40 results about "Life stage" patented technology

Described systems and methods may enable a cloud-computing system to use data model-based analysis to manage maintenance of an industrial automation system based on life stage predictions and real-time evaluations. Based on the data model-based analyses, the cloud-computing system may instruct an industrial control system of the industrial automation system to implement recommended adjustments and / or to confirm a part replacement occurred in situ. The recommended adjustment may include a part replacement. The cloud-computing system may generate instructions to cause shipment of the part to be installed as part of the part replacement to the industrial automation system.

Provided are a method for battery derating protection, an electronic device, and a storage medium. In the method, application stages of a to-be-tested battery are correspondingly adjusted based on decline of a state of health of the to-be-tested battery; when the to-be-tested battery is in a beginning of life stage or a middle of life stage, a state-of-charge usage interval of the to-be-tested battery is set to an initial usage interval; when the to-be-tested battery transitions from the middle of life stage to an end of life stage, the state-of-charge usage interval is narrowed to a limited usage interval; when the to-be-tested battery transitions from the safety of life stage to the hazard of life stage, the to-be-tested battery is forcibly discharged to the lower limit of the limited usage interval; after completing forced discharge, the state-of-charge usage interval of the to-be-tested battery is set to a single value.

The invention relates to a full-life-cycle indoor variable space building system. The full-life-cycle indoor variable space building system comprises a novel structure system and a building space layout. The novel structural system comprises a core tube, steel-concrete composite structural floors are connected to the two sides of the core tube, each steel-concrete composite structural floor is provided with a floor structural unit and a hidden beam frame for supporting the floor structural unit, the hidden beam frames and the floor structural units form an integrated structure by pouring concrete, and the hidden beam frames are connected with the core tube. The hidden beam frame is connected with a plurality of frame supporting columns, and the frame supporting columns are separated from the outer wall and the separating wall. The building space layout comprises the steps that a water utilization space is centrally arranged on one side of a living space, a large-space non-water utilization space is arranged on the other side of the living space, a vestibule backcourtyard and an equipment platform are arranged on the outer side of the outer wall of the centralized water utilization space, and a shared facility tube well is arranged. According to the invention, global variation of the living room space, the water utilization space and the balcony space in the whole life cycle of families and residences is realized.

The embodiment of the invention discloses a method and system for predicting the life stage of a vulnerability, a storage medium and electronic equipment, and the method comprises the steps: obtaining the basic information of a to-be-predicted vulnerability; according to the basic information, repairing parameters of the to-be-predicted vulnerabilities are calculated; based on a preset vulnerability life cycle prediction function, calculating a distribution probability value of a to-be-predicted vulnerability according to the basic information and the repair parameters; and determining the life stage of the to-be-predicted vulnerability according to the distribution probability value based on a preset mapping relationship between the life stage of the vulnerability and the distribution probability. According to the method and the system, the life cycle prediction of the vulnerability is associated with the time and the code library of the vulnerability, the probability density function for predicting the life stage of the vulnerability is constructed, and finally the life stage of the vulnerability is determined through the mapping relation between the life stage of the vulnerability and the distribution probability, so that the life stage of the vulnerability can be predicted more accurately, and the reliability of the vulnerability is improved. And thus, engineers can repair the vulnerabilities more pertinently.

The application discloses a batch generation and life cycle monitoring method for CFD test working conditions, belongs to the field of CFD test working condition generation and life cycle monitoring, and comprises a batch generation process of CFD test working conditions and a CFD test working condition life cycle monitoring process. The application is aimed at multiple CFD programs, constructs multiple sets of configuration templates according to different parameter files, and based on the configuration information of each set of templates, each type of parameter with variable quantity and value is combined and configured to batch generate test working conditions, so that the automation level and efficiency of complex test working condition configuration and generation are significantly improved. Meanwhile, the state of the working condition in each life stage and the operation state in each test task are set and tracked, an extensible rule set is applied to limit the test task, and the corresponding rule component can be adjusted in real time during the execution process of the test task, so that the normal operation of the test process is ensured, and the risk of data confusion is effectively avoided.

The application discloses a kind of electric power data management method and system, the method comprises: first, obtain historical data of power equipment, construct the mathematical model of historical data value and equipment life change;Then, according to the model, calculate the data value of different life stages, draw life change curve and mark key nodes and features;Curve is used as a reference, compared with real-time data, determine the warning level and data influence value;Combine multiple factors to generate electric power space situation estimation index;According to the index, determine the accuracy of the prediction node, analyze the curve fitting degree, and trace back the real cause of the equipment;The scheme quantifies the relationship between equipment data value and life by constructing a mathematical model, intuitively presents the trend, can timely warn life anomaly, comprehensively assesses the operation situation, traces back the failure cause, improves the management accuracy and efficiency, and ensures the stable operation of power system.

The invention provides a personnel health abnormity monitoring method, which comprises the following steps of: 1, dividing a day into six key life stage time periods, detecting the existence state of a check-in person at a time interval of 5 minutes, recording the existence state as 1, recording the absence state as 0, and counting the sum of the existence values in each time period; 2, carrying out standardization processing on existing values of all time periods, carrying out hierarchical clustering by adopting a Ward minimum variance method, and iteratively merging clusters until six typical life modes are generated; 3, constructing a deep feedforward neural network, and verifying the physiological rationality of a clustering result; and 4, estimating the change of the health condition according to the change trend of the life mode, and eliminating invalid data in combination with an empty room mark to realize health anomaly detection. And dynamic monitoring and anomaly detection of the health condition are realized by analyzing the life behavior mode of the facility check-in and combining a clustering algorithm and neural network verification. The invention further provides a system corresponding to the method.

The invention provides a bearing fault diagnosis and accurate operation and maintenance method based on a fault stage, and the method comprises the following steps: S1, calculating fault feature indications for various bearing faults according to vibration signals collected under a specific working condition; s2, determining a starting point of each bearing fault stage according to the fault feature indication; s3, fault diagnosis is carried out according to the current full-life stage of the bearing; and S4, based on the fault diagnosis in the step S3, formulating a maintenance, maintenance or thorough overhaul strategy based on the bearing fault stage. According to the method, the problem that a fault warning is sent out too early due to the fact that a fault stage is not considered in traditional threshold setting is solved, and waste caused by premature decommissioning of parts is avoided. The problem that a fault warning is sent out too late due to the fact that the fault stage is not considered in traditional threshold setting is solved, the situation that the warning is sent out in the failure stage is avoided, and therefore the situation that a whole machine system faces reliability and safety threats is avoided.

To provide a health care system for realizing a self-care function corresponding to the transition of a life stage.SOLUTION: A network system 100 includes: a user terminal 110 having a control device and an input / output device; and a server 130 having a control device and a storage device for storing association information between an age and a healthcare function to be provided, information, data and / or a self-care program necessary for realizing the healthcare function. An information processing terminal and the server are connected via a network. The control device of the terminal acquires personal data from a user and stores the data in the storage device. The control device of the server determines a healthcare function to be provided to the user using the personal data and the association information, provides the user with information, data, and a self-care program necessary for realizing the healthcare function, and periodically updates the healthcare function to be provided to the user using the personal data and the association information.SELECTED DRAWING: Figure 1

The present disclosure generally relates to the field of pest elimination including all life stages of bed bugs. The present disclosure includes articles, systems, and methods of heat treatment to target and kill pests.

The invention discloses a biological age assessment method and system based on full life cycle DNA methylation and application, and belongs to the technical field of bioinformatics. The method comprises the following steps: firstly, carrying out quality control and standardization treatment on obtained original DNA methylation data; then, screening differential methylation sites significantly related to calendar age as features; then, by taking calendar age as a target variable, constructing a regression model by adopting a LightGBM gradient lifting framework to obtain a methylated clock model; then, predicting the DNA methylation age of the individual by using the model, and calculating an epigenetic age acceleration value based on the deviation between the DNA methylation age and the calendar age; and finally, carrying out correlation analysis on the age acceleration value and health or physiological indexes of different life stages, so as to evaluate the biological aging state of the individual and predict related health risks. The method covers the whole life cycle, is suitable for Chinese population, and can provide an effective tool for clinical disease risk prediction, health management and anti-aging intervention effect evaluation.

Aquaculture system with a tank (1) for raising aquatic organisms, in particular fish (2), preferably saltwater fish, wherein the tank (1) is divided into at least two, preferably three, separate tank sections (4), wherein the aquatic organisms in the tank sections (4) are kept separately in groups according to different life stages, wherein the aquatic organisms of the individual groups are in the same life stage, wherein each tank section (4) is assigned a feeding device (5), wherein the quantity of feed to be added to each feeding device (5) is individually adjustable, wherein the quantity of feed is adjusted depending on a feed conversion ratio (FCR) of the aquatic organisms; wherein the tank (1) comprises at least one device for aeration with ambient air, preferably a side channel blower;wherein the ambient air is introduced into at least one basin section (4) via at least one air nozzle (9), wherein the at least one air nozzle (9) is arranged in the at least one basin section (4) such that a turbulent circular flow is created in the basin section (4), and wherein the circular flow inhibits sedimentation of particulate substances in the basin section (4).

The application discloses a kind of space rolling bearing life stage identification method based on CAHTL, utilize CAHTL network to carry out space rolling bearing life stage identification, CAHTL network includes pre-encoding network, heterogeneous transfer learning network and same domain general contrast learning network;Space rolling bearing life stage identification includes the following steps: S1, information compensation;S2, heterogeneous transfer learning network pretraining;S3, construct feature transfer loss function;S4, construct same domain general contrast learning loss function;S5, construct classification loss function;S6, construct the joint loss function of CAHTL, using random gradient descent and momentum asynchronous update mode will joint loss function training to convergence;S7, utilize CAHTL network to the sample to be measured of target domain classification.The application can utilize the known life stage training sample of small, non-equivalent, partial information defect of space rolling bearing historical working condition under current working condition to the sample to be measured and carry out higher accuracy life stage identification.

The invention provides a charging parameter adjustment method and device, a chip system and a program product, and relates to the technical field of communication. According to different life stages and / or different charging types and / or different charging stages of a life cycle, multiple sets of CV parameters are preset respectively, and each set of CV parameters comprises a temperature range, a charging voltage and a charging current corresponding to one life stage and / or one charging type and / or one charging stage. When the life stage, the charging type or the charging stage are changed, a set of CV parameters is determined, and then the battery is charged according to the charging current in the set of CV parameters. The multiple sets of CV parameters are respectively preset for different life stages, and / or different charging types, and / or different charging stages, so that the corresponding relation among the temperature, the charging current and the charging voltage is not unique, and the attenuation speed of the service life of the battery can be slowed down by dynamically adjusting the CV parameters.

A method is disclosed for directing targeted advertisements to potential multicultural consumers, who are a subset of members of an ethnic group associated with a homeland country and a native language. Information data associated with the members of the ethnic group is retrieved, which includes an age, a household size, a household income, and a number of visits to the homeland country for each of the members. A culture cycle model for the ethnic group representing a plurality of life stages of the members is generated, and then at least one of the life stages to which the potential consumers belong is identified. Based on that identified life stage, characteristics of the advertisements to be directed to the potential consumers are determined, including a language of the advertisements, and then the advertisements are directed to the potential consumers.

This invention relates to the field of data processing technology for predictive purposes, and discloses a method for adjusting the power generation performance of an energy system throughout its entire life cycle. The method determines the parameter boundaries of a wind-solar-storage power supply system, divides the entire life cycle planning period into multiple consecutive life stages, and determines the load demand input for the entire life cycle. It sets a baseline power generation performance at the initial commissioning stage, calculates the degradation maintenance level for each stage, establishes a monotonic mapping relationship between the degradation maintenance level and maintenance intensity, constructs a stage-dependent model for calculating available power generation capacity coupled with degradation and maintenance, and obtains available power supply capacity parameters for each stage of the entire life cycle. It generates a maintenance time series distribution through Monte Carlo simulation and constructs a set of life cycle power generation performance evolution scenarios. With the goal of minimizing the total cost throughout the entire life cycle, a mixed-integer linear programming model is constructed to obtain the optimal capacity configuration and adjust the power generation performance throughout the entire life cycle. This method solves the problems of ignoring degradation maintenance and inaccurate planning, achieving the goal of accurate and reliable life cycle data at a low cost.

The present application relates to the technical field of deep learning, in particular to a smart disconnecting switch life cycle defect early warning system. The system comprises a life event construction module, a life stage constraint module, a deep learning time sequence prediction module, a defect evolution constraint module and a hierarchical alarm module; the commissioning acceptance, opening and closing actions, operation inspection, maintenance defect elimination and historical alarm records are converted into life event sequences arranged according to time, the service stage, action load, maintenance recovery and defect correlation identifier are configured, the stage deviation features are generated based on deep learning, and the defect stage results are generated in combination with the evolution correlation between mechanism jamming, contact contact abnormality, conductive loop overheating, insulation support abnormality and opening and closing misplacement. The present application makes the alarm judgment subject to the life stage, time sequence deviation and defect evolution constraint at the same time, can distinguish normal stage fluctuations, maintenance recovery process and recurrence process after defect elimination, and improves the continuity of implicit defect precursor warning and the stability of alarm level.

This invention relates to a method for collaborative carbon-health accounting throughout the entire lifecycle of power modules, comprising: S1: collecting data from power modules and performing cross-stage data association and alignment based on digital twin identifiers; S2: determining the electricity carbon emission factor; calculating dynamic operating carbon emissions based on the carbon emission factor and real-time power loss; S3: calculating carbon emissions during manufacturing, use, and end-of-life stages, and normalizing them into a product-level full lifecycle carbon footprint value; S4: randomly sampling the remaining lifespan and future green electricity penetration rate using a random simulation method, calculating the probability distribution of net carbon gains from tiered utilization, and outputting retirement decision recommendations; S5: calculating cryptographic hash values, digitally signing key data packets, and generating zero-knowledge proofs. This method achieves seamless data integration across the entire manufacturing-operation-retirement chain, collaborative dynamic accounting of carbon emissions and health status, repeatable calibration of thermal resistance aging parameters, probabilistic quantification of retirement decisions, and reliable anchoring of data sources.

We provide the system. [Solution] Means of collecting user activity data, A means of analyzing the user's lifestyle based on the aforementioned activity data, A means of generating suggestions tailored to the user's cultural background and life stage, A means for notifying the user terminal of the aforementioned proposal, A means of receiving user feedback and continuously learning, A system that includes this.

The application provides a vulnerability management method and device, a storage medium and a computer device, wherein the method comprises: obtaining and storing asset vulnerability information corresponding to a target organization; for each vulnerability data, determining a target stage currently occupied by the vulnerability data from each life stage included in a preset vulnerability life cycle; according to the target stage, marking a state of the vulnerability data and interacting with a target person having a target agency authority matched with the target stage; generating interaction description information according to an interaction result, and determining a new target stage currently occupied by the asset vulnerability from the each life stage according to a target stage corresponding to a current state mark; the interaction description information is used for feedback to a corresponding target person in the new target life stage; returning to the step of marking a state of the vulnerability data according to the target stage until the stored vulnerability data is cleared.

To provide a health care system and a database system for providing a self-care function corresponding to the transition of a life stage.SOLUTION: A health care system 100 includes: an information processing terminal 110 of a user having a terminal control device and an input / output device; and a server 130 having a server control device and a storage device for storing first association information between an age and a life stage, second association information with a healthcare function to be provided in the life stage, information, data and / or a program necessary for realizing the healthcare function. The server control device determines a life stage of the user using personal data and the first association information, determines a healthcare function to be provided to the user from the life stage and the second association information, provides the healthcare function to the user, and periodically updates the healthcare function to be provided to the user using the personal data and the first association information.SELECTED DRAWING: Figure 1

The invention discloses a batch verification maximization fault diagnosis system and method for life stage migration. The dependence on target domain labeling is remarkably reduced while cross-stage high-precision fault recognition is achieved. The system comprises a life stage pseudo-domain division label generation module which inputs source domain and target domain test data containing timestamps or residual service life information and outputs data subsets corresponding to all stages; the stage perception sharing-specific feature coding module is used for inputting original signal data of each stage and a corresponding stage label, and outputting a shared feature and a stage specific feature; the channel-stage fusion selection module is used for receiving the shared features and the stage specific features from the stage perception sharing-specific feature coding module and outputting fused features; and the multi-stage batch nuclear norm maximization migration module is used for inputting fusion features and stage label information, respectively calculating a Softmax output matrix for each pseudo-domain, and maximizing the batch nuclear norm of the Softmax output matrix so as to enhance cross-stage discrimination and feature consistency.

The invention discloses an intelligent valve monitoring system based on industrial vision, which comprises a multispectral imaging module which comprises a visible light sensor, a thermal infrared sensor and a short wave infrared sensor, synchronously acquires three-dimensional shape data of a valve at a preset sampling rate, processes light velocity, modulation frequency and phase difference calculation axial resolution through a three-dimensional reconstruction method, and obtains a three-dimensional shape data of the valve; ambient light and mechanical vibration interference are inhibited; the corrosion evaluation module is used for calculating a corrosion index based on the multispectral reflectivity difference value and the characteristic spectrum peak intensity, judging the corrosion grade according to a preset threshold value and triggering a maintenance instruction; and the predictive maintenance module is used for dynamically correcting the residual life prediction model through the multi-source characteristic parameters, dividing life stages and adjusting a monitoring strategy. According to the invention, the blank of multi-dimensional perception, dynamic prediction and autonomous optimization in the field of industrial vision and intelligent monitoring is filled, and better intelligent valve monitoring is realized.

The techniques described herein relate to systems and methods for generating deliberative committees of age-stratified large language models (LLMs). An example method for generating a dialogue between at least first and second age-stratified LLMs trained to generate simulated responses to questions that respective first / second persons would have provided in a particular life stage comprises receiving a prompt comprising a question, executing the first LLM to generate a first simulated response to the question that the first person would have provided in the first life stage, executing, using the prompt and / or the first simulated response, the second LLM to generate a second simulated response to at least the question that the second person would have provided in the second life stage, and generating, using the response(s), an output representing a third simulated response to the question that the first / second persons would have collectively determined to provide in their particular life stages.

The application discloses a biological age evaluation method and system based on whole life cycle DNA methylation and application, and belongs to the technical field of bioinformatics. First, the obtained original DNA methylation data is subjected to quality control and standardization processing; then, differential methylation sites significantly related to calendar age are screened as features; next, taking calendar age as a target variable, a regression model is constructed by adopting a LightGBM gradient boosting framework to obtain a methylation clock model; subsequently, the model is used to predict the DNA methylation age of an individual, and an epigenetic age acceleration value is calculated based on the deviation of the DNA methylation age from the calendar age; finally, the age acceleration value is subjected to correlation analysis with health or physiological indexes in different life stages, so as to evaluate the biological aging state of the individual and predict related health risks. The application covers the whole life cycle, is suitable for Chinese population, and can provide an effective tool for clinical disease risk prediction, health management and anti-aging intervention effect evaluation.

Techniques for evaluating the effectiveness of an intervention for a condition of a plurality of subjects are disclosed. First data can be received from a plurality of activity monitors respectively associated with the plurality of subjects. Each of the activity monitors can measure at least some activity by a respective plurality of subjects over a time period. For each of the plurality of subjects, second data can be received that identifies one or more indicators of a life stage or a quality of life condition of each of the plurality of subjects receiving a therapy, nutrition, or intervention over at least some portion of the time period. The first data and the second data can be input into at least one machine learning (ML) model that can process the first data and the second data. The ML model can determine that the intervention is relatively effective, or that the intervention is relatively ineffective.

The invention belongs to the field of power supply aging detection, relates to a data analysis technology, is used for solving the problem that in the prior art, the optimal analysis period duration cannot be allocated to a power supply at different life stages of the power supply, and particularly relates to an operation aging detection system adaptive to a communication power supply, which comprises an aging detection platform, the aging detection platform is in communication connection with an operation detection module, a service life evaluation module, a detection optimization module, an optimization verification module and a database; according to the invention, accurate monitoring of the operation state of the communication power supply and accurate evaluation of the aging trend are realized. The randomized detection period duration breaks through the limitation of a fixed mode, so that the system can adapt to the aging characteristics of different life stages of the power supply, the accuracy of aging risk assessment is improved by a multi-dimensional parameter weighted analysis mechanism, and the accuracy of aging risk assessment is improved based on life stage division and pairing interval marking of historical data. And a basis is provided for dynamic optimization of a detection strategy.

The invention relates to the technical field of machine learning, and discloses a mold life evaluation method based on machine learning, which comprises the following steps: collecting mold data, and extracting features related to a mold physical failure mode; self-adaptive decomposition is carried out, decomposition parameters are adjusted, and signal components are obtained; constructing an integrated model to obtain a residual service life prediction value of the mold; analyzing the feature contribution degree, optimizing a model input feature set, and outputting a maintenance decision suggestion based on an analysis result; and deploying the integrated model to an edge computing device, and adjusting an evaluation strategy according to degradation characteristics of the mold in different life cycles to realize real-time life evaluation and decision support. According to the method, the accuracy and reliability of mold life prediction can be remarkably improved, the model transparency is enhanced, and closed-loop management from prediction to maintenance decision is realized.

The system according to the embodiment aims to provide comprehensive support to single women to help them cope with various life stages and challenges. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides various types of support based on the analysis results obtained by the analysis unit.

This invention discloses a method and system for predicting the aging trend of a battery across its life stages. The method includes: S1, acquiring early aging characteristic data of the first life stage of the battery to be predicted and corresponding load condition information; S2, constructing a condition-aware model based on a cross-attention mechanism, using battery data from no-load switching as the main input and corresponding load condition information as the condition input to train the condition-aware model; S3, using the condition-aware model to output the evolution sequence of subsequent aging characteristics of the battery to be predicted from the early stage of the first life stage in a time-series prediction manner, the evolution sequence of subsequent aging characteristics covering the remaining lifespan of the first life stage and the aging characteristic changes of the second life stage after load condition switching; S4, estimating the battery health state based on the evolution sequence of subsequent aging characteristics to generate a cross-life stage aging trajectory prediction curve from the first life stage to the second life stage, thereby improving the robustness and practicality of the prediction.