289 results about "Respiratory frequency" patented technology

The invention relates to the field of heart rate monitoring, and discloses a millimeter wave radar breath and heart rate synchronous monitoring method and system, and the method comprises the steps: transmitting a linear frequency modulation continuous wave signal according to a millimeter wave radar, and collecting original echo data reflected by a target region; baseband signal demodulation and phase information extraction are carried out on the original echo data to obtain original phase time sequence data, and the original phase time sequence data comprise thoracic cavity micro-motion features; and according to a Butterworth band-pass filter, preprocessing the original phase time sequence data through human body physiological signal frequency band characteristics to obtain a breathing frequency band signal and a heart rate frequency band signal. According to the method, the apnea event triggering threshold value and the arrhythmia early warning index are updated in real time through Kalman filtering and extended Kalman filtering, so that the monitoring system can dynamically adjust the health parameters, which means that the monitoring system can be optimized in real time and the abnormal health event can be accurately responded in different physiological states.

The invention discloses an intelligent adjusting method and system for self-adaptively adjusting a pillow type based on a sleeping posture of a user, belongs to the technical field of intelligent pillow type adjustment, and is used for solving the defects of the prior art in the aspects of support adaptability, adjustment accuracy, data monitoring dimension, interaction capability and the like of an intelligent pillow system. And better sleep experience cannot be provided for the user. The method comprises the following steps: acquiring a pressure distribution matrix of a head and physiological data of a user through a multi-mode perception sensor array; wherein the user physiological data at least comprises respiratory frequency information and cervical curvature information; analyzing the pressure distribution matrix and the physiological data of the user through the edge calculation chip, and identifying the current sleeping posture of the user; according to the current sleeping posture of the user and the cervical vertebra curvature information, adjusting parameters of the intelligent adjusting air bag pillow are determined; and according to the adjusting parameters, the intelligent air pump is controlled to adjust the height and rigidity of the to-be-adjusted air bag.

The invention belongs to the technical field of diving state monitoring, and relates to a diver state monitoring method based on biofeedback, and the method comprises the steps: aligning a sign signal with environment data through a timestamp, extracting motion features through acceleration data, constructing a motion noise model, and removing signal artifacts in real time; in the aspect of EEG signal purification, a db4 wavelet basis is adopted for carrying out wavelet packet decomposition, water pressure related frequency band characteristics are extracted, low-frequency drift is removed, meanwhile, heart rate signals are analyzed through nonlinear dynamics, a Lyapunov index is extracted to evaluate HRV changes, breathing and EEG # imgabs0 # wave coupling are analyzed through wavelet transformation, and the risk evaluation precision is further enhanced; in the aspect of environment compensation, through compensation of a depth and SpO2 relational expression and water temperature on respiratory frequency, in combination with SLTM network extraction time sequence features and a deep causal convolutional layer, risk trend prediction is performed, a diver risk level and a prediction curve are output in real time, a comprehensive risk index is finally calculated, the health state and potential risk of the diver are comprehensively evaluated, and safety is improved.

The invention relates to the technical field of vehicle safety monitoring, and discloses a non-contact type in-vehicle driver respiratory rate detection method and device. Physiological signal data are collected through a millimeter wave radar, infrared thermal imaging and a microphone array, breathing frequency band signals are extracted through self-adaptive wavelet transform, and breathing signal waveforms are reconstructed through time-frequency joint analysis. A model formed by combining a multi-channel convolutional neural network and a bidirectional gating circulation unit is adopted to extract a respiratory rhythm feature vector, interference is eliminated based on a dynamic time warping algorithm, and a respiratory frequency value is estimated by applying methods such as variational mode decomposition. And processing abnormal points and missing data through a sliding window mechanism, and outputting a stable monitoring result. The invention further provides a sensor data synchronous calibration and model online updating method. According to the invention, the breathing frequency can be detected with high precision in a complex in-vehicle environment, and the safety and intelligent level of the vehicle are improved.

The invention relates to the technical field of medical health information, and particularly discloses a heart rehabilitation training control method based on muscle force and electrocardiosignal real-time monitoring, which comprises the following steps: firstly, calculating real-time myoelectricity intensity to quantify muscle load, synchronously analyzing electrocardiosignals and respiratory frequency to obtain a cardiopulmonary state, and correcting physiological indexes in combination with a trunk motion state; then, a multi-parameter fusion result is generated through a weighted fusion algorithm, and the relevance between the muscle force load and the heart response is comprehensively reflected; then, the system judges the training intensity through a preset threshold model, generates a voice feedback instruction in real time to adjust the action of the patient, and adaptively updates training parameters to match the rehabilitation progress of the individual; meanwhile, the system continuously monitors multi-parameter changes, and automatically triggers an early warning mechanism when abnormal risks are detected, so that training safety is ensured. According to the method, the limitation that a traditional scheme depends on a single index is overcome, and personalized and dynamic heart rehabilitation training closed-loop control is achieved.

The invention discloses an auxiliary renal puncture device for the nephrology department, and belongs to the technical field of renal puncture, the auxiliary renal puncture device comprises a sickbed, a plurality of supporting legs are fixedly arranged at the bottom of the sickbed, fixing plates are fixedly arranged on the two sides of the sickbed, and second sliding blocks are slidably connected to one sides of the two fixing plates; the breathing frequency and amplitude of a patient are captured in real time through an infrared breathing monitoring module, and displacement data generated by breathing of the kidney are accurately obtained in combination with motion sensing modules such as a lead screw encoder and a driving gear angle encoder. The intelligent control system drives the first stepping motor and the second stepping motor to adjust the position of the portal frame based on six-dimensional force sensor data of the force sensing module, meanwhile, the angle of the puncture needle is adjusted in real time through feedback of a mechanical arm joint encoder, and dynamic compensation of kidney respiratory movement is achieved. According to the mechanism, the puncture deviation caused by breathing can be controlled within the range of + / -0.5 mm, the puncture positioning precision is remarkably improved, and poor sample quality or operation risks caused by breathing movement are avoided.

The invention discloses an oxygenerator remote control system and method based on the Internet of Things, and belongs to the technical field of intelligent medical equipment and remote health management, and the method comprises the steps: S1, collecting multi-dimensional physiological data of a user in real time through a multi-source physiological signal collection module, a heart rate variability index and a body movement signal; s2, based on the multi-dimensional physiological data, processing is performed through a preset physiological state prediction model, and a unified physiological stress index is solved; s3, determining a predictive oxygen supply flow rate value in combination with the physiological stress index and a preset user basic oxygen supply flow rate; s4, according to the predictive oxygen supply flow velocity value, a control signal for a physical execution component of the oxygen generator is generated so as to dynamically adjust oxygen supply parameters, and conversion from passive compensation to active prediction is achieved.

The invention relates to the technical field of intelligent monitoring, and particularly discloses a lung function evaluation method and device, electronic equipment and a storage medium, and the method comprises the following steps: obtaining a universal dynamic threshold range of respiratory rate and respiratory amplitude; continuously collecting breathing data output by the MEMS breathing sensor, and obtaining and updating the moving average value and the moving standard deviation of the breathing frequency and the breathing amplitude in real time so as to calculate the change rate of the breathing frequency and the breathing amplitude in real time; according to the updated moving average value and the moving standard deviation, dynamically adjusting the dynamic threshold range of the breathing frequency and the breathing amplitude; comprehensively considering the adjusted dynamic threshold range and the change rate to carry out breathing abnormity judgment so as to carry out lung function evaluation; according to the method, finally, the adjusted dynamic threshold range and the breathing parameter change rate are comprehensively considered, breathing abnormity judgment is conducted, the lung function evaluation result is generated, breathing abnormity can be recognized more accurately, and the accuracy and reliability of lung function evaluation are improved.

The invention relates to a livestock health early warning and intelligent driving mechanical auxiliary system based on bio-electricity signal monitoring, and belongs to the technical field of animal husbandry intelligent management. Aiming at the problems of single parameter, strong driving irritability and low efficiency of a traditional monitoring method, the system is used for generating a health score in real time by adopting a CNN-LSTM hybrid model through data fusion of a bio-electricity signal acquisition module (electrocardio, myoelectricity and respiratory frequency) and an environment sensor, and driving an intelligent driving machine in combination with a deep reinforcement learning algorithm, so that the driving effect is improved. The abnormal livestock is guided to be isolated in a non-intrusive mode through ultrasonic directional wave beams and low-intensity laser. The monitoring accuracy is larger than or equal to 95%, the driving efficiency is improved by 60%, all-weather stable operation (availability is larger than or equal to 98%) is achieved, the epidemic disease risk and animal stress are effectively reduced, and an efficient intelligent solution is provided for large-scale breeding.

The invention discloses an adjustable blood transfusion reaction monitoring system. The system comprises a multi-mode physiological signal acquisition module, a data processing and analysis module, a user interaction and parameter configuration interface, a wireless communication module, an alarm and feedback module and a power management module. By integrating an electrocardiogram (ECG) sensor, an oxyhemoglobin saturation (SpO2) detection probe, a non-invasive blood pressure measuring device, a body temperature sensor, a respiratory rate monitor and a skin impedance change sensor, real-time acquisition of various physical parameters is realized; a rule-based threshold judgment model and a time sequence prediction model are built in the data processing and analysis module, and the data processing and analysis module is used for identifying potential blood transfusion reaction risks; and the user interaction and parameter configuration interface provides a touch display screen and remote terminal equipment to realize information input, data display and operation recording. Continuous dynamic monitoring, intelligent early warning and graded response in the blood transfusion process are achieved, blood transfusion safety and nursing efficiency are improved, and the system has wide clinical application prospects.

The invention relates to a self-adaptive sleep assisting method, system and device based on multi-mode perception and a medium. The method comprises the steps of performing seasonal correction based on latitude and longitude coordinates, constructing a dynamic rhythm curve, and obtaining user portrait data and real-time multi-source data; performing Kalman filtering fusion on the respiratory frequency signal and the vibration spectrum to generate a fused sleep staging result, performing classification processing on the original audio data, and generating a noise type label and duration; on the basis of the dynamic rhythm curve and a preset adjustment rate, calculating an upward light real-time parameter, and on the basis of the fused sleep staging result, the noise type label and the user portrait data, generating a downward light real-time parameter; and inputting the real-time parameters of the upper light and the real-time parameters of the lower light into a light field coupling model to calculate a light field distribution target value, and generating a light source driving instruction. According to the method, through multi-modal data perception, rhythm curve construction and light field cooperative regulation and control, the sleep staging precision, the light intervention suitability and the sleep assisting effect can be improved.

The invention discloses an intelligent atomization control method and system for respiratory tract treatment and a storage medium, and relates to the technical field of atomization control. During operation of the system, a multi-mode patient respiratory behavior monitoring module is used for capturing real-time respiratory frequency, depth, rhythm, cough characteristics and atomization efficacy based on a non-contact sonic sensor and lung sound analysis; the method comprises the following steps: monitoring environment humidity, temperature and aerosol diffusion rate in real time, analyzing the influence of the environment humidity, temperature and aerosol diffusion rate on drug mist particle size and sedimentation behavior, controlling atomization particle size through a variable-frequency ultrasonic exciter, calculating to obtain an intelligent atomization curative effect adaptation index AEAI, dynamically adjusting particle size distribution according to the real-time inhalation demand of a patient and environment parameters, and combining with respiration monitoring data to obtain the intelligent atomization curative effect adaptation index AEAI. According to the method, the medicine mist release opportunity is adjusted in real time, threshold value comparison is carried out by monitoring respiratory resistance changes and real-time feedback of lung sound characteristic improvement degree, a medicine release strategy is obtained, and remote doctor viewing and parameter adjusting functions are provided.

The invention discloses a method and a system for predicting a machine withdrawal behavior of a medical respirator, and relates to the technical field of artificial intelligence, the method for predicting the machine withdrawal behavior of the medical respirator comprises the following steps: acquiring first respiration data generated in the process that a current patient uses the medical respirator in real time; inputting the first respiration data into a trained neural network model; based on the first breathing data, the neural network model generates second breathing data generated after the current patient uses the medical breathing machine in a first time period in the future; whether machine withdrawal can be carried out currently is predicted in real time based on the second respiration data and the neural network model, intelligent prediction is carried out through the neural network model, automatic machine withdrawal decision support is provided by the neural network model based on multiple key parameters such as the respiration frequency, the tidal volume, the oxygenation index and the lung compliance index, the burden of manual judgment is reduced, and the working efficiency is improved. The prediction accuracy and efficiency are improved, and the risk of machine withdrawal failure is reduced.

The invention relates to the technical field of intelligent medical rehabilitation, in particular to an abdominal respiration training method and system based on an intelligent guiding algorithm. The continuous real-time breathing data sequence contains at least one of a breathing frequency periodic change characteristic, a breathing amplitude dynamic change characteristic and a breathing phase accurate definition characteristic which change along with time; comprehensively evaluating the continuous real-time breathing data sequence according to a statistical analysis algorithm based on dynamic threshold setting and multi-dimensional feature fusion to obtain a comprehensive breathing health index for evaluating the breathing state of the user; according to the comprehensive respiratory health index, based on inference rules in a knowledge base, a customized respiratory training plan is generated through an intelligent decision engine combining a deep learning algorithm and a biological feedback mechanism. By implementing the method, potential information in the respiration data can be deeply mined, so that a more scientific and personalized respiration training plan is generated.

The invention relates to the technical field of equipment compensation control, and discloses a medical suction device with an altitude compensation function and a control method thereof. The method comprises the following steps: reading ambient air pressure data of a first pressure sensor, reading molecular sieve tank differential pressure data of a second pressure sensor, collecting pressure data of an output end of a third pressure sensor, and determining a real-time altitude according to the ambient air pressure data; a breathing pressure fluctuation value is calculated based on the molecular sieve tank differential pressure data, when the breathing pressure fluctuation value is lower than a preset negative pressure threshold value, the current breathing frequency is recorded, and nitrogen removal time, pressure equalizing time and flushing time are determined to control an electromagnetic valve to work; and when the oxygen concentration is lower than a first concentration threshold value and the real-time altitude height is lower than a preset height, increasing the nitrogen removal time by a first preset proportion and increasing the target rotating speed by a second preset proportion, and when the oxygen concentration is lower than a second concentration threshold value, stopping the operation of the compressor and closing all the electromagnetic valves. According to the invention, the false alarm rate is effectively reduced, and the equipment safety is improved.

A medical imaging method for detecting a movement and a magnetic resonance imaging system, wherein the method includes: receiving, through a plurality of channels, a plurality of original first time domain signals recording a movement of an object under examination; transforming, based on a plurality of respiratory frequency components as bases, the plurality of first time domain signals into a vector matrix including representations of phases; computing an eigenvector based on the vector matrix; transforming the first time domain signals into second time domain signals based on the eigenvector, and removing a maximum energy term related to a respiratory movement from the second time domain signals; and determining whether a portion of a non-respiratory body movement is detected, and determining to execute a sequence based on only a first time domain signal in which a portion of a non-respiratory body movement is not detected.

The invention discloses a device for assisting atomization treatment of children in the technical field of atomization treatment, which comprises an atomization cup, one side of the atomization cup is communicated with an adjustable atomization cover, the adjustable atomization cover comprises a mouth wrapping cover, the mouth wrapping cover is communicated with the atomization cup, and the edge of the mouth wrapping cover can be attached to the edge of the mouth of the children; the adjusting valve is arranged between the mouth wrapping cover and the atomizing cup, the adjusting valve can control the output quantity of the atomizing cup along with the change of the distance between the mouth wrapping cover and the atomizing cup, and the larger the distance between the mouth wrapping cover and the atomizing cup is, the smallest the output quantity of the atomizing cup is; the upper connecting cover is fixedly installed at the upper end of the mouth wrapping cover, and the upper connecting cover can wrap the lower portion of the nose of the child; according to the child breathing assisting device, a child patient can be assisted in breathing, and the output amount of atomized liquid medicine can be adjusted to be matched with the breathing frequency of a child.

The invention provides a sleep breathing condition detection method and system and electronic equipment, and relates to the technical field of human body sign monitoring, and the method comprises the following steps: obtaining a first breathing signal sampled by an optical fiber micro-vibration sensor; performing low-pass filtering processing on the first respiratory signal to obtain a second respiratory signal; envelope analysis and frequency analysis are carried out on the second respiration signal, and a target envelope width sequence and a target respiration dominant frequency sequence are determined; and according to the target envelope width sequence, the target respiration dominant frequency sequence, the reference envelope width baseline and the reference respiration frequency baseline, determining the sleep respiration condition of the user. According to the sleep breathing condition detection method, a large number of electrodes and wires do not need to be pasted on the human body, the use convenience is high, and the sleep comfort of the user is improved. In addition, the envelope width is combined with time-frequency analysis, the accuracy is improved through a multi-dimensional feature cross validation mode, and the accuracy of abnormal breathing event detection is high.

The invention discloses an underground breathing flow monitoring method and system based on adaptive filtering, and the method comprises the steps: collecting an airflow signal at the mouth and nose of a miner in real time through an MEMS hot film sensor, and synchronously collecting a body vibration signal through an acceleration sensor, and obtaining original breathing data containing dynamic interference and corresponding vibration data; the method comprises the following steps: preprocessing original breathing data containing dynamic interference and corresponding vibration data to obtain a digital breathing flow signal and a standardized reference vibration signal; jointly inputting the digital respiratory flow signal and the standardized reference vibration signal into a filtering processing model based on an LMS adaptive algorithm to obtain a target respiratory flow signal; performing data analysis on the target respiratory flow signal, and calculating to obtain a real-time respiratory flow value, respiratory frequency and tidal volume parameters of the monitored object; the real-time breathing flow value, the breathing frequency and the tidal volume parameters obtained through calculation are locally displayed in real time, and monitoring of breathing flow data during underground operation is achieved.

The invention relates to a neonatal respiratory distress risk assessment construction method, which comprises the following steps of: implementing non-intrusive micro-disturbance operation on a neonatal, and detecting respiratory rhythm phase drift, respiratory driving force low-frequency suppression and physiological tremor window change under disturbance so as to identify a pre-instability signal regulated and controlled by a respiratory system; collecting nerve and respiration feedback path data, analyzing the time difference between respiration instruction conduction and actual respiration starting, and identifying an abnormal decoupling phenomenon; based on multiple times of perturbation induction and feedback acquisition results, continuously monitoring the micro-adaptive recovery capability of the respiratory system of the newborn, and identifying the hidden fatigue trend of the respiratory system by judging progressive extension of respiratory frequency recovery time and gradual upward movement of respiratory power consumption; the respiratory redundancy regulation capability in the stress regulation process is monitored, premature intervention and repeated activation of an auxiliary breathing mode or a respiratory muscle group are judged, and the redundancy regulation critical compression phenomenon of a respiratory regulation system is analyzed to complete evaluation construction of the neonatal respiratory distress risk.

The invention relates to a working parameter adjusting method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring gas pressure and gas flow rate output by a breathing machine and collected by a breathing machine sensor; determining breathing information of the target user according to the gas pressure and the gas flow rate; the breathing information comprises at least one of breathing frequency, real-time tidal volume and breathing quality data; determining physiological state information of the target user according to the breathing information and a physiological state threshold value; and adjusting working parameters of the breathing machine based on the physiological state information. By analyzing the gas pressure and flow velocity data collected by the respirator sensor in real time, the breathing frequency, tidal volume and other key parameters of a user can be accurately determined, the breathing state of a patient is intelligently evaluated, the working parameters of the respirator are automatically adjusted according to the evaluation result, and the flexibility of the working parameters is improved.

The invention relates to the technical field of remote detection, and discloses an unmanned aerial vehicle human body vital sign detection method based on an ultra wide band radar, and the method comprises the steps: planning a flight path of an unmanned aerial vehicle; acquiring a reflection signal received by the ultra-wideband radar, position information of the unmanned aerial vehicle and image data of the thermal imager in real time; a reflection signal received by the ultra wide band radar is acquired and preprocessed in real time; processing the reflected signal based on a signal processing algorithm and extracting human body vital sign characteristic parameters; the human body vital sign characteristic parameters comprise respiratory frequency and heartbeat frequency; according to the human body vital sign characteristic parameters, the normal range of the preset human body vital sign characteristic parameters and the image data of the thermal imager, the existence condition of the human body life is judged. According to the invention, vital signs can be remotely detected, and human body vital sign signals under ruins can be rapidly and accurately detected in a large range by using the high maneuverability of the unmanned aerial vehicle.

The invention discloses a millimeter-wave radar respiration monitoring method and device based on a gated attention wavelet network, and the method comprises the steps: a millimeter-wave radar sends a detection signal to a human body target, receives an echo signal returned by the human body target, converts the echo signal into two paths of orthogonal lacing signals, and carries out the sampling and combination of the two paths of orthogonal lacing signals into a complex baseband signal; fourier transform is carried out on the fast time dimension of the complex baseband signal to obtain a one-dimensional distance image, a distance unit with the maximum average energy in the one-dimensional distance image is selected as a distance unit where the human chest is located, and a continuous phase of the distance unit where the human chest is located is extracted; inputting the continuous phase of the distance unit where the human body is located into a time gating attention encoder to obtain a phase after primary interference suppression; inputting the phase after the primary interference suppression into a frequency gating attention decoder, and reconstructing a pure respiration waveform; fourier transform and band-pass filtering are conducted on the pure breathing waveform, and the frequency where a peak value is located serves as the estimated breathing frequency. The method is higher in accuracy.

The invention relates to a flexible electronic skin integrated dust-breath bimodal detection system and detection method. A bimodal sensing module, a signal processing module, a wireless transmission module, a data application module, an abnormity early warning module and a power supply are integrated on a composite flexible substrate; the bimodal sensing module monitors a dust concentration characteristic signal of a working place and a breathing frequency characteristic signal of a worker in real time; the signal processing module receives the signals and carries out noise reduction and data fusion processing to obtain a data set D; the wireless transmission module transmits the data set D to the data application module in real time, performs data real-time display, historical data storage, report generation and data export functions, and supports synchronization to an occupational health management platform; meanwhile, the data application module can process data in the data set D and feed back a processing result to the abnormity early warning module, and the abnormity early warning module correspondingly triggers an early warning signal according to feedback data; according to the invention, the target can be continuously monitored, and a high-precision detection result is obtained.

The invention relates to the technical field of atomizers, and particularly discloses a pediatric medical emergency atomizer which comprises a compressor body and an atomizing cup. The two sides of the breathing mask are respectively and fixedly connected with a bandage; the medical emergency atomizer for the department of pediatrics comprises a breathing mask, two bandages, a wearing mechanism, an expiration frequency monitoring mechanism and an atomization rate regulating and controlling mechanism, the wearing mechanism is connected with the two bandages, the expiration frequency monitoring mechanism is connected with the wearing mechanism, and according to the medical emergency atomizer for the department of pediatrics, the breathing mask is worn on the face of a patient through the wearing mechanism; if the breathing frequency of the child patient is rapid, airflow exhaled by the child patient impacts the breathing mask at the moment, the bandage is pulled when the breathing mask is impacted by the airflow, the expiration frequency monitoring mechanism is triggered through the bandage, and the atomization rate regulation and control mechanism is controlled to work according to the rapid expiration degree of the child patient; the atomization rate regulation and control mechanism regulates the flow amount of high-speed airflow entering the atomization cup from the connecting guide pipe.

The present application discloses a vital sign evaluation system based on data analysis, which relates to the field of vital sign monitoring technology. The specific scheme includes: mixing the echo signals from the target area to obtain two intermediate frequency signals, and then amplifying them to obtain the first signal and the second signal; then performing respiratory filtering and heartbeat filtering to obtain the third signal and the fourth signal, and then determining the respiratory frequency and the heartbeat frequency, and then updating the respiratory filter parameters and the heartbeat filter parameters; obtaining the respiratory frequency and the heartbeat frequency at the current moment, as well as multiple historical respiratory frequencies and multiple historical heartbeat frequencies, determining the heart rate variability and respiratory depth changes corresponding to the current moment, and then determining the vital sign evaluation results based on the vital sign evaluation score formula. The present disclosure can solve the problem in the prior art that the filtering processing cannot follow the frequency changes of the vital sign signal in real time, resulting in inaccurate vital sign data, and improve the accuracy of the vital sign evaluation results.

The invention provides an airway atomization flow intelligent drug delivery control method and system, and relates to the technical field of medical instruments.The method comprises the steps that basic information of the age, the weight and the illness state severity of a patient is obtained, and physiological parameters of the respiratory rate and the tidal volume of the patient are collected in real time through a sensor; inputting the basic information and the physiological parameters into a fuzzy adaptive control algorithm, and initializing an individual administration rule base according to the basic information; based on the real-time change trend of the respiratory rate and the tidal volume, three initial control index values including an atomization flow value, a drug concentration value and a time interval are dynamically generated through a membership function. According to the invention, through the full-process design of data acquisition, intelligent modeling, accurate execution and data closed loop, intelligentization of atomization treatment is realized.

The invention discloses an infusion state and vital sign intelligent monitoring method based on multi-modal fusion, and relates to the technical field of sign monitoring, and the method comprises the following steps: respectively collecting infusion dripping speed, pipeline liquid state, infusion pressure and breathing and heart rate signals of a patient; filtering the original signal of each sensor; key features are extracted from the processed signals, wherein the key features comprise parameters including the average dripping speed, the liquid existence proportion, the pressure change rate, the respiratory rate and the heart rate; carrying out normalization and confidence weighted fusion on the extracted infusion and vital sign characteristics, calculating an infusion state index and a vital sign index, and further obtaining a combined abnormal index; performing three-level anomaly judgment according to the fused index; monitoring data and alarm information are uploaded to a hospital information system in real time. By introducing a judgment algorithm based on multi-modal data fusion and confidence coefficient weighting, monitoring and graded alarming of the infusion state and the vital signs of the patient can be achieved.

The invention belongs to the technical field of information, and discloses an intelligent sleep environment adjusting method based on artificial intelligence, and the method comprises the steps: synchronously collecting the heart rate variability, respiratory rate and body temperature data of a user, and carrying out the classification and recognition of a sleep state through a long-short-term memory network and a random forest algorithm, and obtaining a sleep state label; judging a deep sleep state according to the sleep state label and a corresponding body temperature data change trend, and further obtaining indoor environment parameters and calculating a deviation value with an ideal sleep environment; a reinforcement learning algorithm is adopted to construct a personalized environment adjustment strategy model, and when the environment parameter deviation exceeds a personalized threshold value, a multi-dimensional environment optimization control scheme is automatically generated. According to the invention, accurate identification of the sleep state of the user and intelligent adjustment of the environmental parameters are realized, the sleep quality is effectively improved, and a comfortable and healthy personalized sleep environment is created for the user.

The invention relates to the technical field of orthopedic rehabilitation management, in particular to a self-adaptive orthopedic rehabilitation management system, which comprises a training module, an acquisition module, an evaluation module and an adjustment module, and is characterized in that the training module is used for assisting a user to execute training actions; the acquisition module is used for respectively acquiring the actuating muscle force, the actuating range and the breathing frequency of the user for executing the training action; the evaluation module is configured to generate an evaluation result of the training action process executed by the user on the basis of the difference between the action range and the standard action range, the difference between the action muscle force and the standard muscle force and the respiratory rate; the adjusting module is configured to correct the training level of the next training action based on the evaluation result, or output a rehabilitation prompt; through comprehensive analysis of the muscle force and the actuation range, the rehabilitation degree of the orthopedics department can be effectively evaluated, then the rehabilitation training intensity is accurately and adaptively adjusted, and the adaptability and timeliness of the rehabilitation management system for the orthopedics department are effectively improved.