34 results about "Pressure index" patented technology

The invention belongs to the technical field of bedding, and discloses a mattress supporting performance self-adaptive adjusting method based on pressure comfort and a mattress, the method is applied to a mattress provided with a pressure detection assembly and an air pressure adjusting assembly, and the method comprises the following steps: obtaining pressure index data and sleep quality data of each partition of a body; calculating a pressure comfort comprehensive value of each partition of the body based on each pressure index data of each partition of the body; based on the intermediary factors influencing the sleep quality, constructing a correlation model of the pressure comfort comprehensive value of each partition of the body and the sleep quality; real-time data of pressure indexes of all the partitions of the body are obtained, the real-time sleep quality is calculated through the correlation model, and based on the real-time sleep quality and a preset sleep quality threshold value, the air pressure of the mattress areas corresponding to all the partitions of the body is independently adjusted at the same time. The overall comfort is defined by calculating the comprehensive value of the pressure comfort of each partition of the body, the overall dynamic balance adjustment of the supporting performance of the mattress is realized by utilizing a correlation model, and the overall supporting performance unbalance caused by local adjustment is avoided.

The invention provides a hypertension VR intervention method based on dynamic biofeedback and individualized modeling, and provides a hypertension VR intervention system based on dynamic biofeedback and individualized modeling. The method comprises the following steps: performing state analysis on dynamic biological feedback indexes of a hypertensive patient, namely a predicted pressure index (SPI) and an exercise intensity index (EII), deciding a hypertension VR intervention decision matched with the state of the dynamic biological feedback indexes, activating a VR scene of the corresponding decision in a system, and actively enabling the hypertensive patient to perform adaptive intervention training through VR equipment. Therefore, dynamic biological feedback intervention is realized by utilizing an intelligent decision training mode of the intelligent model. According to the method, training mode recommendation can be accurately carried out according to the patient state, state reward can be carried out according to the blood pressure change amplitude and the patient compliance score, so that the patient training mode is optimized, intelligent training is achieved, and the method has high guiding significance.

The invention relates to the technical field of intelligent wearable equipment, and discloses an intelligent wearable system integrating multi-modal physiological monitoring and active intervention, which comprises a ring end acquisition module, a mobile end cache module and a cloud intelligent analysis module, and the cloud intelligent analysis module comprises a multi-modal fusion analysis unit, an active intervention decision unit and a user digital twinborn model construction unit, and is used for calculating a sleep score, a readiness score, a pressure index, a blood oxygen trend and a menstrual period prediction result according to the multi-source physiological characteristics, and executing an active intervention decision based on a health state. The sensing module is integrated in the ring body, the light-weight design is suitable for long-term wearing, all-weather data acquisition is guaranteed, compared with a bracelet or a watch, the ring is comfortable and hidden to wear, the continuous use willingness of a user is enhanced, meanwhile, the ring is suitable for high-voltage office or sports scenes, and non-inductive health monitoring is achieved.

The invention discloses a visual monitoring method assisted by an AI image recognition algorithm in a transparent working sleeve under a spine endoscope, and relates to the technical field of medical instruments, and the method comprises the following steps: collecting a torque waveform, a perfusion pressure signal and a cavity pressure signal under a unified event time base line, constructing a resonance frequency band spectrum, and obtaining a resonance frequency band spectrum; calculating a resonance pressure index and extracting a phase evolution trajectory for liquid level distortion reconstruction; and under the constraint of the resonance pressure index and the phase evolution trajectory, recovering the liquid level phase, reconstructing a lens phase field in front of the camera, calculating a view distortion gradient, and determining a liquid level dynamic distortion core area. According to the method, a resonance dynamic map is constructed through multi-source data fusion, a liquid level potential field is reconstructed, a visual field distortion area is locked, artifacts are eliminated in combination with image feature recognition, perfusion control is reversely driven based on a reliable visual field state, and a closed-loop regulation and control strategy with time reversal gating and self-adaptive updating capacity is formed. And stable and intelligent control of the visual field in the operation is realized.

The invention provides an employee emotion index evaluation method and device based on multi-modal data. The method comprises the following steps: acquiring multi-modal related data of an employee; the related data comprises voice data of the employees, text data in an enterprise communication tool, video data captured by visual behaviors and physiological signals of the employees; carrying out feature extraction on the related data and carrying out cross-modal alignment to obtain multi-modal technical features; calculating a data quality coefficient and a scene correlation coefficient of each mode so as to calculate a final dynamic weight of each mode; fusing the multi-modal technical features based on the final dynamic weight of each modal, and performing analysis to obtain a comprehensive emotion index of the employee; the comprehensive emotional index comprises emotional tendency, emotional intensity and a pressure index. According to the method and the device, the comprehensive emotion index of the employee is obtained by comprehensively analyzing the data of the multiple modes, so that the defects that the current emotion evaluation of the employee is not comprehensive enough and low in accuracy are overcome.

The invention discloses a multi-modal data acquisition and processing system for dynamic pressure assessment, and the system comprises a multi-modal data acquisition module which synchronously acquires dynamic pressure signals of different modals through a plurality of high-frequency response pressure sensors; the data preprocessing module is used for carrying out real-time signal conditioning processing on the dynamic pressure signals in different modes to obtain preprocessed multi-mode dynamic pressure signals; the multi-modal data fusion module is used for carrying out space-time alignment and feature extraction on the preprocessed multi-modal dynamic pressure signals by adopting a multi-factor coupling model, and generating fusion pressure data through a fusion algorithm; and the dynamic pressure evaluation module is used for executing time-frequency domain analysis and mode recognition on the fusion pressure data through an analysis model, extracting key characteristic parameters, calculating a dynamic pressure index through a pressure evaluation algorithm, and generating an evaluation result containing the dynamic pressure index. According to the invention, high-precision and real-time dynamic pressure assessment can be realized, and the comprehensiveness and reliability of data acquisition and analysis are improved.

The invention provides a target detection method and system based on an improved topological myxin algorithm and a neural network, and relates to the technical field of target detection. The method is characterized by comprising the following steps: extracting saliency and boundary proportion characteristics from original radar image data, and obtaining an oil spill region-of-interest image; obtaining an optimal segmentation threshold value by using an improved topological myxomycete optimization algorithm, and carrying out optimal segmentation on the oil spill region-of-interest image in combination with a neural network to obtain a final segmented image; wherein the improved topological myxomycete optimization algorithm comprises the steps of converting fitness into a pressure index, dynamically updating conductivity based on an evaporation-growth mechanism, calculating information flow among myxomycete individuals in combination with the pressure index and the conductivity, and updating a myxomycete center based on the information flow. The method weakens the influence of wave reflection and mist interference on the segmentation result, improves the precision and robustness of oil spill identification, balances the precision and real-time performance of oil spill segmentation under the dynamic sea condition, and can meet the real-time processing requirements of maritime affair supervision and other scenes.

The invention discloses a pollution risk identification and early warning method based on a multi-level scale. The method comprises the following steps: dividing county level grids, town level grids, village level grids and extensive plot grids; selecting a pressure index, a state index and a response index as risk identification indexes, collecting corresponding remote sensing data, ground sensor network data and agricultural data, and then performing space-time matching by adopting a Bayesian fusion model to obtain a standardized data set of each level of grid unit; a Delphi method is adopted to carry out multiple times of assignment to determine the weights of the three indexes, then a risk identification model is constructed, then a risk value is calculated, and a total quantity Quantile grading method is adopted to divide risk levels to identify the risk of each grid unit; a quantile and exponential smoothing method is adopted to dynamically adjust a threshold value, then early warning grades are divided according to the threshold value, and finally early warning is carried out according to the early warning grades. The agricultural non-point source pollution multi-level scale high-precision identification and real-time early warning are realized, and the precision and timeliness of pollution management are improved.

The invention provides a non-inductive access method and device based on a Bluetooth key, a medium and a program product, and relates to the technical field of automotive electronics. The equipment firstly evaluates the current urgency degree of a user through a situation pressure index, the index fuses information of two dimensions of communication abnormality and schedule urgency, and the probability that the user is in an emergency state can be identified. When the situation pressure index exceeds the preset emergency situation threshold value, the system automatically starts an effective outlier recognition mechanism, and the mechanism retains signal characteristics representing rapid approaching of the user by screening candidate outliers with upward signal jumping, so that the rapid approaching trend of the user can be more sensitively captured. Therefore, when the user is in an emergency situation, the technical scheme can improve the unlocking response speed and the recognition accuracy of the Bluetooth key.

The invention discloses a multi-source equipment state visual monitoring method of a docking station and a related device, and the method comprises the steps: obtaining original data traffic on an uplink data bus between a target docking station and host equipment; calculating an average flow value and a flow change rate sequence of the original data flow; calculating an I / O pressure index of the uplink data bus; determining whether the docking station is in a performance sensitive state based on the I / O pressure index; when the docking station is not in the performance sensitive state, obtaining first state data of the host equipment and second state data of the external equipment through a first preset sampling frequency; when it is determined that the docking station is in the performance sensitive state, first state data of the host device and second state data of the external device are obtained through a second preset sampling frequency; generating a visualization instruction of the running states of the host equipment and the external equipment; and controlling a display screen of the docking station to carry out visual presentation according to the visual instruction. According to the invention, the adaptability of visual monitoring of the docking station can be improved.

The invention provides a calibration method of an internal pressure-resistant tester, belongs to the field of calibration of internal pressure-resistant testers, and can solve the problem that a pressure index and a time index cannot be accurately detected when the internal pressure-resistant tester is calibrated. The calibration method comprises the following steps: connecting the high-frequency digital pressure gauge with the internal pressure resistance tester through the interface connecting device; the memory of the high-frequency digital pressure gauge is reset, and the detection recording function is restarted; starting the internal pressure resistance tester to carry out pressure detection, and continuously detecting for three times under the same detection condition; pressurizing is stopped, the recording function of the high-frequency digital pressure gauge is stopped, and data recorded by the high-frequency digital pressure gauge is exported; and analyzing the exported data, and judging whether the pressure index and the time index meet requirements or not. The method can be applied to calibration of the glass bottle / PET bottle internal pressure resistance tester, key indexes such as peak pressure, pressure increasing rate, dwell pressure and dwell time which are difficult to measure by a conventional method of the glass bottle / PET bottle internal pressure resistance tester are accurately verified, the index detection effect of the glass bottle / PET bottle internal pressure resistance tester is ensured, and safety risks such as bottle explosion are effectively prevented.

The invention discloses a river network flood control situation studying and judging method and system based on blood vessel-nerve collaborative bionics. According to the method, a multi-source monitoring sensing network is constructed in a river network in a bionic mode to collect hydrological data in real time, the river network is divided into three levels of aorta, arteries and capillaries according to human body blood vessel grading characteristics, and a dynamic regulation and storage model is constructed in combination with an improved Saint-Venant equation set and a Manning formula. And calculating a basic conveying index, a dynamic buffer index, a structural stability index and an instantaneous risk pressure index. A comprehensive bearing capacity index is generated through a bearing capacity-pressure framework, five-level situation risk grading is achieved, and finally visual study, judgment and early warning are conducted based on a GIS platform. According to the invention, the accuracy and foresight of river network flood control regulation and control are improved.

The invention discloses an index and weight combined water resource bearing capacity evaluation robustness analysis method, system and equipment and a medium. The method comprises the following steps: establishing a multi-dimensional evaluation index system according to a support index subset and a pressure index subset; querying index data of the research area in multiple years to form samples in multiple years, and calculating index weights by respectively adopting four weight calculation methods; constructing a disturbance scene, and calculating an annual water resource bearing capacity index of each year; in a disturbance scene, considering a plurality of disturbance factors, and executing sensitivity analysis to identify disturbance factors and indexes which have obvious influence on the water resource bearing capacity index; and carrying out robustness evaluation according to the distribution stability degree of the water resource bearing capacity index, and selecting a most robust weight calculation method and a corresponding robust disturbance scene. According to the method, the related problems of high subjectivity of an index system, single weight setting, insufficient uncertainty propagation mechanism and lack of quantitative judgment of robustness in the prior art are solved.

This invention provides a method and system for time-series fusion of multidimensional physiological data and prediction of health status, relating to the field of electronic digital data processing technology. The invention employs high-precision sensing devices and parameter calibration models to calibrate the data, and uses a weighted fusion algorithm to achieve time synchronization and error compensation, improving the accuracy of physiological parameters. Simultaneously, a closed-loop feedback mechanism is used to correct fatigue and stress indices in real time, and a comprehensive status index is generated based on the evaluation results for further analysis and prediction. Machine learning technology is used to predict the changing trend of the comprehensive status index, and combined with the user's physiological data, targeted dynamic rehabilitation plans are provided, including training intensity optimization, dietary adjustments, and rest cycle planning. A timed feedback mechanism is established to allow users to obtain real-time changes in their health status and adjustment suggestions, thereby improving the scientific rigor and timeliness of rehabilitation plans, significantly optimizing rehabilitation effects and efficiency, and simultaneously achieving intelligent health management.

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 invention discloses an emotion-driven multi-region adaptive joint optimization intelligent temperature control method for an open space, and the method comprises the steps: building a corresponding relation between a region and a temperature control execution unit, and obtaining the state data of the region; calculating a regional emotion pressure index and a confidence coefficient corresponding to the regional emotion pressure index; establishing an inter-region influence weight matrix, obtaining a coupling emotion pressure index of each region, and determining an influence coefficient; constructing a multi-region joint optimization model; judging whether an emotional event triggering condition is met or not, solving the multi-region joint optimization model, obtaining an optimal control quantity vector, generating a control instruction and issuing the control instruction; and acquiring the environment parameters and the temperature control interaction disturbance parameters after execution in the next control period, and performing updating according to the evaluation result. The method has the beneficial effects that the timely, coordinated and stable adjusting effect on partition temperature control can be achieved under the operation environment of an open type multi-person shared space, the comfort is improved, and fluctuation caused by manual intervention is reduced.

The embodiment of the invention provides a fan regulation and control method and system based on multi-source environmental parameter self-adaption, and the method comprises the steps: firstly processing multi-source data, and carrying out macroscopic scene recognition to understand the context of a current environment; afterwards, the contextual information is utilized to guide an attention mechanism to generate dynamic weights for various environmental parameters, so that a composite environmental pressure index capable of accurately reflecting the current core regulation and control requirement is calculated, the system can dynamically adjust the control focus according to a real-time scene, and finally, the control focus of the system is dynamically adjusted according to the real-time scene. By inputting the composite index and the target value into the PID controller for accurate adjustment, the optimization of the operation strategy of the fan can be realized, and the intelligent level, the adaptive capacity and the comprehensive energy efficiency of the regulation and control system are remarkably improved.

The invention is suitable for the technical field of tactile sensors, and provides a tactile pressure switch and continuous pressure measurement fusion method and system, and the method comprises the steps: 1, obtaining tactile channel signals, and building a tactile channel reference value, the tactile channel signals comprise a tactile capacitance signal and a tactile resistance signal, establishing a tactile capacitance reference value corresponding to the tactile capacitance signal and a tactile resistance reference value corresponding to the tactile resistance signal one by one; step 2, constructing a tactile pressure index based on the tactile channel signal and the tactile channel reference value; 3, constructing a hysteresis threshold value based on the tactile pressure index, and carrying out hysteresis judgment to output a switching value; 4, acquiring a continuous pressure channel signal, converting the continuous pressure channel signal to obtain continuous pressure, and converting the tactile pressure index to obtain tactile equivalent pressure; according to the invention, jitter springback is suppressed by dynamic hysteresis, the stability and consistency of the switch are improved, recharge correction is triggered by residual errors, and the self-diagnosis and self-correction capability is provided, so that the precision is improved, and the user experience is improved.

The application is suitable for the technical field of underwater monitoring equipment sealing management, and particularly relates to a monitoring equipment health management method, system and equipment, which comprises the following steps: obtaining the cavity pressure and cavity temperature in a sliding window and external water pressure; calculating the sealing state characteristics of each sampling point based on the cavity pressure, cavity temperature and external water pressure, and calculating a pressure index; determining that the pressure index meets a sealing suspicious condition, and calculating a pressure abnormal confidence according to the pressure index and a sealing health baseline; obtaining a current image; determining an image index according to the current image and the external water pressure of the last sampling point, and calculating an image confidence; and determining a sealing health state grade according to the pressure abnormal confidence, the image confidence, the average equivalent water inflow volume change rate and the cumulative equivalent water inflow volume of the last sampling point in the sliding window. The method can realize quantitative identification of the degradation trend of the sealing performance of the underwater monitoring equipment, and realize grading of the sealing health state.

The invention discloses an emotion intervention method and system based on emotion detection, relates to the technical field of emotion intervention, and adopts chaos dynamics to analyze a Lyapunov index and a phase-space reconstruction dimension of a steering wheel, breaks through the static limitation of traditional threshold detection, realizes early quantitative recognition of an emotion sensitive state, and improves the accuracy of emotion intervention. The detection sensitivity of the road rage incubation period is obviously improved; a driving behavior state transition matrix is constructed through a hidden Markov model, and a dynamic evolution rule of an emotion accumulation process is effectively captured in combination with a time sequence feature of an accelerator pedal depth change rate, so that one-sidedness of single time point detection is avoided; road pressure index coupling environment pressure and physiological response are introduced, dynamic optimization of intervention opportunity is realized by means of a time convolutional network, and the problem of misjudgment caused by sudden change of external environment in a traditional scheme is effectively solved.

The invention belongs to the technical field of human body part pressure zoning signal processing, and provides a human body part pressure zoning processing method which comprises the following steps: acquiring human body pressure data and a preset anthropometry statistical model; according to the human body pressure data, determining the head position of the human body through a core interval data index; according to the gender, the height and the weight of the testee, a preset anthropometry statistical model and the head position, dividing the human body part into six areas to obtain six human body part subareas; pressure indexes, overall pressure indexes and body quality indexes of the six human body part partitions are calculated; and evaluating by using the pressure indexes of the six human body part partitions, the overall pressure index and the body quality index. By reducing three-dimensional data into one-dimensional data, partitioning of human body parts is realized, and meanwhile, more accurate partitioning data, pressure data and body quality index data are output in combination with preset data of a database.

The embodiment of the invention discloses a burying point sampling method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a system congestion index according to capacity and load characteristics acquired in real time, and determining a sampling pressure index according to the capacity and load characteristics and the system congestion index; determining a sampling decision score according to the capacity and load characteristics, and inputting the capacity and load characteristics and the system congestion index into a sampling strategy model to obtain a dynamic sampling rate output by the sampling strategy model; and determining a target sampling strategy according to the sampling pressure index, the sampling decision score and the dynamic sampling rate, and carrying out buried point sampling according to the target sampling strategy. According to the method, the target sampling strategy is reasonably determined according to the real-time dynamic operation state of the electronic equipment and the service priority condition of each piece of service burying point data, accurate burying point data acquisition is performed according to the strategy, and a comprehensive and accurate data basis is provided for subsequent data analysis.

The invention discloses a control method of an intelligent mattress and a control system based on the intelligent mattress, and relates to the technical field of smart home. According to the embodiment of the invention, processing, separation and feature extraction are sequentially carried out on collected original composite signals to obtain heart rate variation features and micro body movement features; calculating a pressure index and a sleep stage according to heart rate variation characteristics and micro body movement characteristics; and finally, the intelligent mattress is controlled based on the processed target composite signal, the heart rate variation characteristics, the pressure index and the sleep stage, so that a complete closed-loop path from'non-sensing 'to'accurate evaluation' and then to'dynamic intervention 'is constructed, and active and intelligent adjustment on the emotion of the monitored object is realized.

The invention relates to the technical field of intelligent analysis, regulation and control of resources, in particular to an intelligent analysis, regulation and control system for anesthesiology department operating room resources. Eye movement, physiology and operation behaviors of an operator are collected through a multimode parameter collection device group to obtain eye movement signal parameters, pulse wave signal parameters and operation behavior signal parameters, and then the attention concentration degree, the working memory load and the pressure index are analyzed and output through a multi-task deep learning model. Accurate recognition of the cognitive state of the operator is realized, and data support is provided for subsequent cognitive scene division; according to the method, the multi-dimensional cognitive state vector is analyzed through the cognitive scene division mechanism to obtain the corresponding cognitive decision scene, the cognitive decision scene is analyzed through the rendering control mechanism to output the rendering control strategy, invalid information interference and operation interaction cost are reduced, cognitive burden is reduced, decision efficiency and accuracy are improved, and user experience is improved. And the safety and the high efficiency of operating room resource regulation and control are ensured.

The invention relates to a virtual power plant scheduling method and system based on multiple interaction modes. The scheduling method comprises the following steps: acquiring a pressure signal of a distributed energy resource group, and quantizing according to the pressure signal to generate a mode pressure index of the resource group; the mode pressure index is monitored in real time, and when the mode pressure index is close to a preset pressure limit, a pressure early warning signal is sent out; according to the pressure early warning signal and the current power dispatching demand, adjusting an interaction mode parameter of the resource group; based on the adjusted interaction mode parameters, whether a scheduling gap exists or whether the early warning level is high after adjustment is predicted, if the scheduling gap or the early warning level is predicted to be high, negotiation with a standby resource group is carried out in a pre-judgment mode, and a scheduling instruction is sent based on the availability intention and the acceptance condition of the standby resource group; and scheduling the power of each resource group according to the scheduling instruction, monitoring and verifying the actual scheduling response effect of each resource group, and feeding back the actual scheduling response effect to the pressure signal acquisition link.

The invention relates to a pressure regulation and control method, device and equipment based on heart rate variability and a storage medium, and belongs to the field of pressure regulation and control. The method comprises the steps that in a control task, the blood volume change of subcutaneous capillary vessels of an operator is detected, and pulse wave signals for isolating vagus nerve electrical stimulation signal interference are collected in real time; after the collected pulse wave signals are subjected to preprocessing including amplification and filtering, the pulse wave signals are converted into a frequency domain for heart rate variability analysis, and a real-time pressure index in a control task is evaluated; establishing a pressure baseline, judging a real-time pressure level, adjusting stimulation parameters according to the pressure level to electrically stimulate the neck vagus nerve, and adjusting sympathetic-parasympathetic nerve balance to reduce pressure. The optimal physiological state of an operator is dynamically maintained on the premise that a control task is not interrupted, and safety, stability and high efficiency of high-precision operation are guaranteed.

The invention discloses a dynamic pre-reading throttling method, device and equipment based on IO path pressure and a medium, and relates to the technical field of pre-reading, and the method comprises the following steps: periodically detecting pressure information on an IO path of solid-state storage equipment; determining an IO pressure state of the solid-state storage device based on the pressure information; and dynamically adjusting a throttling strategy of a pre-reading mode according to the IO pressure state. According to the method, the IO path pressure in the SSD and the IO path pressure of a host channel are periodically detected, multi-dimensional pressure indexes are fused, calculated and mapped into a discrete pressure state, and accordingly pre-read starting, stopping and parameters are dynamically adjusted. According to the method, the pre-reading behavior is matched with the real-time load of the system, positive pre-reading is performed at low voltage to improve the performance, and pre-reading is controlled at high voltage to guarantee the key request response. According to the method, IO delay fluctuation under high load can be remarkably reduced while pre-reading earnings are enjoyed, resource waste caused by invalid pre-reading is reduced, and balance between performance and stability of a storage system is achieved.

The invention relates to the technical field of solid state storage, particularly provides an SSD write Buffer allocation method and device based on dynamic pressure sensing, and solves the problems of serious write amplification effect, large performance fluctuation, low resource utilization rate and the like caused by a static Buffer allocation strategy of a traditional solid state disk. The method comprises GC pressure assessment and Buffer writing dynamic allocation, the GC pressure assessment generates a quantized garbage collection pressure index by analyzing the physical resource state of the solid-state storage system in real time, and the garbage collection pressure index fuses the space resource shortage degree, the collection efficiency expectation and the reliability risk to obtain the garbage collection pressure index. Therefore, sudden load change is responded and long-term stability of the system is maintained. Buffer dynamic allocation is written to map the garbage collection pressure index into a Buffer resource allocation proportion, smooth transition from the garbage collection pressure index to the Buffer proportion for executing garbage collection is achieved, and GC is executed based on the Buffer resource allocation proportion.