34 results about "Stress index" patented technology

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.

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.

This invention relates to the field of geological environment monitoring technology and provides a method for constructing and monitoring carbon sequestration leakage stress indices based on integrated air-ground technology. The method includes the following steps: acquiring SAR and optical time-series remote sensing data; calculating the surface deformation index and vegetation stress index respectively; constructing a comprehensive index through a collaborative enhancement multiplicative model; and identifying leakage risk areas based on index anomalies. This invention effectively suppresses false positives caused by single-factor interference through spatial coupling of physical and ecological signals, significantly improving the accuracy and reliability of monitoring CO2 geological sequestration leaks using remote sensing technology. It provides a core technical solution for achieving large-scale, automated, and low-cost sequestration safety monitoring.

The application discloses a four-fingered tetra intelligent net cage breeding system and method based on behavior recognition and low stress control, which comprises a flexible anti-collision light-blocking net cage module, an underwater AI behavior early warning module, a micro-bubble and sound-light soothing intervention module and a dry-free flexible transfer module. The flexible anti-collision light-blocking net cage eliminates net collision and scale shedding and visual disturbance from the physical structure, the underwater AI behavior early warning module extracts three-dimensional features of sudden change rate of swimming speed, spatial dispersion and gill cover vibration frequency to calculate a comprehensive stress index, realizes four-level grading early warning, the micro-bubble and sound-light soothing intervention module realizes targeted soothing through mute oxygenation, blue-green light and low-frequency sound waves, and the dry-free flexible transfer module realizes underwater zero-fishing transfer of fish bodies. The application improves the survival rate of four-fingered tetra by 22.6% and reduces the scale infection rate by 93.5%, solves the stress bottleneck of large-scale breeding of the four-fingered tetra and is suitable for large-scale near-shore net cage breeding of the four-fingered tetra.

Disclosed is a method for monitoring attainment of flow state for activity by user. The method comprises: receiving first sensor data from eye-tracking means (202), and second sensor data from heart rate monitoring device (204), wherein first and second sensor data are collected while user is engaged in activity; determining eye fixation frequency and eye fixation percentage, and determining heart rate and heart rate variability, by processing first and second sensor data, respectively; determining focus index (304) of user, based on eye fixation frequency and eye fixation percentage; determining stress index (306) of user, based the heart rate and heart rate variability; determining flow index (308) of user, based on difference between focus index and stress index; and detecting that user has attained flow state for activity, when determined flow index is equal to or greater than predefined flow index for activity.

The application relates to the technical field of virtual reality and artificial intelligence data processing, and discloses a museum ecological system based on a meta-universe joint AI double engine and an implementation method, which comprises an immersive near-eye display terminal, a cloud distributed processing center and a distributed ledger network.The terminal collects multi-source sensing data streams; the processing center separates physiological tremor energy characteristic values through a multi-source biological signal analysis module and synthesizes a comprehensive stress index, generates rendering pipeline intervention instructions according to the comprehensive stress index by using a nonlinear semantic reconstruction module, calculates an effective collision radius by using a physical simulation and rendering engine to comprehensively equip attribute metadata, the comprehensive stress index and the physiological tremor energy characteristic values, and generates a drill evaluation report; and the distributed ledger network encrypts and stores the report.The application realizes self-adaptive adjustment of virtual environment visual rendering and physical interaction difficulty according to the physiological and psychological state of a user, and solves the problems of lack of real-time physiological response and low data reliability in the existing drill system.

The application discloses a container plant factory energy-saving regulation and control method based on fuzzy reinforcement learning and distributed collaborative optimization, and relates to the technical field of industrial automation control. The method comprises the following steps: collecting in-box environment, equipment energy consumption, external environment, time-of-use electricity price and crop canopy image data; performing core derived feature calculation based on the data to construct a comprehensive state vector; inputting the comprehensive state vector into an intelligent decision-making model based on a deep Q network; using a fuzzy reward calculator, obtaining an instant reward value according to a crop stress index and an electricity price pressure level through fuzzy rule base reasoning, training the model based on the value and making a decision, and outputting macro joint action instructions containing temperature, light and ventilation intentions; inputting the instructions into a hierarchical rule collaborative optimization layer, sequentially checking and refining through safety and boundary rules, energy efficiency optimization rules and economic dispatching rules, generating device-level control instructions and executing the instructions, and effectively reducing system energy consumption and electricity cost on the premise of ensuring the stability of the crop growth environment.

The application discloses a kind of multi-specification rolling linear guide precision retention-oriented fast evaluation and migration prediction method, belong to precision machinery component performance test and life prediction technical field, it includes obtaining time series monitoring signal and precision evolution data, generates source domain degradation sample pair;Non-dimensional mapping is executed, the standardized hertz contact stress index and lubrication state index are calculated, a general precision degradation base model is obtained by training deep neural network;For the new specification guide to be tested, execute transfer learning, generate target domain special prediction model, combined with target domain short-period physical invariant feature sequence, execute extrapolation calculation, output long-term precision retention evolution curve.The application adopts the technical means of constructing non-dimensional physical invariant feature space and degradation trajectory dynamic mapping, can use mature specification's historical full life cycle data, realize the cross-type prediction and fast performance screening of new specification guide precision retention.

This invention relates to the field of respiratory detection technology and discloses a system and method for assessing myocardial load in obstructive pulmonary disease (OSA) based on multimodal physiological signal fusion. The system comprises: a signal acquisition module that acquires blood oxygen saturation, electrocardiogram (ECG), and chest surface electrocardiogram (CTECG) signals; a window positioning and trimming module that determines candidate windows based on blood oxygen saturation and ECG signals, extracts the trunk vibration component of the CTECG signal to determine trigger points, and extracts an effective excitatory sub-window; a feature extraction module that extracts myocardial mechanical load, ECG ischemic components, and timestamps within the sub-window, and calculates the electromechanical delay decoupling time difference; and an assessment and judgment module that calculates a stress index based on the aforementioned components and time difference, and outputs results with phenotypic classification. This invention utilizes multimodal physiological signal fusion to extract the effective excitatory window and extract electromechanical delay features, achieving objective assessment and phenotypic refinement of OSA myocardial load.

This invention relates to the field of competitive performance monitoring and prediction technology, and discloses a method and system for predicting competitive performance based on multimodal data fusion and machine learning. The system collects physiological data (such as heart rate, blood oxygen, HRV, body surface temperature, etc.), environmental data (such as temperature, humidity, air pressure), and behavioral data (such as steps, sleep structure, calorie consumption) in real time, performs feature weighted fusion using the entropy method, and evaluates competitive performance based on an LSTM-CNN hybrid neural network model. The system outputs fatigue index, stress index, recovery ability index, and exercise readiness index, enabling efficient early warning and in-depth analysis report generation, significantly improving the accuracy and real-time performance monitoring of individual athletic performance, and is applicable to sports training, health management, and competitive performance optimization.

The present application relates to the field of ecological environment monitoring and geographic information system (GIS) application technology, and specifically provides a loess plateau ecological region carbon sink accounting method based on terrain effect and mining stress. The method of the present application comprises obtaining a multi-source correction data set, wherein the types of the multi-source at least include remote sensing data, geographic data and ground data; determining a mining stress index based on the multi-source correction data set; determining a soil erosion carbon pool estimation value based on the multi-source correction data set; determining a net ecosystem productivity based on the multi-source correction data set, the mining stress index and the soil erosion carbon pool estimation value. The present application realizes the quantitative inhibition effect of mining activities on vegetation carbon fixation, thereby reducing the deviation of forest carbon sink accounting around the mining area, making the accounting result in the ecological fragile area more objective and conservative, improving the accuracy of carbon sink accounting, realizing regional correction of net ecosystem productivity, and improving the accuracy of loess plateau ecological region carbon sink accounting.

This invention discloses a dynamic risk assessment method for distribution network equipment operation based on digital twin and SVDD, relating to the field of distribution network monitoring and maintenance technology. The method includes: processing multi-dimensional observation data of the equipment, completing missing data, and quantifying its reliability to construct a reliability matrix and equipment feature vectors; constructing a reliable noise covariance matrix based on the reliability matrix, and updating the calibrable parameters of the digital twin model using model residuals to obtain reliable model parameters; constructing an SVDD input vector based on the equipment feature vectors, the reliable noise covariance matrix, model residuals, and reliable model parameters, and calculating the SVDD anomaly distance and equipment health; calculating the predicted temperature rise based on the twin prediction state within a preset prediction window, and coupling the predicted electrical stress with the predicted temperature rise to obtain a predicted comprehensive stress index; calculating the cumulative risk probability of the equipment within the preset prediction window based on the equipment health and the predicted comprehensive stress index, and calculating the equipment risk value based on the cumulative risk probability.

This invention provides an intelligent monitoring method for electronic fences based on stress perception and variational uncertainty. The method includes: deploying a multimodal sensor array to collect multimodal data and preprocessing it; performing multimodal data fusion optimization based on the preprocessed multimodal data to obtain global fusion features; determining the approximate range of parameters based on the global fusion features, locking local extrema, and generating an optimal parameter configuration through a reinforcement learning algorithm; and calculating the intrusion stress index and false alarm rate in real time based on the optimal parameter configuration, and optimizing and adjusting the physical threshold and calculation frequency of the electronic fence. This invention employs multi-fusion strategies such as cross-modal attention and dynamic projection fusion, adaptive ISI calculation and feature fusion, and conflict-aware gating fusion to deeply mine the complementary information of each modality's data, effectively resolving the heterogeneity and conflict problems between modalities. The generated global fusion features are more comprehensive and representative, improving the ability to characterize the features of the monitored targets.

The application relates to the technical field of supply chain management, in particular to a dynamic management and control method and system for integrated supply chain materials. The method comprises the following steps: S1: acquiring real-time multi-source data of supply chain materials, and constructing a molecular attenuation chain, a physical supply chain and a future sequence chain of the supply chain materials according to the real-time multi-source data of the supply chain materials; S2: acquiring a stress propagation graph of the supply chain materials according to the physical supply chain and the molecular attenuation chain of the supply chain materials, and calculating a cumulative stress index of the supply chain materials based on the stress propagation tree; S3: calculating a dynamic potency attenuation index of the supply chain materials according to the cumulative stress index, and constructing an intervention decision tree of the supply chain materials according to the physical supply chain, the future sequence chain and the dynamic potency attenuation index. The effect index and intervention scheduling of the supply chain materials in transportation are calculated, and the supply chain materials are dynamically managed and controlled.

The present application relates to the technical fields of intelligent chip detection, and discloses a packaging abnormality monitoring method and system for multi-layer stacked memory chips, comprising: collecting the thermal compression bonding temperature and bonding pressure value of the chip in real time, determining the packaging process stress index of the chip by using the thermal compression bonding temperature and bonding pressure value, querying the multi-process cumulative duration and historical packaging failure information of the chip, analyzing the packaging process deviation coefficient of the chip by using the packaging process stress index and multi-process cumulative duration, constructing the monitoring cycle table of the chip based on the packaging process deviation coefficient and historical packaging failure information, measuring the electrical characteristics of the through silicon via structure of the chip, determining the impedance amplitude and phase angle of the chip based on the measurement data of the electrical characteristics measurement, and monitoring the packaging abnormality of the chip based on the impedance amplitude and phase angle to obtain an abnormality monitoring report. The present application can accurately locate the abnormal packaging position of the multi-layer stacked memory chip, thereby improving the efficiency of abnormality monitoring.

This application relates to the field of analysis and design of cable-stayed structures and structural safety monitoring technology. It discloses a data processing method, equipment, apparatus, and medium. The method includes acquiring performance reference information, which characterizes the displacement changes of at least some nodes and the internal force changes of at least some components in the cable-stayed structure when various time-varying factors act on it. Based on the displacement and internal force changes, the method determines the performance indicators of the cable-stayed structure during a target time period. These performance indicators include the total nodal displacement index and the total component stress index under the influence of various time-varying factors, as well as the influence strength of each time-varying factor on the nodal displacement and component internal force. Based on the total nodal displacement index, the total component stress index, and the influence strength, the method obtains the performance evaluation results of the cable-stayed structure during the target time period. This method can make the performance evaluation results of the cable-stayed structure more closely match the actual performance changes.

This application discloses a method, device, and storage medium for driver emotion regulation, belonging to the field of vehicle control technology. The method includes: acquiring the power of theta and beta waves in purified EEG signals and the pure PPG waveform of the driver's facial ROI in infrared video; using the ratio of the theta wave power to the beta wave power as a fatigue index; calculating a stress index based on the pure PPG waveform of the facial ROI; calculating the product of blink frequency and grip force fluctuation coefficient on the steering wheel as attention distraction; determining music parameters for regulating the driver's emotions based on the fatigue index, stress index, and attention distraction; combining the music parameters, data on the vehicle's environment, and the driver's historical music preferences to generate a music prompt text for regulating the driver's emotions and converting it into buffered audio of a certain duration; arranging the buffered audio in the order of generation in a playlist and playing it, thereby performing effective emotion regulation.

The application aims to provide a highland adaptation capability screening index system and method, comprising the following steps: S3, simulating motion load: implementing timed / intensity aerobic load by a treadmill or a power bicycle, continuously monitoring stress indexes such as blood oxygen minimum value and its occurrence time, heart rate trajectory and peak value, blood pressure change before and after exercise, symptom score and optional lactic acid increment; S4, recovery monitoring: recording SpO2 rising time, 1 / 3 minute heart rate recovery amount and blood pressure falling condition after terminating the load and recovering to normal oxygen, and re-evaluating symptom increment; S5, scoring and grading: inputting the two indexes of rest and stress into a scoring model to obtain a comprehensive score S and outputting an adaptation grade according to a threshold value; the problems of insufficient dynamic stress monitoring, unclosed loop of process and safety strategy, lack of portable deployment capability, lack of data-driven calibration of scoring function and weight in the prior art can be solved.

The application discloses a pig growth curve prediction method based on multi-source data, an electronic device, a storage medium and a program product, relates to the field of intelligent breeding and agricultural information technology, and is used for solving the problem of extensive stress large-feeding decision collection in contact type in pig growth prediction technology. By fusing an RGB image and depth point cloud data, an improved HRNet network introducing trunk rigid constraint and four-limb elastic constraint loss is used to extract pig key points and calculate body size parameters; environment data is fused to calculate an environment stress index to correct time sequence characteristics; a growth inflection point is determined based on multi-head self-attention weight and growth rate; a Chrono-Growth Transformer model is used to segment and fit a growth curve with the inflection point as a boundary; and growth deviation is calculated to realize abnormal early warning and individualized feeding. The application combines deep learning and multi-source data, and the average accuracy of weight estimation can reach more than 95% and the breeding cost is reduced.

This application discloses an insulin pump dose adjustment method, system, device, and storage medium. The insulin pump dose adjustment method includes acquiring the target user's electronic health record, real-time blood glucose data, and perioperative stress index data, wherein the perioperative stress index data includes the target user's surgical type and anesthesia method; inputting the electronic health record, real-time blood glucose data, and perioperative stress index data into a trained insulin pump dose prediction model to obtain the insulin dose prediction result output by the trained insulin dose prediction model; and converting the insulin dose prediction result according to a preset standardized format to obtain a standardized insulin dose prediction result, which meets the blood glucose management needs of the user during the perioperative period.

The application discloses a kind of based on multi-factor fusion evaluation's water budget benchmark quota of water user measure method, comprising: obtaining the multi-source data of water user and constructing multidimensional dynamic feature tensor;Heterogeneous graph structure is constructed, and implicit state vector of user of fusion space-time association is generated using graph attention network and time series convolution network;Real-time meteorological factor and market quotation factor are obtained, and after splicing with implicit state vector, input bayesian deep neural network to obtain the probability density function sequence of future water demand;With the sequence as random constraint, combine water permit and industry index to build multi-objective optimization model to obtain candidate benchmark quota, determine preliminary benchmark quota after decision preference screening;Market sensitivity index is generated using historical water right transaction data, and dynamic adjustment coefficient is generated by inputting fuzzy reasoning system combined with meteorological stress index, and the final water budget benchmark quota is output after calibration. To improve the scientificity, accuracy and adaptability of water resources budget management.

The application provides a network flow data packet balancing discarding method, device and medium, relates to the technical field of network flow data packet balancing discarding, and comprises the following steps: determining a stress index used for representing the overall balancing state of a system; judging whether the system is in a balancing state according to the numerical range of the stress index; if the system is not in the balancing state, adjusting the transfer rate through a preset feedback control algorithm; the control amount of the feedback control algorithm is determined according to the errors of the stress index, the occupancy rate of a network card cache area relative to a target value and the occupancy rate of a discarding cache area relative to a target value; transferring the network flow data packets temporarily stored in the network card cache area to the discarding cache area according to the adjusted transfer rate; allocating corresponding hard disk identifiers to the network flow data packets in the discarding cache area, and writing the network flow data packets into corresponding hard disks in batches; and the application can enhance the running stability and reliability in a high-load or performance heterogeneous environment.

This invention relates to the field of fault prediction and health management technology for rotating electrical machines, specifically a rotating electrical machine operation support system and method, comprising: collecting operating data of the rotating electrical machine; extracting harmonic voltage and harmonic current based on the operating data; calculating electrical stress index and mechanical stress index to generate systematic latent stress; calculating the evolution rate of systematic latent stress; generating dynamic failure boundary based on electrical stress index; calculating mode switching decision function value based on systematic latent stress, evolution rate, and dynamic failure boundary; generating control mode switching decision based on function value; generating and executing a suppressive control strategy in response to the control mode switching decision; collecting operating data after executing the suppressive control strategy; and performing closed-loop correction of the suppressive control strategy based on the updated operating data. This invention transforms complex potential risks into quantifiable unified engineering indicators, significantly improving the accuracy and robustness of risk assessment.

This invention relates to a multi-scenario adaptive intelligent wearable human-machine collaborative emergency intervention method, belonging to the field of intelligent wearable emergency medical technology. It addresses the problems of fragmented monitoring and treatment chains for hypoxia, pain, and stress in emergency scenarios, as well as the lack of closed-loop correction mechanisms after intervention. The invention collects blood oxygen, circulation, electromyography, skin conductance, and posture parameters through wearable devices; determines hypoxia risk values, pain indices, and stress indices; uses a joint decision-making model to prioritize oxygen administration, physical therapy, and transdermal drug delivery interventions based on the principle of life support priority; executes interventions and performs closed-loop corrections based on parameter changes before and after intervention, enabling unified assessment and tiered collaborative treatment of emergency states. This method is applicable to emergency medical care, disaster relief, and field operations.

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 relates to the technical field of greenhouse environment control, in particular to an intelligent greenhouse temperature and humidity control method and system, which comprises the following steps: acquiring temperature channel and humidity channel of each sensing node in a greenhouse and marking space-time stamps; aligning the original sequence of the temperature channel and the humidity channel of each sensing node in the greenhouse to a regular space-time grid through space-time double-weight interpolation to obtain a temperature grid matrix and a humidity grid matrix; identifying saturation distortion based on the temperature grid matrix and the humidity grid matrix, inversely calculating a real dew point temperature based on a water vapor partial pressure conservation initial value of a grid suffering from saturation distortion, and calculating a dew stress index; dividing the greenhouse into an execution subzone, taking a minimum value of the dew stress index in the execution subzone as a stress representative value, and scheduling an execution mechanism to respond when the stress representative value crosses a trigger boundary. The application effectively solves the saturation distortion problem of the sensor under the condensation working condition, and significantly improves the response capability of the greenhouse control system under the extreme dew working condition.

The application belongs to the technical field of neural networks, and particularly relates to a wisdom management platform for import and export trade data based on big data, which comprises the following steps: obtaining original information containing order data, logistics node time stamp and enterprise credit level through a data acquisition preprocessing module; calculating shelf life urgency coefficient and residual time urgency through a life cycle stress index construction module, and integrating the two into a life cycle stress index; correcting the life cycle stress index based on the enterprise credit level through a credit level correction module; applying the corrected life cycle stress index to the forget gate of an LSTM network through a neural network construction module to dynamically regulate internal memory decay; and dividing the risk level according to the prediction result of the LSTM network and issuing a control instruction through an adaptive risk control module. The application improves the accuracy of transportation delay prediction and provides stable support for the whole-process wisdom management and control of cross-border logistics.

The application discloses a small stem radial growth automatic monitoring method and system based on machine learning, relates to the technical field of fruit tree data monitoring management, and the method obtains stem, leaf and fruit characteristic data of a preset space range of a target monitoring point in a current period, obtains a leaf and fruit stress index and a leaf and fruit source index through a pre-trained reinforcement learning model, dynamically adjusts the weight of stress and source according to the comparison result of the two indexes and a preset threshold, generates a first or second risk index through weighted summation, and outputs an initial risk report, further obtains a historical period sequence, obtains a maximum cross-correlation time lag through lag cross-correlation analysis, and if the maximum cross-correlation time lag is greater than a preset threshold, a secondary adjustment is made on the weight in combination with a development stage transition factor and other characteristics, and a more accurate risk index and report are generated. The application realizes dynamic, adaptive and high-precision automatic monitoring and early warning of small stem fruit drop risk.