24 results about "Stress factor" patented technology

The invention provides a coastal wetland ecological restoration system construction method based on a native function, and the method comprises the steps: carrying out the comprehensive evaluation W of an ecological condition and the dynamic prediction of a stress factor f through employing an analytic hierarchy process and a machine learning model in combination with the historical data, the ecological current situation and the real-time monitoring data of an ecological system in a restoration region; determining a degradation degree and a restoration target according to the ecological condition comprehensive evaluation W and the stress factor f; performing repair layout on the repair target, wherein the repair layout comprises corresponding engineering measures and management measures; and an ecological monitoring system and an evaluation index system are established. The method has the following advantages that comprehensive evaluation of the ecological condition of the coastal wetland is achieved through the analytic hierarchy process, restoration measures and management measures are effectively taken, and ecological restoration of wetland native function remodeling is ensured; a perfect system construction method is provided for ecological restoration of the coastal wetland, and important social, economic and environmental benefits are achieved.

The invention relates to the field of data processing, in particular to an MEMS gyroscope temperature drift compensation method based on a neural network, which comprises the following steps: firstly, acquiring data through a multi-source sensor, and calculating a composite disturbance index fused with a mechanical agonist factor and a thermal stress factor so as to evaluate whether a system state deviates from a routine or not in real time; and then, the dynamic confidence of the LSTM model is calculated by using the index. And finally, according to the confidence coefficient, carrying out adaptive weighted fusion on a high-precision LSTM model prediction value and a high-robustness baseline polynomial model compensation value. According to the method, the compensation precision is guaranteed under the normal working condition, smooth transition to the safe baseline model is achieved under the abnormal working condition, and the overall reliability and safety of the compensation system are remarkably improved.

The invention discloses a new energy equipment part aging prediction method and system based on digital twinning and multi-source data fusion, and relates to the technical field of digital twinning and intelligent prediction of new energy equipment, and the method comprises the steps: building a digital twinning model, and carrying out the working condition label labeling of multi-source monitoring data according to the current operation working condition of the equipment; extracting working condition aging characteristic parameters, calculating change gradients and working condition switching stress factors, and storing the change gradients and the working condition switching stress factors into a working condition associated aging characteristic library; inputting the working condition aging characteristic parameters and the working condition switching stress factors into a cross-working condition aging accumulation model, and outputting an aging accumulation index; and taking the aging accumulation index as a simulation initial state, simulating an attenuation trajectory of a component performance parameter under a preset future working condition sequence, when the performance parameter reaches a preset failure threshold value for the first time, extracting a running time length, and taking a difference value between the running time length and a current actual running time length as a component aging prediction remaining life. According to the invention, prospective residual life prediction based on the current aging state is realized.

To provide a route search device capable of promoting the use of a road section where a user easily feels stress.SOLUTION: Based on the data of the plurality of drivers, the driver search unit 20 searches the plurality of drivers for a similar driver who feels stress from the stressor in a manner similar to that of the target driver. Subsequently, the allowable range estimation unit 21 estimates the allowable range of the target driver for the characteristic value of the stressor based on the stress data indicating the degree S of the stress felt by the similar driver during traveling in the road section for each characteristic value of the stressor existing in the road section traveled by the searched similar driver in the past. Subsequently, based on the estimated allowable range, the route search unit 22 searches for a travel route of the vehicle C e such that a road section in which the characteristic value of the stress factor is within the allowable range is traveled preferentially over a road section outside the allowable range.SELECTED DRAWING: Figure 1

The present application relates to the technical field of health monitoring, in particular to a cancer survivor work pressure monitoring system based on big data, which comprises a physiological parameter analysis module, a stress cause analysis module, a work load distribution module and an abnormal stress monitoring module. In the present application, the sensitivity and accuracy of data analysis are improved through the difference analysis of heart rate fluctuation, skin conductance and blood pressure range, the data points with large fluctuations are screened and classified, the efficiency of abnormal data capture and classification is optimized, the stress cause identification accuracy and distribution law insight are improved, the key stress factors are determined through sensitivity sorting, the stress evaluation is more scientific and quantitative, the priority dynamic adjustment is realized in load classification and grouping, the work pressure distribution is optimized, the abnormal stress point monitoring and grading judgment are strengthened, the stress abnormality grade fine monitoring is realized, the data quantification and dynamic monitoring capability are enhanced, the stress management efficiency is improved, and the targeted data support is provided for personalized health intervention.

The application discloses a mouse vitiligo induction model based on chronic stress factors, wherein different kinds of stress factors capable of triggering stress response of animals are applied to stimulate the mouse periodically within a certain time period, so as to prevent the mouse from producing habitual response to the stress source and to promote the mouse to become white and the tail epidermis to be flaky. The technical method is different from the existing vitiligo model method, is based on the mechanism of occurrence and development of vitiligo observed in the clinic, and promotes the mouse to truly develop the vitiligo phenomenon from the whole instead of simulating the disease evolution locally. Compared with the existing vitiligo promoting mechanism, the effect is more stable and the authenticity is higher. The technical method is based on the objective fact that the clinical vitiligo patients often combine anxiety and depression to design the stress factors, and considers that the stress factors and the occurrence and development of vitiligo may be inter-caused and viciously circulate, namely, the adverse life events as the stress factors cause the damage and dysfunction of the melanin synthesis system at the micro level and form the vitiligo change at the macro level.

The invention belongs to the field of aviation reliability tests, and particularly relates to an aviation generator product reliability strengthening test method. Comprising the following steps: analyzing a fault mode, a fault mechanism and a load of an aero-generator caused by a lowest agreed level, and determining main sensitive stress factors of an aero-generator product; according to the determined sensitive stress factor, a reliability strengthening test item is determined according to the working characteristics of the generator, and the vibration stepping stress test is an environmental stress strengthening test; the acceleration and deceleration test is a working stress strengthening test; the heating test is a working stress strengthening test; carrying out test parameter and profile design for each test item, carrying out a vibration stepping stress test, adopting a product working environment vibration spectrum pattern, and taking an environment vibration test requirement as an initial condition; acceleration and deceleration test: adopting acceleration and deceleration indexes required by normal work as initial conditions, and performing the test step by step; and heating test: taking the inlet oil temperature during normal work as an initial condition, and performing the test step by step.

The invention discloses a biomarker combination based on pollutant exposure detection and application thereof, and relates to the technical field of biomarkers, the biomarker combination comprises an integrin subunit itg-alpha11, a nuclear transcription factor nf-kb, and an antioxidant stress factor nrf2; according to the biomarker combination disclosed by the invention, a complete association network from molecular response to phenotypic risk is constructed by accurately capturing an action mechanism of BDE-47 at molecular and cellular levels; the rapid diagnosis of pollutant exposure can be realized in the early stage of embryonic development, and the risk of development malformation and the like can be effectively early warned; and meanwhile, a quantitative basis is provided for evaluating the pollution remediation effect, and accurate formulation of an environment quality standard and optimization of an aquaculture management strategy are supported. The biomarker combination not only deepens the theoretical cognition of a pollutant toxicity mechanism, but also provides an innovative tool for environmental health risk assessment and ecological protection practice, and has important theoretical significance and wide application prospects.

The invention belongs to the technical field of storage management of biological fermentation products, and particularly relates to a temperature and humidity self-adaptive control method for prolonging the storage time of a microbial agent. The method comprises the following steps: acquiring real-time environmental parameters of a warehouse, and calculating an environmental fluctuation stress factor according to the deviation between the environmental parameters and a historical mean value; constructing a fluctuation correction type microbial inactivation rate model, introducing an environmental fluctuation stress factor into a kinetic equation to calculate an instantaneous inactivation rate, and performing integration to obtain an actual accumulated damage index; and dynamically calculating a temperature set value according to the proportional relation between the actual damage and the theoretical expected damage, and issuing the temperature set value to the temperature control equipment for adjustment after safety boundary verification. According to the method, the recessive accumulated damage of the environmental fluctuation to the microorganisms is calculated, so that the environmental self-adaptive adjustment based on the life cycle is realized, and the storage energy consumption is effectively reduced while the activity retention rate of the microbial agent is guaranteed.

The invention relates to a ship coating environmental adaptability modeling and evaluating method under the action of temperature-salinity-irradiation, which comprises the following steps of: firstly, obtaining coating degradation data through a temperature-salinity-irradiation multi-factor orthogonal test, secondly, constructing a coating electrochemical performance degradation model, and further, establishing two models for environmental related parameters, and performing parameter estimation by using a maximum likelihood estimation method. And finally, calculating the environment adaptive service life of the ship coating through the established model, and comparing the influence degrees of different stress factors on the service life of the coating. The method comprises the following specific steps: 1, carrying out orthogonal test design on three factors of temperature, salinity and irradiation; 2, constructing an electrochemical performance degradation model of the ship coating; 3, environment related parameter modeling and maximum likelihood estimation; and 4, ship coating environment adaptation life calculation and stress influence analysis. The method provided by the invention is suitable for the fields of environmental adaptability evaluation of the ship equipment coating in a complex environment and the like, and has relatively high operability.

The subject matter of the present application is a composition, especially cosmetic and / or dermatological composition comprising a combination of a roselle extract and a betaine compound, as well as a non-therapeutic cosmetic treatment process of the skin to fight against the signs of a stress factor-induced osmolarity dysregulation of the epidermis, such as the appearance of fine lines and wrinkles, the withering, the thinning, the dryness, the looseness, the dull appearance, or the heterogeneous surface, of the skin.

The invention provides an interactive cognitive ability evaluation method for unmanned driving. The method comprises the following steps: constructing a layered scene library; designing a pressure factor, injecting the pressure factor into each scene, and carrying out scene testing; in the scene testing process, testing data are collected, and features are extracted; calculating a cognitive entropy-interaction entropy; and carrying out comprehensive scoring and grade authentication. According to the interactive cognitive ability evaluation method oriented to unmanned driving, the cognitive and interactive process of an unmanned driving system can be efficient and predictable, the uncertainty in the decision-making and interactive process of the unmanned driving system is quantified through entropy in the information theory, a pressure test scene is designed and injected, the change of the entropy value is evaluated, and the evaluation accuracy of the interactive cognitive ability of the unmanned driving system is improved. Therefore, the comprehensive ability score is obtained.

This invention provides a method, apparatus, computer equipment, and readable storage medium for predicting slope welding stress. It establishes a database containing slope factors, welding process factors, and asymmetric stress factors through thermo-mechanical coupled numerical simulation and conducts experimental verification. After data preprocessing, an LSTM-ResNet-Diffusion hybrid model is constructed and trained. The model predicts asymmetric stress factors, and SHAP analysis clarifies the correlation patterns and critical thresholds of these factors. This invention offers high prediction accuracy and can precisely reveal the correlation mechanism between various influencing factors and asymmetric stress, providing a scientific basis for optimizing slope pipe welding processes.

The invention belongs to the technical field of yarn performance detection, and discloses a vortex spinning yarn tensile property detection method which comprises the following steps: cutting out a main detection sample, pre-stretching the main detection sample, and collecting an initial stress value; stretching the main detection sample to generate a stress change curve, when the sample is detected to be stretched to preset strain, collecting continuous stress data in the relaxation process, continuing stretching until the sample is fractured, and recording the representative fracture strength; identifying a stress stable section, extracting stress characteristic parameters, and obtaining breaking strength through coupling correction; calculating stress relaxation characteristic parameters based on the continuous stress data; preparing parallel detection samples, repeatedly stretching the parallel detection samples, and calculating a strength uniformity index according to the numerical value groups of the parallel detection samples; correlation integration is carried out through a detection evaluation system, and a comprehensive detection report is formed. According to the method, detection errors caused by non-stress factors are effectively offset, and the corrected breaking strength obtained through calculation is closer to the real mechanical property of the yarn.

The application provides a chip performance degradation model construction method and device under accelerated aging test, comprising: constructing a candidate test condition space containing temperature, voltage, workload, test duration, idle holding time, read-write ratio and queue depth, performing initial accelerated aging test and collecting performance data, reliability data and telemetry data, and then training a chip performance degradation model capable of outputting prediction uncertainty. According to the information gain, degradation mode distinction degree, test cost, sample damage cost and constraint condition of the candidate test condition, the next round of test condition is generated, and the model is updated after multiple rounds of test and the degradation analysis result is output. The application can improve the test effectiveness under limited chip samples, and is used for distinguishing the influence of different stress factors on chip performance degradation.

The present application relates to the field of data processing, and more particularly to a MEMS gyroscope temperature drift compensation method based on neural network, first, data is acquired through multi-source sensors, a composite disturbance index fused with mechanical excitation factor and thermal stress factor is calculated to evaluate whether the system state deviates from the normal in real time. Then, the dynamic confidence of the LSTM model is calculated using the index. Finally, the high-precision LSTM model prediction value and the high-robustness baseline polynomial model compensation value are adaptively weighted and fused according to the confidence. The present application ensures the compensation accuracy under normal working conditions, and smoothly transitions to the safe baseline model under abnormal working conditions, significantly improving the overall reliability and safety of the compensation system.

The application provides a product low-cost reliability test profile design method, which comprises the following steps: analyzing stress factors and total time of a specified single-cycle reliability test profile T and further comprises the following steps: decomposing the single-cycle reliability test profile into stress level combinations of different levels of stress factors; determining stress combination time without vibration, normal temperature and normal humidity stress and stress combination time with abnormal temperature and humidity stress or vibration; designing a low-cost reliability test profile containing test profiles outside and inside a test box; and decomposing stress combinations according to the designed low-cost reliability test profile containing test profiles outside and inside the test box. In the case of meeting the unchanged cumulative test stress experienced by the electromechanical equipment, the reliability test is split into comprehensive stress test bench test and non-comprehensive stress test bench test, and under the condition of meeting the consistent product reliability verification, the reliability test cost is greatly reduced.

The invention relates to the technical field of data processing, and discloses a high-voltage fuse state data feature extraction method and system, and the method comprises the steps: collecting the operation current data, environment data and insulator leakage current data of a target object in a monitoring period in real time; operation current data, environment data and insulator leakage current data are collected, a medium environment stress factor is obtained through preprocessing analysis, a stress modulation current track is generated, singular fluctuation entropy and dispersion offset are extracted, and a comprehensive degradation potential index and a health state identifier are obtained through cooperative calculation. Dispersive random weak characteristics such as leakage current micro-amplitude fluctuation and local temperature slow-rate rise can be accurately captured, judgment of early insulation recession and contact degradation of the target object is realized, monitoring accuracy is improved, and stable operation of the target object in coastal salt mist and rainwater corrosion environments is guaranteed.

The application discloses the technical field of yarn performance detection, and discloses a vortex spinning yarn tensile property detection method, which comprises the following steps: intercepting a main detection sample, pre-tensioning the main detection sample, and collecting an initial stress value; stretching the main detection sample, generating a stress change curve, collecting continuous stress data of a relaxation process when the sample is detected to be stretched to a preset strain, continuing to stretch to sample fracture, and recording the breaking strength; identifying a stress stable section, extracting stress characteristic parameters, and obtaining the breaking strength through coupling correction; calculating stress relaxation characteristic parameters based on the continuous stress data; preparing parallel detection samples, repeating the stretching process for each parallel detection sample, calculating the strength uniformity index through the numerical group of the parallel detection samples; and correlating and integrating through a detection evaluation system to form a comprehensive detection report. The application effectively offsets the detection error caused by non-stress factors, so that the corrected breaking strength obtained by calculation is closer to the real mechanical properties of the yarn.

The invention provides a reservoir circulating water industrial aquaculture system control method combining the Internet of Things and AI, and relates to the technical field of control, and the method comprises the steps: collecting water body parameters through a distributed sensing unit to construct space-time state representation data, recognizing a stress factor distribution position and a metabolic load accumulation characteristic, calculating the environment regulation and control demand quantity of each region, and calculating the environmental regulation and control demand quantity of each region. The spatial response priority and the time sequence matching relation of the functional units are determined, cooperative work parameters are optimized by applying a reinforcement learning model, and the accuracy and the intelligent level of aquaculture water environment regulation and control are improved with the growth rate, the health level and the water stability of aquaculture objects as targets.

This invention relates to the field of agricultural breeding technology, and particularly to a method for predicting and analyzing egg production rate in laying hens and a feed additive. The method includes: extracting heat stress factors and ammonia stress factors based on environmental data within an analysis window, thereby determining environmental stress measures; determining dietary abnormality measures based on cumulative feed intake within a monitoring period; determining production performance deviations based on egg production rate and hen weight within the monitoring period; integrating environmental stress measures, dietary abnormality measures, and production performance deviations to determine a comprehensive stress index, and predicting egg production rate based on the comprehensive stress index; and determining disease risk factors based on disease incidence and immunization records, thereby updating the predicted egg production rate. This invention achieves accurate short-term prediction and early warning of egg production rate in laying hens by quantifying the comprehensive stress index through multi-source data fusion.

The application belongs to the technical field of electric power engineering, and discloses a cross-linked polyethylene-silicon rubber interface contact characteristic analysis method, which equates the cross-linked polyethylene-silicon rubber interface contact to the contact of a rough surface and an ideal smooth surface; a random rough surface is constructed by using a spatial frequency synthesis method, and the Ogden model is used to describe the super-elastic stress-strain relationship of the silicon rubber structure to obtain the Ogden model parameters of the random rough surface; a finite element method is used to construct a cross-linked polyethylene-silicon rubber interface contact characteristic analysis model to obtain the contact topography and stress distribution calculation results of the cross-linked polyethylene-silicon rubber insulation interface under different pressure and surface topography conditions. The application can more accurately and effectively analyze the influence law of the material surface roughness, the interface pressure factor, the mesoscale cavity size and the local stress distribution of the interface, and realizes the quantitative analysis of the internal cavity and the local stress distribution of different interface contact conditions under the mesoscale.

This invention relates to a method and device for exercise intervention to improve nonylphenol-induced learning and memory impairment. By constructing an animal treadmill exercise model, experimental mice were randomly grouped and subjected to exercise interventions of different intensities. The effects of exercise on behavioral performance and biological indicators were systematically evaluated. Results showed that low-intensity and moderate-intensity exercise significantly improved the learning and memory abilities of mice. Metabolic and molecular biological analyses revealed that moderate exercise effectively restored NAD⁺ levels and the activity of related energy metabolism pathways in the hippocampus, and regulated the expression of key molecules such as AMPK and BDNF, thereby improving impaired neuroplasticity. This method optimizes exercise intensity to achieve synergistic regulation of neural function and metabolic networks, avoiding interference from traditional stress factors and improving the reliability of the data. This invention provides a safe and effective technical solution for intervention research in environmental toxicology and neurodegenerative damage, and has promising application prospects.

The application discloses a kind of based on 5G communication's wisdom mine supervision system and its supervision method, including following steps: system is collected environmental parameter, equipment state and personnel behavior data by the sensing node deployed in mine area, real-time evaluation high-risk area is calculated using stress factor, and dynamically adjust the acquisition frequency, range and collaborative mode of sensing network, by generating risk distribution map, system predicts the change trend of high-risk area, and based on gradient change real-time allocation resource, optimize the task allocation and coverage range of sensing node, system supports dynamic setting early warning threshold, when stress factor reaches threshold value, trigger emergency response, generate equipment operation mode adjustment, personnel evacuation path optimization and task reassignment instruction, emergency instruction is distributed in real time through 5G network, combine feedback data and historical data and constantly optimize node allocation and risk prediction model, realize the wisdom, real-time and high efficiency of mine supervision.