twinborn model for training; judging a training result according to a Bayesian judgment principle, and if the Bayesian judgment principle is met, outputting a model as a vehicle digital twin health diagnosis model; and inputting to-be-diagnosed rail transit vehicle data into the vehicle digital twinborn health diagnosis model in real time for health diagnosis, and calculating and outputting final diagnosis data. According to the method, the health state of the rail transit vehicle can be accurately analyzed in real time, operation decision information is provided for a user, traffic accidents caused by vehicle health are avoided, social threat is reduced, and the safety of passengers and drivers is guaranteed.

The invention provides a method and device for judging the state of a negative pressure system. The method includes the steps of setting up membership functions of all evaluation indexes of a unit to be judged, generating an evaluation rating set, obtaining running state values of all the evaluation indexes of the unit to be judged, setting up fuzzy similar matrixes of integrative factors of the unit to be judged according to a set weight set, the evaluation rating set and the running state values, generating fuzzy equivalent matrixes of the fuzzy similar matrixes of the integrative factors through square calculation, classifying the fuzzy equivalent matrixes according to set cutting values, and generating a judgment result for the state of the negative pressure system of the unit to be judged. According to the method and the device, the health condition of the negative pressure system of the unit can be accurately analyzed and judged, the method and the device are suitable for both water-cooled units and air-cooled units, accurate classification can be conducted on complex equipment, no omitting occurs, reliable analysis is provided for related personnel, and therefore work efficiency of a whole power plant is improved.

The invention discloses a wearable smart bracelet and a method for continuously monitoring human physiological signs. The smart bracelet includes a bracelet body, and a monitoring circuit is embedded in the bracelet body; the monitoring circuit includes a main control unit and various monitoring devices. unit. Each monitoring unit simultaneously detects various types of human physiological sign data, which are processed in parallel by the main control unit. The present invention has the following beneficial effects: a variety of sensor circuits are integrated, various human physiological signs can be collected, and various types of collected data can be processed in parallel; a unique data analysis method is used to analyze the collected human physiological signs , can obtain a comprehensive and accurate health status of the human body; the user can trigger the start with one hand or start the monitoring function at a fixed time controlled by the main control unit, which meets the needs of the user to monitor the physiological signs of the human body 24 hours anytime and anywhere; it is equipped with a wireless communication unit, which can communicate with the mobile terminal Through data synchronization, users can understand the physiological signs and health status of the human body in real time.

A health condition estimation device capable of accurately estimating the health condition of a cow includes: a three-dimensional coordinates acquisition unit which acquires a group of three-dimensional coordinates representing the three-dimensional shape of a cow extracted from a distance image of the cow; a feature amount extraction unit which extracts the feature amount of the cow according to the group of three-dimensional coordinates acquired by the three-dimensional coordinates acquisition unit; and a score calculation unit which calculates a score that indicates the health condition of the cow, according to the feature amount extracted by the feature amount extraction unit. It is preferable: that the feature amount extraction unit extracts the feature amount that indicates the degree of sinking of the rumen, according to a group of three-dimensional coordinates in a rumen area in the group of three-dimensional coordinates acquired by the three-dimensional coordinates acquisition unit, the rumen area being an area on the body surface of the cow proximal to the rumen; and that the score calculation unit calculates the cow rumen fill score according to the feature amount extracted by the feature amount extraction unit and representing the degree of sinking of the rumen.

The invention provides a vehicle health prediction method, a vehicle health prediction device and equipment. The vehicle health prediction method comprises the steps of: acquiring attribute information and historical driving data of a target vehicle; generating a user usage investigation report according to the attribute information of the target vehicle, so as to acquire usage feedback data provided by the user according to the investigation report; acquiring a first reference data set according to the attribute information of the vehicle; and determining a current health status of the targetvehicle according to the historical driving data of the vehicle, the usage feedback data, and health status of reference vehicles in the first reference data set. The vehicle health prediction methodrealizes timely and effective detection of the health status of the vehicle, and further helps the user to timely and precisely grasp the health status of the vehicle, so that the user can perform correct and reasonable maintenance on the vehicle, thereby improving the safety and reliability of the user in driving the vehicle, and enhancing the usage experience of the user.

Provided is a method for estimating the state of health of a battery according to various embodiments. A method for estimating the state of health of a battery according to one embodiment, comprises: a step for measuring the voltage and the current of a battery in use to periodically generate a voltage value and a current value; a step for updating, in real time, the value of a G parameter and the value of an H parameter representing the current state of the battery from the voltage value and the current value using an adaptive filter; and a step for estimating, in real time, the state of health of the battery using the preset initial value and end value of the G parameter and the current value of the G parameter. The G parameter is a parameter representing the sensitivity of a voltage with respect to a current change of the battery, and the H parameter is a parameter representing the effective electric potential determined by local equilibrium electric potential distribution and resistance distribution in the battery.

The invention provides a vibration monitoring and tunneling equipment data real-time acquisition synchronization method and system, which are used for solving the technical problem that synchronous analysis of vibration monitoring and tunneling equipment data is difficult due to different storage positions, large sampling frequency difference and difficult time synchronization of the vibration monitoring and tunneling equipment data. The method comprises the following steps: synchronously updating the time and date of equipment for acquiring vibration data and tunneling equipment data; selecting the smaller one of the sampling frequencies of the vibration data and the tunneling equipment data as a reference frequency, and scaling the sampling frequency of the vibration data or the tunneling equipment data by using the reference frequency; sparse processing is carried out on the collected vibration data, so that the sampling time interval between two adjacent points in the vibration data is consistent with the tunneling equipment data; and combining the rarefied vibration data with the tunneling equipment data to complete synchronous linkage analysis of the vibration data and the tunneling equipment data. The synchronization efficiency and automation degree of the vibration data and the tunneling equipment data are high, and the method is easy to implement.

The invention provides a health diagnosis method and device suitable for rail transit vehicles, including: collecting and preprocessing the whole life cycle data of rail transit vehicles based on the workshop service system to form a label data set; using self-adaptive antlion strategy and virtual construction Modeling technology to build a digital twin health analysis model, input the label data set into the data twin model for training; judge the training results according to the Bayesian judgment principle, and output the model if the Bayesian judgment principle is satisfied It is a vehicle digital twin health diagnosis model; the rail transit vehicle data to be diagnosed is input into the vehicle digital twin health diagnosis model in real time for health diagnosis, and the final diagnosis data is calculated and output. The invention can accurately analyze the health status of rail transit vehicles in real time, provide operation decision information for users, avoid traffic accidents caused by vehicle health, reduce social threats, and ensure the safety of passengers and drivers.

The invention provides a method and a device for automatically deploying different interface flow ratios in an interface calling system. The method comprises the following steps: receiving a query request sent by a client; obtaining first flow scheduling proportion data; generating a first calling request to call the first data provider interface, judging whether the first calling request is successfully called or not, and if the first calling request fails to be called, fusing the first data provider interface to obtain second flow scheduling proportion data; when the second call request is successfully called, sending the query request to a second data provider according to the second traffic scheduling proportion data, adapting the query result, generating feedback data in a preset format, and sending the feedback data in the preset format to the client; and if successful, sending the query request to the first data provider according to the first traffic scheduling proportion data, adapting the query result, generating feedback data in a preset format, and sending the feedback data in the preset format to the client.

The invention provides an SOH estimation method and device for a power battery system of an electric vehicle, and belongs to the technical field of vehicle-mounted power battery systems. The SOH estimation method comprises the steps: electrifying a power battery system, and determining the state of charge SOCStart charge of the power battery system at the start moment of current charging; and according to the SOCStart charge, the rated capacity CR of the power battery system, and the health state SOHBCR based on the capacity retention rate of the power battery system, which is calculated lasttime, calculating to obtain the initial capacity CStart charge of the power battery system at the beginning of the current charging: CStart charge = SOCStart charge * CR * SOHBCR; counting the charging capacity CCharge in the current charging process at the charging end moment; according to the CStart charge and the CCharge, obtaining the capacity Cend charge at the charging end moment, wherein the Cend charge = CStart charge + CCharge; and according to the Cend charge and the CR, obtaining the state of health SOHCRR based on the capacity retention rate of the power battery system at this time: SOHCRR = Cend charge / CR * 100%. The accuracy of the health state of the power battery system can be improved.

The invention discloses a method for directly identifying and judging the health state of an IGBT (Insulated Gate Bipolar Translator) in electrical equipment, which is characterized by comprising the following steps of: starting to pre-charge the electrical equipment; after charging is completed, a control instruction is sent out, waiting is delayed, and interference is eliminated through multiple times of measurement; and recording a measurement value under each instruction. Analyzing and processing the data according to the mode of the control instruction; when a certain value exceeds a certain range of a production record value, or is obviously different from data of devices at other positions, or the value is unstable and has a large fluctuation range, the device can be judged as a risk device. The invention provides a brand-new identification method, the health state of the power device can be quickly and accurately obtained, meanwhile, the modes of time-sharing detection, grouping detection and circulating detection can be supported, and the health state of the device is screened and identified through the absolute value and the relative difference value.

A health condition estimation device capable of accurately estimating the health condition of a cow includes: a three-dimensional coordinates acquisition unit which acquires a group of three-dimensional coordinates representing the three-dimensional shape of a cow extracted from a distance image of the cow; a feature amount extraction unit which extracts the feature amount of the cow according to the group of three-dimensional coordinates acquired by the three-dimensional coordinates acquisition unit; and a score calculation unit which calculates a score that indicates the health condition of the cow, according to the feature amount extracted by the feature amount extraction unit. It is preferable: that the feature amount extraction unit extracts the feature amount that indicates the degree of sinking of the rumen, according to a group of three-dimensional coordinates in a rumen area in the group of three-dimensional coordinates acquired by the three-dimensional coordinates acquisition unit, the rumen area being an area on the body surface of the cow proximal to the rumen; and that the score calculation unit calculates the cow rumen fill score according to the feature amount extracted by the feature amount extraction unit and representing the degree of sinking of the rumen.

The invention provides a vehicle health prediction method, a vehicle health prediction device and equipment. The vehicle health prediction method comprises the steps of: acquiring attribute information and historical driving data of a target vehicle; generating a user usage investigation report according to the attribute information of the target vehicle, so as to acquire usage feedback data provided by the user according to the investigation report; acquiring a first reference data set according to the attribute information of the vehicle; and determining a current health status of the targetvehicle according to the historical driving data of the vehicle, the usage feedback data, and health status of reference vehicles in the first reference data set. The vehicle health prediction methodrealizes timely and effective detection of the health status of the vehicle, and further helps the user to timely and precisely grasp the health status of the vehicle, so that the user can perform correct and reasonable maintenance on the vehicle, thereby improving the safety and reliability of the user in driving the vehicle, and enhancing the usage experience of the user.

The embodiment of the invention discloses a method and device for determining the health state of a battery, electronic equipment and a storage medium. The method for determining the health state of the battery comprises the steps that the charging and discharging current and the environment temperature of each charging and discharging cycle of the battery and the state of charge after the charging and discharging cycles are finished are obtained; determining an attenuation factor of each charge-discharge cycle according to the charge-discharge current of each charge-discharge cycle, the environment temperature and the charge state after the charge-discharge cycle is ended; and determining the health state of the battery according to the attenuation factor of each charge-discharge cycle, the current charge-discharge cycle number of the battery and the total charge-discharge cycle number of the battery. When the health state of the battery is calculated, the charging and discharging current of each charging and discharging cycle, the environment temperature, the charge state after charging and discharging, the current number of charging and discharging cycles and the total number of charging and discharging cycles of the battery are comprehensively considered, so that the determined health state of the battery is more accurate and better conforms to the actual health state of the battery.

The invention discloses a pulse condition detection method based on a relative intensity pulse condition theory. The method comprises the steps of sequentially collecting a plurality of sets of pulsecondition data through a single sensor or a plurality of sensors installed on a detection device; analyzing the pulse condition data according to the relative intensity pulse condition theory to obtain a diagnosis result; presenting the diagnosis result to a user. The invention further discloses the device for detecting the pulse condition data based on the relative intensity pulse condition theory. The device comprises a pulse condition sensor, an MCU and a client side. By means of the embodiment, the pulse condition is analyzed based on the relative intensity pulse condition theory, and thehealth condition of the body can be more accurately obtained; meanwhile, the theoretical system of the method has the advantages of being very simplified and practical, the data can be systematicallyanalyzed, health monitoring and health management schemes are combined, and the convenient and intelligent effects are achieved.

The invention provides a mining battery pack two-stage distributed type management system. The mining battery pack two-stage distributed type management system comprises a management battery pack, a battery monitoring unit, a field control host and a server; the management battery pack consists of a plurality of batteries connected in series; the management battery pack is electrically connected with the field control host through a battery general monitoring unit and a current sensor separately; the batteries are electrically connected with the field control host through the battery monitoring unit separately; the field control host is electrically connected with a temperature sensor, a voltage sensor, the battery monitoring unit and the server separately; and the voltage sensor is electrically connected with the battery general monitoring unit. By virtue of the battery general monitoring unit and the battery monitoring unit for performing the distributed type management on the wholebattery pack, the interface and control complex degree is simplified, and the server can perform battery performance analysis according to the received battery parameter information so as to obtain accurate battery health state.

The invention provides an ECG (electrocardiogram) diagnosis system based on chaos features. The system comprises an ECG acquisition front-end, namely, single-channel ECG equipment, and further comprises an ECG diagnosis platform, wherein the ECG acquisition front-end is used for acquiring ECG data through a single lead and transmitting the ECG data to the ECG diagnosis platform through a USB interface or WIFI wireless communication; chaos feature parameters of ECG signals are extracted by an ECG analysis terminal and compared with a discriminant rule obtained through training samples in advance, and a diagnosis result can be obtained. The ECG diagnosis system has the advantage that the health condition of the heart can be described more accurately according to the chaos features of the ECG signals. The chaos features of the ECG signals include a correlation dimension and a more accurate lyapunov exponential spectrum, and the heart diseases can be diagnosed more effectively according to the feature parameters.