59 results about "Pressure index" patented technology

The invention provides a time sequence fusion and health state prediction method and system for multi-dimensional physiological data, and relates to the technical field of electrical digital data processing.The method includes the steps that high-precision sensing equipment and a parameter calibration model are adopted to calibrate data, time synchronization and error compensation are achieved through a weighted fusion algorithm, and the accuracy of physiological parameters is improved; meanwhile, a fatigue index and a pressure index are corrected in real time by using a closed-loop feedback mechanism, and a comprehensive state index is generated based on an evaluation result for further analysis and prediction; the change trend of the comprehensive state index is predicted through a machine learning technology, and a targeted dynamic rehabilitation scheme including training intensity optimization, diet adjustment, rest cycle planning and the like is provided in combination with physiological data of the user; and a timing feedback mechanism is established, so that the user can obtain health state change and adjustment suggestions in real time, thereby improving scientificity and timeliness of a rehabilitation scheme, remarkably optimizing the rehabilitation effect and efficiency, and realizing intelligent health management at the same time.

The invention relates to the technical field of medical data processing, in particular to an intelligent liver, gall, pancreatic and spleen disease nursing management system which comprises a parameter selection module, a feature extraction module, a risk assessment module, a node deduction module and a collaborative distribution module. According to the invention, through correlation analysis of disease types and physiological parameters, establishment of a monitoring parameter screening mechanism, avoidance of ineffective parameter interference, dynamic identification of key indexes, and quantitative evaluation of parameter deviation degree and organ function influence, the real-time evaluation capability of patient states is improved; risk level evolution trend prediction and intervention matching degree analysis are adopted, the timeliness and adaptation degree of nursing intervention measures are optimized, task pressure index calculation and role permission recognition are utilized, dynamic allocation of nursing resources among multiple roles is achieved, the response efficiency of a management system to an emergency state is enhanced, and the risk level evolution trend prediction and intervention matching degree analysis are combined. And the collaboration and the real-time performance of the nursing process are improved.

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 relates to the technical field of pulse condition sensors, and particularly provides a pulse condition collection method, medium, device and product, the method can comprise the steps that in the process that a bionic touch pressure pulse condition sensor collects the pulse condition of a target object, the pulse wave amplitude change rate collected by the bionic touch pressure pulse condition sensor is obtained; under the condition that it is confirmed that the pulse wave amplitude change rate is smaller than a change rate threshold value, based on a preset pressure index, the pressing force of the bionic touch pressure pulse condition sensor is adjusted, pulse signals of the target object in different stages are obtained, and the stages comprise the floating stage, the middle taking stage and the sinking taking stage; wherein the pressing forces at different stages are different. According to some embodiments of the application, accurate collection of the pulse condition of the target object can be realized.

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 sales data-based drug inventory demand analysis method and system, and relates to the technical field of medicine management and supply, and the method comprises the steps: employing a BERT architecture, carrying out the medical field entity recognition and emotion intensity analysis of preprocessed multi-modal data of a medicine supply chain, outputting a public opinion pressure index, and carrying out the analysis of the emotion intensity of the preprocessed multi-modal data of the medicine supply chain; space coordinate conversion is carried out through a geographic entity association mapping method, and a geographic public opinion pressure index is generated; fusing the policy-demand coupling matrix and the public opinion geographic pressure index, performing multi-source time sequence feature extraction and dynamic weight distribution through a TCN-ConvLSTM hybrid model, and generating a demand tension vector; according to the invention, the responsiveness of medical institutions to policy adjustment and public health emergencies and the inventory management efficiency are obviously improved, and reliable guarantee is provided for the medication accessibility of patients.

The invention discloses a multi-source physiological information comprehensive feedback type music physiotherapy system, method and equipment. The system comprises a biological signal acquisition module, a data processing and analysis module, a music generation and regulation module, a feedback control module, a user interaction module, a receiving and forwarding module and the like. A physiological signal of a user is collected in real time through wearable equipment; denoising the physiological signals and extracting features, and fusing to generate a comprehensive pressure index (CSI); personalized physiotherapy music is matched or generated according to the CSI, and physiotherapy parameters are dynamically adjusted; music physiotherapy is carried out, physiological feedback is monitored in real time, and loop optimization is carried out until the optimal music physiotherapy state is achieved; synchronous collection of multiple physiological indexes is achieved through the wearable device and the like, and the music physiotherapy effect is enhanced by establishing a multi-stage intervention system and dynamic closed-loop regulation and control.

According to the breathing type safety relief valve provided by the invention, through the structural design of an air channel flow channel and a valve body, an independent'rubber valve + metal cutting edge 'end face sealing structure is designed in the small safety relief valve body, and the negative pressure air inlet function and the positive pressure exhaust function under different pressure indexes are met respectively; the valve is opened in an involute mode, breathable pressure relief is achieved, and pressure stability is guaranteed; the sealing mode of the valve is end face sealing, abrasion of a sealing pair is small during long-term reciprocating work, and the service life of a product is prolonged.

The invention discloses a soil aeration intelligent control system, which relates to the field of soil management and comprises a data acquisition module, a pressure acquisition module, a power adjustment module, a timer module and an anomaly analysis module. The data acquisition module is used for acquiring soil data, plant data and historical operation data; the pressure acquisition module is used for processing soil data to obtain a pressure index, and combining the distance between each aerator pipe and the air compressor to obtain the output pressure of the air compressor; the power adjusting module is used for obtaining a proofreading coefficient according to the historical operation data so as to adjust the output power; the timer module is used for determining aeration time and interval according to the pressure index and the plant data; the abnormity analysis module is used for analyzing the aeration difference value and processing abnormity; the aeration effect of the aeration equipment can be improved, and the aeration strategy is optimized.

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 shale gas hydraulic fracturing real-time anomaly judgment method based on a dynamic reference point, which comprises the following steps: according to real-time data, in combination with static pressure of sand-carrying fluid in a shaft, flowing friction resistance of fracturing fluid in the shaft and flowing friction resistance of fracturing fluid passing through a perforation hole, calculating net pressure of a shaft bottom at each moment; smoothing the well bottom net pressure array; selecting well bottom net pressure data at a plurality of moments from the well bottom net pressure array after smoothing processing, and taking the moment corresponding to the maximum value of the net pressure fitting relative error as a dynamic reference point; and taking the bottom net pressure derivative and the maximum pressure as indexes, and combining the net pressure index average value of the dynamic reference point to carry out abnormal comprehensive judgment. According to the invention, the abnormity can be carefully and timely captured.

The invention provides a self-adaptive cache control method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting resource occupation information of each process, and determining a resource occupation pressure index of each process according to the resource occupation information of each process; obtaining index information of each process, and determining a process importance score of each process according to the index information of each process; calculating an initial score of each process according to the process importance score and the resource occupation pressure index of each process; judging whether a process meeting a punishment condition exists in each process or not; if the process meeting the punishment condition exists, the initial score of the process meeting the punishment condition is adjusted, and the actual use score of the process meeting the punishment condition is obtained; according to the actual use score and the progress importance score of each progress, determining a call-in page number distribution rate of each progress; and adjusting the cache of each process according to the call-in page number distribution rate of each process.

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.

Methods for reducing the health burden on a user (14), characterized by a) the measurement of three-dimensional coordinates (17a, 17b, 17c) of selected key points (18a, 18b, 18c) of the musculoskeletal system (20) of the user (14) in a body posture (12) of the user (14) by a sensor (16) in the form of a depth sensor (40) and / or a camera in relation to a predetermined reference point f) Assigning the three-dimensional coordinates (17a, 17b, 17c) of the key points (18a, 18b, 18c) to an index value of a stress index by an analysis unit (34) and determining the time period in which the stress index assumes the index value, wherein the assignment of the three-dimensional coordinates (17a, 17b, 17c) of the key points (18a, 18b, 18c) to an index value is carried out using a neural network (44), (g) the output of feedback to the user (14) to reduce the health burden by an output unit (38), wherein the output of the feedback takes place after a shorter or a longer period of time in which the user assumes a posture (12), wherein the output of the feedback takes place after the shorter period of time if the deviation of the posture (12) of the user (14) from the reference posture (36) is greater than a predetermined first reference deviation.

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 invention provides a wind power gear box oil liquid monitoring method and device and a wind generating set, and is applied to the field of wind power gear box operation and maintaining.The method comprises the steps that firstly, information of abrasive particles in gear box oil liquid of each set is obtained, the abrasive particle index of target gear box oil liquid is calculated according to the information of the abrasive particles, and pressure data of the target gear box is obtained; the method comprises the steps of acquiring pressure data of a target gear box, calculating a target gear box pressure index according to the pressure data of the target gear box, then calculating a target gear box oil index according to a target gear box oil abrasive particle index and the target gear box pressure index, and finally determining the current state of the target gear box according to the target gear box oil index. According to the method, the gear box oil index is calculated by comprehensively considering the oil abrasive particle index and the pressure index, the current gear box state is represented based on the oil index, a basis can be provided for fault early warning of the fan gear box, fault hidden dangers can be found in time, serious faults of the gear box are avoided, and the operation and maintenance cost of a fan is effectively 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.