154 results about "Stress index" patented technology

The invention provides a deep coal seam roadway surrounding rock stability intelligent early warning method, and relates to the technical field of coal mine dynamic disaster prevention and control. According to the intelligent early warning method for the stability of the surrounding rock of the deep coal seam roadway, a basic sample library is constructed through a dynamic and static combined loading rock test; synchronously collecting field surrounding rock static stress and micro-seismic data and carrying out time sequence polymerization; constructing an equivalent dynamic and static combined stress index based on a dynamic and static load superposition principle, and forming a seismic-force coupling feature vector; establishing an LSTM early warning model, adopting a transfer learning strategy, pre-training by using laboratory data, and then performing fine tuning by using field data; inputting the real-time feature vector into a model and outputting an instability probability; and verifying and correcting the probability based on actual deformation data of the surrounding rock, and applying a missing report sample to online incremental updating of the model. According to the method, the dynamic and static load coupling effect is quantified, the difficulty of small sample training is overcome, and accurate and self-adaptive early warning of the deep roadway surrounding rock instability risk is realized through a physical feedback closed-loop correction mechanism.

The invention discloses a feed additive ratio control method and system based on environmental perception. The method comprises the steps that breeding environment, feed characteristics, equipment operation and animal physiological parameters are collected, and a high-quality parameter set is generated after preprocessing; calculating an environment stress index based on a multi-factor data fusion algorithm and analyzing a change trend; combining historical and current data, using time sequence prediction and a machine learning algorithm to predict additive requirements and generating an optimal proportioning strategy; a control instruction is generated according to the strategy, and the additive conveying and spraying process is regulated and controlled; and a matching strategy and control parameters are corrected in real time through closed-loop feedback, so that accurate regulation and control are realized. The device can dynamically adapt to environmental changes, reduce animal stress, and improve mixing uniformity and nutrition retention effect.

The invention discloses a mental stress assessment and intelligent dredging method and system, and belongs to the technical field of digital medical treatment and health informatics. Multi-modal physiological signals are continuously collected through the wearable device, medical diagnosis level pressure state evaluation is carried out based on the personalized physiological baseline, and personalized pressure indexes and levels are generated; secondly, when it is diagnosed that the pressure level exceeds the standard, the system serves as an intelligent decision support system, the environment and schedule information after privacy protection processing is fused, and an optimal grooming action is dynamically selected from a predefined intervention action library and executed; finally, the system serves as a continuous learning system, the intervention efficiency is evaluated in real time according to feedback data of the pressure index after execution, the decision model is updated, and collaborative self-optimization of the diagnosis strategy and the intervention strategy is achieved. According to the method, medical diagnosis, personalized treatment decision and adaptive learning are integrated, and the systematicness, accuracy and intelligent level of mental stress related health problem management are improved.

A method for diagnosing regional moisture conditions based on evaporative stress oscillation patterns is presented. [Solution] The method includes the following steps: Step 1: acquiring data; Step 2: calculating the time series of the evaporative stress index; Step 3: calculating the evolution rate of the evaporative stress index; Step 4: determining the oscillation patterns of the evaporative stress index; Step 5: calculating the frequency or duration of each oscillation pattern; and Step 6: diagnosing the evaporative stress-driven pattern and regional moisture status. The method of the present invention effectively solves the problem of information leakage and failure to reflect the transition mechanism of the stress state due to smoothing of the long-term series in the process of diagnosing the current regional moisture status, and provides solid theoretical and technical support for the diagnosis and evaluation of the evaporative stress-driven pattern and regional moisture status.

The invention relates to the technical field of stress testing, in particular to a VCSEL epitaxial wafer stress distribution monitoring method and system. The method comprises the following steps: acquiring a PL spectrum of each measurement point and a peak wavelength of the PL spectrum, and decomposing the PL spectrum into polarization components along a first crystal orientation and a second crystal orientation; obtaining the initial polarization orthogonality of the measurement point based on the peak intensity of the polarization component; obtaining a reference wavelength of the VCSEL epitaxial wafer, and calculating a thermally induced wavelength drift factor of a measurement point; based on the thermally induced wavelength drift factor and a set maximum saturation value, thermally induced polarization artifacts of the measurement points are calculated, the thermally induced polarization artifacts are in positive correlation with the thermally induced wavelength drift factor, and the maximum saturation value is taken as an upper limit; subtracting the thermally induced polarization artifact from the initial polarization orthogonality to obtain a pure stress index; the pure stress indexes are mapped to the corresponding coordinates, the stress distribution diagram of the VCSEL epitaxial wafer is obtained, and the accuracy of the stress distribution monitoring result of the VCSEL epitaxial wafer is effectively improved.

The invention relates to a cascade reservoir flood control capacity collaborative optimization method and system based on a dynamic game, and belongs to the technical field of hydropower station group flood control regulation and storage. Comprising the following steps: collecting a historical water level sequence, upstream flood discharge flow, drainage basin rainfall confluence data, radar flow measurement data, dam body stress indexes and dynamic correlation parameters of a cascade reservoir group, inputting the collected data into a space-time risk field model, and constructing a space-time risk field; constructing a game model based on the space-time risk field, taking an upstream reservoir as a leader to formulate a reservoir capacity allocation strategy, taking a downstream reservoir as a follower to dynamically adjust a flood discharge scheme according to the leader strategy, and coordinating a flood control target and an economic target through a time-varying game weight; solving the game model by adopting an improved distributed ADMM optimization algorithm to generate a scheduling instruction, and verifying the legality of the scheduling instruction through a block chain smart contract; and pre-executing the scheduling instruction in the digital twin simulation platform, dynamically calibrating the mapping parameters of the physical reservoir and the virtual mirror image through reinforcement learning, generating a final scheduling instruction, and issuing and executing the scheduling instruction.

A method for assessing a state of stress of an individual when the individual is contacting a receiving surface of a device comprising a pressure sensor comprising a plurality of elemental gauges, each elemental gauge having a gauge factor of at least 10, the method comprising acquiring and digitizing each signal delivered by each elemental gauges of the pressure sensor each signal comprising a pseudo-periodic part, exploring the signal and detecting patterns corresponding to heartbeats, measuring an Inter-Beat interval and recording in a heartbeat timestamped file each Inter-Beat Interval, from the heartbeat timestamped file computing at least 3 stress indices and categorizing the state of stress of the individual based on the at least three indices and a stress assessment program implementing a trained algorithm, among an increasing stress, a decreasing stress and a neutral state.

The invention provides a post evaluation simulation method based on virtual reality, and relates to the technical field of virtual reality, and the method comprises the steps: calculating a real-time cognitive load pressure index and an emotion state parameter of a student; obtaining a fault evolution speed parameter and a complexity parameter used for adjusting the fault evolution process; inputting the fault evolution speed parameter and the complexity parameter into an intelligent fault chain generation system, and generating a cascade fault situation conforming to the physical law of the power system through a graph neural network model; in a high-precision simulation environment of a digital twin platform of a power system, students are guided to execute simulation operation based on a cascade fault situation, and multi-dimensional behavior data are collected and recorded; the multi-dimensional behavior data comprises operation time sequence data, operation path deviation data and decision delay data; and inputting the multi-dimensional behavior data into a multi-dimensional human factor engineering evaluation system, and processing the multi-dimensional behavior data through a human factor analysis model to obtain a post evaluation result of the student. According to the embodiment of the invention, the accuracy of post evaluation can be improved.

The invention provides a Tibetan chicken intensive breeding health monitoring and early warning method and system, and relates to the technical field of computers, and the method comprises the steps: obtaining the three-dimensional motion and interval time sequence data of a Tibetan chicken flock in a breeding house, and the original chirp audio time sequence data; performing motion mode analysis according to the three-dimensional motion and the interval time sequence data to obtain a vector field sequence; carrying out stress level inference according to the vector field sequence to obtain a group recessive stress index; according to the group implicit stress index and the original chirp audio time sequence data, performing model construction to obtain a chirp-stress association model; acquiring real-time chirp audio data, and inputting the real-time chirp audio data into the chirp-stress correlation model for health state critical point prediction to obtain an instability precursor signal; and performing graded early warning according to the instability precursor signal to obtain a health early warning grade. According to the invention, prediction and early warning of health risks caused by group stress of the Tibetan chickens in an intensive breeding environment are realized.

The invention provides a remote data acquisition and analysis method and system for a baby stroller, and the method comprises the steps: synchronously acquiring environmental stimulation and passenger physiological reaction data through multiple modules on the baby stroller, locally processing the environmental data into cumulative stress indexes, and processing the physiological data such as sound production into state classification labels. And when the passenger has negative stress states such as crying, screaming and the like, the system automatically packs the accumulated stress index sequence in the previous period of time to form a stress event data packet. And the data packet and baseline data in a normal state are transmitted to a remote server together, and a specific environment mode causing the negative stress of the passenger is identified through comparative analysis. According to the method, through event-triggered data acquisition and comparative analysis, specific external factors causing discomfort of infants can be accurately identified and understood.

The invention relates to the field of deep coal bed gas development and reservoir reconstruction engineering, in particular to a compressibility evaluation method for a deep coal bed gas reservoir containing dirt band. The invention discloses a method for evaluating compressibility of a deep coal bed gas reservoir containing dirt band. The method comprises the following steps: obtaining a normalized coal rock brittleness index, a normalized coal body structure index, a normalized fracture toughness index, a normalized ground stress heterogeneity index, a normalized dirt band and coal rock layer stress difference index, a normalized fracture surface density index and a normalized mechanical specific energy index; converting the data into dimensionless numerical values, and unifying the value range to an interval of [0, 1]; and performing hierarchical weight assignment by using a triangular fuzzy analytic hierarchy process to form a deep coal rock compressibility index, and establishing an evaluation standard according to the deep coal rock compressibility index. According to the method, a theoretical basis is provided for optimal decision-making of the on-site deep composite coal rock reservoir fracturing transformation horizon.

The invention discloses a self-adaptive regulation and control method and system for an in-cabin environment driven by vital signs of a wounded person, and relates to the technical field of crossing of medical equipment and environment control. According to the self-adaptive regulation and control method and system for the in-cabin environment driven by the vital signs of the wounded person, a four-dimensional data acquisition system composed of physiology, environment, medical intervention and space-time scenes is built; the method comprises the following steps: synchronously acquiring multi-dimensional vital signs including brain electricity, skin electricity and other nerve and stress indexes, an in-cabin dynamic environment including detailed parameters such as airflow velocity and particulate matter concentration, external medical operation covering drug dosage and equipment working parameters, and transfer scene data including altitude change and bumping intensity; the limitation that only single environment or physiological data is acquired in the prior art is solved; when a craniocerebral injury wounded person is transported in an air transport mode, the system can be combined with the altitude change rate and the electroencephalogram alpha wave power, the blood oxygen lowering risk can be pre-judged 10 minutes in advance, and the problem that misjudgment is conducted only through current data traditionally is avoided.

The invention relates to the technical field of health data processing, in particular to a multi-modal health data processing method and device and a storage medium, and the method comprises the steps: collecting vital sign data, motion data and biochemical index data; dividing a multi-scale time window based on the vital sign data, the exercise data and the biochemical index data, and dividing user data types according to the vital sign data, the exercise data and the biochemical index data in the multi-scale time window; screening a positive sample and a negative sample based on the user data type, the vital sign data, the motion data and the biochemical index data, and performing feature coding analysis to obtain a joint feature vector; constructing a branch decision tree based on the joint feature vector; and analyzing a pressure index trend according to the vital sign data and the biochemical index data so as to update the branch decision tree. According to the invention, accurate abnormal early warning of the multi-modal health data is realized.

The invention provides a roadway surrounding rock stability judgment method and a classified supporting method, and relates to the technical field of soil layer or rock drilling and mine safety. The method comprises the following steps: S1, determining roadway surrounding rock stability influence factors; s2, dividing a rock mass strength index, a surrounding rock stress index, a surrounding rock integrity index, a roadway size index, a coal pillar ruler habit index, a water spraying index and the like; s3, determining a roof strength stress coefficient, a roof integrity coefficient and a side loosening coefficient; s4, performing field investigation or testing to obtain a classification index value, and determining an index threshold value through a fuzzy clustering method; s5, determining classification of non-impact roadways and impact dangerous roadways according to the roadway impact dangerousness and the impact danger index; and S6, determining a roadway support mode according to the roadway surrounding rock stability classification. According to the method, more comprehensive influence factors are considered, parameters are more convenient to obtain, values of factors required by field test classification indexes are facilitated, and scientific support of roadway surrounding rocks is achieved.

The application provides a multi-factor coupling asphalt accelerated aging simulation method and system, and relates to the technical field of road engineering. The method comprises the following steps: at the beginning of simulation, performing multi-factor coupling asphalt aging environment simulation through an aging test box; obtaining set parameters of the multi-factor coupling asphalt aging environment simulation; determining an aging stress index according to the set parameters; obtaining the running time of the aging test box; determining an equivalent aging time according to the aging stress index and the running time; at the end of simulation, obtaining aging parameters of asphalt through a dynamic shear rheometer and a penetration meter; determining an aging degree deviation according to the aging parameters; and determining whether to adjust the set parameters according to the aging degree deviation. According to the application, the aging rate can be dynamically adjusted according to the aging stress index, and the set parameters can be dynamically adjusted according to the aging degree deviation to optimize the simulation conditions, improve the accuracy and reliability of the simulation.

The application discloses a method and system for rapidly predicting a creep stress index of a tin-based solder alloy by integrating a model, collects element compositions, test temperatures and creep stress index values of the tin-based solder alloy from literature, and adds experimental data as dataset samples; the element compositions and test temperatures of the tin-based solder alloy are sorted out and used as features for modeling; the dataset is randomly divided into a training set and a test set in a 4:1 ratio; the collected creep stress index values of the tin-based solder alloy are used as target variables, and the features are used as independent variables; based on the divided training set, the independent variables are scaled by using RobustScaler, three learners are trained and integrated to obtain an R-X-L integrated model; and the R-X-L integrated model is used to rapidly predict the creep stress index of the test set samples and four independent experimental samples. The prediction model for the creep stress index of the tin-based solder alloy is simple, fast, low-cost, pollution-free and the like based on reliable literature data and a modeling method.

A heart-lung stop prediction device includes: right and left sound detection sections for detecting sounds generated by heartbeat fluctuation at right and left corneal wind sites, respectively, and detecting sounds generated by breathing fluctuation at the right and left corneal wind sites, respectively; a calculation section for calculating a stress index of a mammal based on the sounds generated by heartbeat fluctuation and the sounds generated by breathing fluctuation detected by the right and left sound detection sections; a prediction section for predicting a heart-lung stop period of the mammal based on the stress index calculated by the calculation section; and a notification section for notifying the heart-lung stop period predicted by the prediction section.

A healthcare apparatus includes a BCG sensor; a camera; and a processor configured: to detect a ROI) corresponding to the face from the color facial image; to convert the detected first color image into a black and white image to acquire a first black and white image; to convert the detected second color image into a black and white image to acquire a second black and white image; to apply the acquired first black and white image and the acquired second black and white image to a predetermined trained algorithm model to output a remote photoplethysmography (rPPG) signal waveform of the subject; to calculate a first stress index based on the first heart rate variability; to calculate a second stress index based on the second heart rate variability; and to output a stress index of the subject based on the first stress index and the second stress index.

The invention belongs to the technical field of bioengineering, and discloses a bacteriophage growth environment monitoring and evaluating method. The method comprises the following steps: firstly, establishing a baseline data set with state labeling by utilizing a historical time sequence of a multi-batch culture process; environment parameters, host cell state parameters and phage replication related signals are synchronously collected in the operation process, and standardized time sequence data are formed through unified preprocessing; extracting an instant value, a short-time variable quantity and a window fluctuation quantity from the standardized data, and generating a deviation degree record; calculating an environment deviation score, a host deviation score and a replication deviation score according to the deviation degree record, and constructing a bacteriophage growth suitability index, a host stress index and a stability factor; performing hierarchical evaluation, trend fitting and significant change point identification on the time sequence of the indexes, and generating a trigger signal when a continuous threshold exceeding condition is met; and in combination with a preset intervention strategy set and the process simulation model, performing simulation verification on the candidate intervention scheme.

The invention relates to the technical field of data processing, in particular to an ecological environment detection method and device based on multi-source data and a storage medium, and the method comprises the steps: collecting spatial distribution parameters and environmental stress parameters; constructing an intrusion space index based on the target plaque area and the target plaque boundary perimeter; constructing a habitat stress index according to the daily average submerging duration and the soil salinity of the root system layer; constructing a diffusion risk factor according to the plaque area of the associated region of the target plaque, constructing a risk index based on the diffusion risk factor, the intrusion space index and the habitat stress index, and constructing a plaque treatment priority; and constructing an adjustment coefficient according to the canopy temperature daily difference, constructing an update coefficient according to the seed removal rate and the adjustment coefficient, and updating the plaque treatment priority based on the update coefficient and the target plaque area. According to the method, accurate diagnosis, risk early warning and scientific sorting of treatment priorities of the spartina alterniflora invasion situation are realized.

The invention discloses a light pressure relieving method based on face micro-expression recognition, and relates to the technical field of intelligent driving, and the method comprises the steps: collecting a driver face image through a vehicle-mounted camera, carrying out the preprocessing of the driver face image, carrying out the standardization processing of the preprocessed driver face image, and obtaining key micro-expression feature data; performing pressure state evaluation based on the key micro-expression feature data and the driving context data to generate a pressure index; triggering a hierarchical soothing intervention strategy according to the pressure index, verifying the soothing effect after the hierarchical soothing intervention strategy is executed, and updating an intervention triggering threshold value based on a verification result. The method can improve the depiction capability of a real driving situation, keeps continuous effectiveness along with individual and scene evolution, and has the engineering advantages of maintainability and expandability.

The invention discloses a plateau road hybrid electric vehicle energy management system performance evaluation method and a plateau road hybrid electric vehicle energy management system performance evaluation system, and relates to the technical field of hybrid electric vehicle energy management system performance evaluation. The environment information comprises the altitude of the plateau road working condition, the crosswind speed of the plateau road working condition, the environment temperature of the plateau road working condition and the atmospheric pressure of the plateau road working condition; an energy management system performance evaluation model is set, an energy management system performance evaluation index is calculated according to the environment information, the energy management system performance evaluation model is composed of a normalized energy deviation value, a thermal stress index value, a power distribution smoothness index and a battery SOC, and the change rate of the engine power distribution proportion is calculated through an environment correction factor; calculating a power distribution smoothness index; and mapping the performance evaluation index of the energy management system into a comprehensive performance score so as to quantify the overall performance level under the working condition of the plateau road.

The invention, which relates to the technical field of sugarcane monitoring, discloses a monitoring system for the induction effect of a sugarcane flowering phase, the system comprises a multi-dimensional sensing layer, an intelligent decision-making center and a precise execution layer. The multi-dimensional sensing layer collects illumination, temperature, phenological period, stress index and ScFT6 gene expression data through an environmental factor sensing module, a plant physiological status sensing module and a key gene expression real-time monitoring module; the intelligent decision center corrects a photoperiod and a temperature threshold through a dynamic threshold regulation and control model, and evaluates an induction effect in combination with gene expression; the precise execution layer executes shading, light supplement and temperature and humidity adjustment to form closed-loop regulation, and the system realizes dynamic precise regulation, improves the induction effect stability, adapts to complex environments and variety differences, and is suitable for sugarcane flowering phase induction monitoring.

The invention relates to the field of ecological restoration, and discloses a soil carbon sequestration restoration method aiming at plateau peat land freeze-thaw characteristics, which comprises the following steps: S1, on-site data acquisition and raw material preparation: acquiring data of a plateau peat land construction site through an acquisition tool, and preparing a restoration matrix material at the same time; s2, threat level weight distribution and comprehensive restoration base value coefficient establishment: based on the data collected in the step S1, dividing and distributing physical stress data and chemical stress data weights, and establishing a comprehensive restoration base value coefficient according to the distributed physical stress data and chemical stress data weight values; and S3, dynamically adjusting a formula and performing precise adaptive construction: performing formula adjustment and adaptive construction on the repair base value material prepared in the step S1 through the comprehensive repair base value coefficient obtained in the step S2. According to the method, through field data acquisition and stress index construction, the repair prescription can be accurately matched with the specific stress type of the field, and accurate construction is realized.

Methods, systems, and devices for measuring compressive toughness are described. A system may obtain heart rate variability (HRV) data from a user via a wearable device over a plurality of time intervals, wherein each time interval includes a waking interval at which the user wakes and a sleeping interval at which the user falls asleep. The system may determine a stress index and a recovery index associated with a waking interval of the respective time interval and a sleep recovery index associated with a sleeping interval of the respective time interval. The system may then determine a compressive toughness metric for the user based on a weighted sum of the pressure index, the recovery index, and the sleep recovery index for multiple time intervals, where the compressive toughness metric indicates a relative ability of the user to handle and / or recover from pressure.

The invention discloses a hydropower dispatching and monitoring integrated processing method based on edge calculation, and particularly relates to the technical field of hydropower station operation control. The method comprises the following steps: collecting real-time operating parameters of a hydropower station to construct a fluctuation feature vector; locally running a trend deduction model at the edge node, and outputting a scheduling evaluation value and an equipment stress index; judging a communication state and an equipment risk level, and dynamically selecting to execute a center fusion scheduling strategy or an edge autonomous scheduling strategy; during a communication interruption period, continuously recording local execution data and feature input, and constructing a backtracking index; after communication is recovered, local data are uploaded to a central system, and synchronous compensation and model trend reconstruction are completed; through prediction deviation sliding correction and a model retraining triggering mechanism, the self-adaptive capability and the fault-tolerant level of the scheduling system are improved; the method has the advantages of high robustness, high real-time performance and evolvability, and is suitable for distributed and disaster-sensitive hydropower dispatching scenes.

This application provides a method, apparatus, equipment, and storage medium for identifying agricultural irrigation districts based on the irrigation-enhanced evaporation stress index (IESI), relating to the fields of agricultural water resources monitoring and management and remote sensing applications. The method includes: constructing a multi-source remote sensing and auxiliary dataset for the target area; calculating potential evapotranspiration and actual evapotranspiration and coupling them with a vegetation growth stability factor to obtain quarterly IESI data for the target area; determining irrigation identification thresholds applicable to plains and mountainous areas respectively; and discriminating the IESI of the target area to generate a binary spatial distribution map of irrigated and non-irrigated areas. This application solves the problems of existing irrigation area identification methods in large-scale applications, such as reliance on statistical data or large training samples, strong empirical thresholds, insufficient adaptability to terrain and regional differences, and complex calculation processes. It achieves irrigation behavior identification centered on evaporation stress response, ensuring computational efficiency while also considering spatial accuracy and regional adaptability.

The invention discloses a line safety margin assessment method and system under the influence of a forest fire, and relates to the field of power systems and automation thereof. The method comprises the following steps: S1, acquiring spatial information of a power transmission line based on a power grid geographic information system, generating a path evaluation point set, and acquiring multi-source real-time monitoring data of an area where the path evaluation point set is located by using a space-air-ground integrated monitoring network; s2, performing synchronization and space registration processing on the multi-source real-time monitoring data, performing reconstruction to obtain a dynamic fire scene parameter set, and calculating a line heat stress index; s3, performing Monte Carlo scene simulation by adopting a probability risk assessment method based on the dynamic fire scene parameter set and the line heat stress index, and generating a probabilistic safety margin index; and S4, determining a risk level according to the probabilistic safety margin index and the line heat stress index, and outputting a visual risk assessment result. And through multi-source data fusion and probabilistic evaluation, cumulative, precise and robust safety margin judgment of the power transmission line under the forest fire is realized.