50 results about "Life condition" patented technology

The invention relates to the technical field of monitoring and early warning, in particular to an intelligent pressure vessel pressure monitoring and early warning system which comprises an environmental influence assessment module, a pressure state analysis module, a pressure trend analysis module and a risk assessment and early warning module. According to the invention, by analyzing the correlation between the environment temperature and the service life of the pressure vessel, the early warning threshold value is automatically adjusted, the dynamic adaptability of the early warning threshold value is realized, the real condition of the pressure vessel in a specific operation environment is reflected more accurately, the accuracy and timeliness of safety early warning are improved, and the working efficiency is improved. By means of calculation of the pressure state deviation index and the deviation state grade, the safety state of the pressure container can be judged more accurately, potential risks are evaluated more pertinently, the abnormal pressure change trend can be recognized through continuous analysis of the pressure change rate, the service life condition of the pressure container is combined, and the safety of the pressure container is improved. And the overall abnormal risk is evaluated and the corresponding early warning level is formulated, so that the potential safety accident risk is reduced.

The invention relates to a manned spacecraft on-orbit health state evaluation method, which comprises the steps of manned spacecraft on-orbit state evaluation: evaluating the on-orbit state of manned spacecraft key equipment according to telemetry parameter evolution characteristics representing manned spacecraft function indexes; evaluating the technical capability of the manned spacecraft: before the manned spacecraft executes a key on-orbit task, evaluating whether a cabin sealing function, an attitude control function, an orbit control function, a docking function, and a mechanical arm and a pressure relief and recompression device related to execution of the key task are normal or not; carrying out task evaluation on the manned spacecraft: carrying out task execution condition evaluation on a key on-orbit task during the on-orbit operation period of the manned spacecraft; reliability and safety evaluation: evaluating the on-orbit safety, reliability, maintainability and life condition of the manned spacecraft, and identifying internal and external risks possibly existing in the long-term on-orbit operation period; and guarantee condition evaluation: evaluating whether the ground flight control team guarantee condition meets the flight control task requirement or not.

The invention discloses a GaN HEMT discrete multi-stage active gate driver and a driving method, and relates to the technical field of gate drivers, and the driver comprises a PWM generation module which outputs a PWM high-frequency signal; the driving signal generation module is used for generating a driving signal through a logic gate combination PWM signal and adjusting a driving stage; the gate driving module comprises two independent dual-channel gate drivers, each driver is provided with two independent driving units, each driving unit comprises a driving stage, and the driving stages are controlled through driving signals to enable the GaN HEMT to have discrete gate resistance values; and the device service life condition prediction module is used for optimizing an active grid driving strategy by collecting dynamic electrical parameters and thermodynamic parameters of the device in real time. According to the invention, switching oscillation and voltage and current overshoot generated by the GaN HEMT at an ultrahigh switching speed are effectively suppressed, real-time service life prediction and active protection of the GaN HEMT are realized, and the system reliability is improved.

The invention discloses multi-station high-low temperature life testing equipment for a ball screw pair for automobile braking. The multi-station high-low temperature life testing equipment comprises a driving mechanism, a high-low temperature environment box, a loading mechanism, an experiment frame, a cast iron lathe bed and the like. The driving mechanism is used for driving the detected nut sample piece to rotate and monitoring the input torque in real time; the high and low temperature environment box is used for providing environment temperatures under different working conditions for the tested sample piece; and the loading mechanism is used for stably loading the ball screw and monitoring the loading force in real time. The experiment frame is used for bearing variable load generated by the loading mechanism and ensuring stable work of the test equipment. The casting lathe bed is used for providing a base plane with certain horizontal precision and installing the experiment frame and the linear guide rail. The device is compact in layout and reliable in structure, can provide service life tests and comprehensive efficiency tests for a plurality of ball screw pairs for automobile braking in high and low temperature environments at the same time, greatly shortens the verification period of the service life conditions of the automobile ball screws in the high and low temperature environments, and is high in measurement efficiency and space utilization rate.

The invention discloses an elevator health prediction method and system based on deep learning, and relates to the technical field of health prediction of special equipment. Comprising the steps of obtaining operation state data of a to-be-evaluated elevator in multiple operation processes; collecting the fault operation state data of the to-be-evaluated elevator to obtain a training set, and collecting the normal operation state data to obtain a verification set; constructing a health life prediction network; inputting the training set into a health life prediction network, and updating network weight parameters through a loss function to obtain a trained health life prediction model; and inputting the verification set of the to-be-evaluated elevator into the trained health life prediction model, evaluating the health life prediction result of the elevator, and outputting the health life condition level of the elevator. Multi-dimensional health inspection on the elevator is facilitated, the health condition of the elevator is scientifically analyzed, potential safety hazards of the elevator and safety parts are pre-judged, life health prediction of the elevator in the whole life cycle is achieved, and elevator safety accidents are reduced.

The invention provides a detection method for detecting the sleep state of a child in a child seat and the child seat. The detection method comprises the steps that the use condition of a mobile phone is monitored, and a first condition report about the child is obtained; after the seat body detects the child, the child is detected, and a second situation report about the child is obtained; generating a first seat adjusting command according to the first condition report and the second condition report; analyzing the driving record of the mobile tool to obtain third data, and generating a second seat adjusting command in combination with the second condition report; the seat body is adjusted according to the first seat adjusting command and / or the second seat adjusting command. The technical problems that in the related technology, an adjusting mechanism of a child seat is single, linkage is not formed with the actual life condition of a child and running of a moving tool, and a linkage adjusting mechanism is lacked are solved, so that adjustment of the seat is difficult to meet complex and changeable actual requirements, and safety and comfort of the child cannot be guaranteed when the child takes a car are solved.

The invention provides a mobile body mediation system and a mobile body mediation method, which match a user who starts to use a mobile body such as a vehicle through a transaction with a mobile body that satisfies a desired remaining life condition of the user. A moving body mediation system (1) is provided with: a desired remaining life condition identification unit (11) that identifies a desired remaining life condition for a moving body by a user; a candidate moving body remaining life identification unit (12) that identifies the remaining life of the plurality of candidate moving bodies; a remaining life consumption degree identification unit (13) that identifies a remaining life consumption degree on the basis of use history information of the moving body by the user; a corrected remaining life calculation unit (15) that calculates a corrected remaining life obtained by correcting the remaining life of the plurality of candidate moving bodies by the degree of remaining life consumption; and a transaction target moving body selection unit (16) that selects, as a target of a predetermined transaction with the user, a candidate moving body for which the corrected remaining life satisfies the desired life condition.

The invention discloses an LSTM integrated residual life prediction model based on multi-dimensional data processing, and the model comprises the following steps: S1, selecting a measurement point of a device, and collecting the signal data of the measurement point, the data types comprising a temperature signal and a vibration signal; s2, performing data preprocessing on samples in the data set, deleting columns of which the data missing value proportion is higher than a threshold value a, and filling rows of which the data missing value proportion is lower than a threshold value b; carrying out feature engineering processing on the preprocessed data to obtain features capable of being input into the LSTM model; s3, constructing a plurality of LSTM models, and adopting a network serial integration strategy to accurately predict the residual life of the equipment; and S4, testing the test set by using the trained integrated neural network model to obtain a prediction result of the residual life of the equipment. According to the method, multi-dimensional measurement point data is utilized, prediction is carried out in the trained LSTM models through the collected data, and the service life condition of the equipment is effectively reflected.

According to the invention, a method for determining the service life condition of a vibration damper (1), in particular a torsional vibration damper or shock absorber, comprises a main mass and a secondary mass, and a working chamber arranged between the main mass and the secondary mass, the working chamber being filled with a viscous damping medium, wherein the vibration damper (1) is arranged on a crankshaft (7) of an engine (8), in particular an internal combustion engine, and in order to suppress or eliminate torsional vibrations of the crankshaft (7), the crankshaft (7) constituting a part of the engine (8) is designed to have the following steps when operating the engine (8): S1) operating the engine (8); S2) determining at least one operating parameter of the engine (8); S3) simulating the temperature distribution of the viscous damping medium in the working chamber; and S4) determining the service life condition of the vibration damper (1) based on the operating parameter of the engine (8) and the result of simulating the temperature distribution of the viscous damping medium. Components for carrying out the method are also provided.

The present invention discloses a system and method for predicting the life of a DC fast charging contactor based on big data mining, and relates to the technical field of electronic component life prediction. The present invention includes a judgment parameter acquisition module for obtaining the service life condition judgment parameters of the DC fast charging contactor; a database processing module, based on the service life condition judgment parameters of the DC fast charging contactor, forms a database of the service life condition judgment parameters of the DC fast charging contactor; a data screening module, based on the service life condition judgment parameter database of the DC fast charging contactor, constructs a data screening model to form life prediction historical learning data; a life prediction module, based on the life prediction historical learning data, forms a DC fast charging contactor life prediction model; and a warning module. By adopting the method of big data mining, the continuous changes of data are used to analyze the changes between various parameters and the life of the DC fast charging contactor, so as to achieve accurate prediction of the life and prevent problems before they occur.

The invention discloses a method and equipment for detecting living conditions of elderly people living alone based on gas data and a medium. The method comprises the following steps: collecting daily gas consumption historical data of the elderly people living alone, establishing a single-time gas consumption detection model and establishing a gas consumption detection model of a statistical period in different seasons; collecting daily gas consumption data of the elderly in real time, matching the daily gas consumption data with the single-time gas consumption detection model in the corresponding period, and judging whether the gas consumption data of the elderly living alone is abnormal or not; if yes, abnormal information is sent to the client; counting the gas consumption data in the last statistical period, matching the gas consumption data with the gas consumption detection model in the corresponding period, and judging whether the gas consumption data of the elderly living alone is abnormal or not; if the data is abnormal, sending abnormal information to the client, otherwise, labeling the data and adding the data into a real-time database; according to the invention, by analyzing and detecting the gas consumption data, the daily life condition of the elderly living alone is effectively monitored, so that the elderly can timely and effectively find the abnormal condition and take measures when the abnormal condition occurs.

A propulsion system includes an electrical assembly and a controller. The electrical assembly includes at least one contactor and a sensor assembly. The at least one contactor is switchable by a switching operation between an open condition and a closed condition. The controller is connected in signal communication with the sensor assembly. The controller is configured to determine, using the sensor assembly, an electrical energy of the at least one contactor for each switching operation of the at least one contactor to determine a cumulative electrical energy for the at least one contactor and identify a presence or an absence of an end-of-life condition for the at least one contactor by comparing the cumulative electrical energy to an end-of-life energy threshold. The presence of the end-of-life condition is identified where the cumulative electric energy exceeds the end-of-life energy threshold.

The invention discloses a rotating machine equipment life analysis method and system, and belongs to the technical field of mechanical equipment life prediction, and the method specifically comprises the steps: collecting data related to the life of rotating machine equipment, carrying out the preprocessing of the collected data related to the life of the rotating machine equipment, and carrying out the analysis of the life of the rotating machine equipment according to the obtained data related to the life of the rotating machine equipment; establishing a rotating machine equipment life analysis model, predicting the life of the rotating machine equipment by using parameter values of the rotating machine equipment life analysis model and the adjustment model, analyzing the life condition of the rotating machine equipment according to a prediction result, finding out an existing risk, and formulating a corresponding maintenance plan or measure. Compared with the prior art that the service life prediction model of the rotating machine equipment is directly trained through the equipment operation data, the precision of predicting the residual service life of the rotating machine equipment is greatly improved, and manpower is saved.

The invention relates to the technical field of endowment service evaluation, in particular to a family endowment ability evaluation method, device, equipment, medium and program, and the method comprises the steps: inputting life condition data, nursing support data and emotion support data into a trained target language model, calling a pre-established service evaluation system by the target language model, mapping the life condition data, the care support data and the emotion support data to pre-established evaluation indexes in a family old-age care ability evaluation system, and calculating a quantitative score of the corresponding evaluation index according to mapping data corresponding to each evaluation index; and identifying a weight corresponding to each evaluation index, and generating a family old-age care ability evaluation result according to the quantitative score of each evaluation index and the corresponding weight. Therefore, the problems of low data processing efficiency, one-sided evaluation dimension, insufficient evaluation result precision, large error and the like caused by a manual evaluation mode in related technologies are solved.

The invention provides a surgical instrument service life indication information adjusting method and a surgical robot, and relates to the technical field of medical instruments, and the surgical instrument service life indication information adjusting method comprises the steps that service life indication information of a surgical instrument and real service life information of the surgical instrument are acquired, and the service life indication information is service life information indicated by a service life indication shaft of the surgical instrument; determining a life difference between the life indication information and the real life information; and adjusting life indication information of the surgical instrument based on the life difference. The service life indication information of the surgical instrument can be corrected based on the real service life information of the surgical instrument, so that the service life indication information of the surgical instrument is consistent with the real service life information, and the accuracy and precision of the service life indication information of the surgical instrument reflecting the real service life condition are improved; a user can obtain the real service life condition of the surgical instrument without depending on additional system query operation, and convenience and reliability in the surgical preparation process are improved.

A polishing system includes a robotic polishing assembly, a belt removal assembly, and a controller. The robotic polishing assembly includes an end effector. The end effector includes a polishing arm, a motor, and a tool head. The tool head extends along a lengthwise axis between and to an inner end and a tip end. The tool head includes a belt tensioner, a roller, and an abrasive belt. The belt tensioner is configured to bias the tip end outward from the inner end. The abrasive belt is disposed on the motor and the roller. The belt removal assembly includes a first lateral arm, a second lateral arm, and a back stop. The controller includes a processor in communication with a non-transitory memory storing instructions, which instructions when executed by the processor, cause the processor to: identify an end-of-life condition is present for the abrasive belt and remove the abrasive belt from the tool head.

The invention discloses a system and a method for predicting the service life of a direct-current fast-charging contactor based on big data mining, and relates to the technical field of electronic component service life prediction.The system comprises a judgment parameter acquisition module used for acquiring service life condition judgment parameters of the direct-current fast-charging contactor; the database processing module is used for forming a service life condition judgment parameter database of the direct current fast charging contactor based on service life condition judgment parameters of the direct current fast charging contactor, and the data screening module is used for constructing a data screening model based on the service life condition judgment parameter database of the direct current fast charging contactor and forming service life prediction historical learning data; the service life prediction module is used for forming a service life prediction model of the direct current fast charging contactor based on the service life prediction historical learning data; and a warning module. A big data mining mode is adopted, the change condition between each parameter and the service life of the direct-current fast-charging contactor is analyzed through continuous change of data, accurate prediction of the service life is achieved, and precautions are taken in the bud.

The application discloses a kind of life consumption detection method and device of mechanical hard disk, it is related to the life detection field of mechanical hard disk, first, the life influence information of mechanical hard disk in current period is acquired, the information includes one or more of the vibration amplitude of the temperature value of mechanical hard disk, disk in mechanical hard disk and the information of ECC generated by mechanical hard disk, according to the relationship between these life influence information and life consumption to determine the life consumption degree corresponding to each life influence information, finally, according to the life consumption degree of mechanical hard disk in current period and the current life of mechanical hard disk, the new current life of mechanical hard disk is determined, based on this, the current life of mechanical hard disk can be periodically determined, so as to execute corresponding operation in time according to the life condition of mechanical hard disk, avoid various situations caused by the life of mechanical hard disk is too low, and further avoid economic loss.

The application discloses a tool life monitoring type target object pre-separation device based on a random forest regression algorithm and belongs to the field of artificial intelligence cognitive mechatronics. A cutter spacing adjusting mechanism is installed above a tool holder assembly and a support sheet metal assembly, a horizontal linear slide rail guide assembly is installed below a slide rail guide support assembly, and the slide rail guide support assembly is installed above the support sheet metal assembly. The tool holder assembly is installed below the horizontal linear slide rail guide assembly, and the support sheet metal assembly is connected with a longitudinal screw sliding assembly and a longitudinal optical axis sliding guide assembly respectively. Two ends of the longitudinal screw sliding assembly and the longitudinal optical axis sliding guide assembly are installed on end faces of upper and lower square plates of a rack, a manual rotary positioning table assembly is installed on an end face of a lower square plate of the rack, and a motor driving chain assembly is installed at a lower end of the lower square plate of the rack and connected with the longitudinal screw sliding assembly. The application can predict and display the life condition of a tool and also can adjust the distance between cutters.

The utility model discloses a movable beekeeping platform with transition and living functions, which is matched with transportation equipment for use and comprises a main platform provided with living space, a foldable platform capable of being unfolded and folded is arranged on the rear portion of the main platform, a platform lifting device is arranged on the main platform, and the platform lifting device is connected with the movable beekeeping platform. And lifting support devices are arranged at the bottoms of the main platform and the folding platform. According to the utility model, the fundamental transformation from the original hostile living environment such as the hostile living environment to the home living environment is realized, the living condition of the bee-keeping transition operation is improved, the breakthrough of the bee-keeping transition operation from the original shoulder carrying to the whole-course mechanization is also realized, and the labor intensity of the bee-keeping transition operation is reduced.

The utility model discloses a guide rail multi-load service life detection device, which comprises a base, a cavity is arranged in the base, a driving mechanism is arranged in the cavity, two sides of the top of the driving mechanism are respectively and fixedly connected with a supporting plate, a top plate is fixedly connected between the tops of the supporting plates on the two sides, and the top plate is fixedly connected with the driving mechanism. The bottom of the top plate is fixedly connected with a pressure pump; a to-be-detected guide rail is installed on the surface of the base, the guide rail is slidably connected with a sliding block, the top of the sliding block is fixedly connected with a pressure plate, the top of the pressure plate is fixedly connected with a sleeve, and the sleeve is connected with the telescopic end of a pressure pump in a matched mode. The driving mechanism drives the slide block to move back and forth on the guide rail, and the telescopic end of the pressure pump applies pressure to the slide block through the sleeve and the pressure plate, so that the service life of the guide rail under different pressures is tested, and the working condition that the guide rail bears different acting forces in actual use is simulated; therefore, the durability and the service life of the guide rail can be effectively evaluated.

The present invention relates to an intelligent analysis system for infant needs, comprising at least a first intelligent terminal, a second intelligent terminal, and a cloud server. The first intelligent terminal sends automatically collected physiological information, sound information, and / or behavioral information of an infant to the cloud server. The second intelligent terminal sends manually input life record information and / or actual emotional state information of the infant to the cloud server. The cloud server includes at least a behavioral feature extraction module and a deep learning training module. The feature extraction module extracts behavioral feature parameters of the infant based on a specific pattern established by the deep learning training module, and matches corresponding action training units, thinking training units, and emotional training units based on the behavioral feature parameters. The deep learning training module selects action training units, thinking training units, and / or emotional training units whose coordination is within a set threshold range based on the infant's specific pattern to form an infant training program. The present invention can accurately determine the impact of external changes on infants and improve the infant's living conditions.

The invention discloses a CANoe-based power battery relay instruction opening and closing life test method and system. The test method comprises the following steps: establishing a hardware test platform based on a VectorVN1600 interface card; parameter configuration, panel design and script writing are carried out on the CANoe project through a computer, power-on and power-off instructions are written into different conditions of code circulation in sequence, and instruction sending is achieved during running; running a CANoe project, starting and stopping a test through a switch on a Panel panel, and recording message data of all communication ports through CANoe software; when the circulation is finished, automatic stop is realized, and a user can check strategy instruction execution and relay opening and closing life conditions through data playback; and after the test is completed, analyzing test data, and evaluating whether the power-on and power-off strategy and the relay on-off life meet design requirements. The scheme is automatic, efficient, convenient, accurate and high in reduction degree.

The invention discloses a long continuous building life prediction method and system and a storage medium. The invention relates to the technical field of constructional engineering. Constructing an initial durability index matrix A, and taking the mass a, the compressive strength b and the splitting tensile strength c as columns of the matrix; performing standardization processing on each index in the matrix A to form an initial evaluation value xij, and eliminating dimensional difference; comprising an input layer, a bidirectional Bi-GRU layer, a full connection layer and an output layer which are connected in sequence. Wherein the input layer receives time sequence data alpha (t) of a comprehensive evaluation factor alpha; by fusing the entropy weight method and the bidirectional Bi-GRU recurrent neural network model, the technical scheme of the invention can more accurately capture the durability change rule of the long continuous building under the salt invasion-dry-wet cycle coupling effect, thereby improving the accuracy and reliability of life prediction. And engineers and decision makers are helped to more accurately evaluate the service life condition of the building and formulate more reasonable maintenance and management strategies.

The present invention provides an intelligent axle monitoring system, which relates to the technical field of vehicle monitoring, and includes a monitoring point selection module, a data acquisition module, a current damage determination module, a remaining life calculation module, a model training module, and an alarm module; the monitoring point selection module screens the monitoring point set to obtain multiple monitoring points; the data acquisition module acquires the current stress sequences at multiple monitoring points; the current damage determination module is used to process the current stress sequences to obtain the current damage of each monitoring point; the remaining life calculation module processes the current damage and historical damage through an RBF model to obtain the remaining life of the axle; the model training module is used to train a genetic algorithm and an RBF model, and the genetic algorithm and the RBF model are obtained through joint training; the alarm module is used to issue an alarm when the remaining life does not meet the safety threshold; so as to improve the accuracy of axle life prediction, more accurately obtain the life condition of the axle, and avoid traffic accidents.

A mobile object brokerage system includes a desired-remaining-life condition recognition section configured to recognize a desired-remaining-life condition of a user for a mobile object, a candidate-mobile-object remaining-life recognition section, a remaining-life consumption degree recognition section configured to recognize a remaining-life consumption degree on the basis of use history information about a mobile object by the user, a corrected-remaining-life calculation section configured to calculate a corrected remaining life obtained by correcting the remaining life of each of the plurality of candidate mobile objects with the remaining-life consumption degree, and a transaction target mobile-object selection section configured to select, as a target for a predetermined transaction with the user U, the candidate mobile object having the corrected remaining life that meets the desired-remaining-life condition.

The invention relates to the technical field of coal mine underground reservoir monitoring, in particular to a coal mine underground reservoir service life monitoring method and system. The method comprises the following steps: acquiring a water surface area of a coal mine underground reservoir, dividing the coal mine underground reservoir into a plurality of life prediction areas, and deploying data acquisition equipment for each life prediction area; historical data and current data of the life prediction area are obtained, and the life condition of the life prediction area is judged; integrating the service life conditions of the plurality of service life prediction areas, judging the service life grade of the coal mine underground reservoir, and outputting; the system comprises a regional data acquisition module, a regional life prediction module and a data integration judgment module. The coal mine underground reservoir is divided into a plurality of life prediction areas for the water surface area, life prediction is carried out on each life prediction area, then integration is carried out to obtain the coal mine underground reservoir life grade, and the accuracy of the coal mine underground reservoir life monitoring result is improved.

The invention relates to a method and device for determining the pipe wall thickness of an injection-production pipeline. The method comprises the steps that material parameters of the injection-production pipeline, working condition information of the injection-production pipeline under a target working condition and the alternative thickness of the pipe wall of the injection-production pipeline are obtained; according to the material parameters, the working condition information and the alternative thickness, the maximum equivalent stress of the injection-production pipeline under the target working condition is determined; if the maximum equivalent stress meets a preset stress condition, stress fatigue simulation is conducted on the injection-production pipeline based on the maximum equivalent stress, and the minimum fatigue life is obtained; and if the minimum fatigue life meets a preset life condition, determining the alternative thickness as a target thickness. On the basis of ensuring that the pipeline meets the mechanical property requirement and the design service life requirement under the target working condition, accurate, low-cost, safe and reliable selection of the pipe wall thickness of the injection-production pipeline can be achieved, excessive surplus of the pipeline performance is avoided, and the engineering cost is saved.

The invention relates to a refrigerator life acceleration experiment method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring basic experiment data sets of a plurality of target devices, and preprocessing each basic experiment data set to obtain an optimized experiment data set; classifying target equipment based on the optimization experiment data set to obtain a plurality of categories of the target equipment; and a preset number of experimental devices are extracted from all kinds of target devices, a life acceleration experiment is carried out on the experimental devices based on life acceleration experimental variables, and the life acceleration experimental variables at least comprise the experimental environment temperature and the refrigerator rotating speed. The service life condition of batch equipment can be comprehensively reflected, the experiment process is faster, and good timeliness is achieved.