726 results about "Heart beat rate" patented technology

The invention discloses a hand-eye coordination and attention evaluation method based on a mobile phone, and relates to the technical field of man-machine interaction and user behavior evaluation, and the method comprises the following steps: S1, in the process that a user executes a symbol matching test task of a mobile phone terminal, obtaining a heart rate variability sequence and an operation sequence interruption frequency, filtering and segmenting the pupil diameter change and the brain wave rhythm to obtain an original feature set; according to the hand-eye coordination and attention evaluation method based on the mobile phone, the continuity, reliability and objectivity of an evaluation conclusion are improved, the method is suitable for cognitive evaluation, man-machine interaction analysis and related intelligent application scenes, and the scientificity and practical value of hand-eye coordination and attention evaluation based on the mobile phone are improved.

The invention belongs to the technical field of wireless sensing and artificial intelligence, and discloses a high-robustness non-contact accurate electrocardiogram monitoring method based on a millimeter wave radar. Firstly, the distance and angle of a potential target are obtained through distance fast Fourier transform and digital beam forming technologies, and static background removal and thoracic cavity position detection are achieved in combination with mean filtering and a two-dimensional constant false alarm rate algorithm. And then a continuous phase is extracted by using a differential cross multiplication method, a two-step heartbeat-related phase extraction scheme is designed, body micro-motion and breathing interference are removed by adopting B-spline fitting and differential operation respectively, and a stable heartbeat-related phase signal is obtained. A heart rate-guided adaptive wavelet decomposition method is designed to obtain multiband features, and time-frequency joint features are extracted through a double-branch attention mechanism and a gating fusion part. Finally, the time-frequency joint features are input into an electrocardiosignal time domain reconstruction module based on a TransUNet architecture, high-quality reconstruction of electrocardiosignals is achieved, and the method has the advantages of being non-contact, continuous and convenient.

The invention discloses a pet wearable vital sign monitoring method based on a flexible sensor, which comprises the following steps: constructing a flexible multi-mode sensing interface, embedding a sensing layer into a fitting layer and fixing the sensing layer at the neck or chest position of a pet; a low-power thermal pulse is output to detect the contact characteristic, and a stable fitting interface is formed; multi-mode signals are collected and synchronously corrected, and filtering and baseline processing are executed to obtain a processed signal set; multi-modal features are extracted, a physical tensor is constructed, and vital sign parameters are calculated; body temperature, heart rate and respiratory rhythm components are extracted, a rhythm resonance curve is generated, and target configuration parameters are determined; sampling and communication are adjusted according to the target configuration, and an alarm is triggered and uploaded to a remote terminal when abnormity occurs. According to the pet vital sign monitoring system, high-precision, low-power-consumption and long-term stable monitoring of pet vital signs is realized by constructing a flexible multi-mode sensing interface and combining a physical consistency driven time sequence reasoning and rhythm energy scheduling mechanism.

The invention belongs to the technical field of smart old-age care product behavior debugging, and particularly discloses a smart old-age care product behavior dynamic adjustment method based on interactive data perception. According to the method, a sole mechanical fragment, a lower limb movement fragment and a heart rate fragment are extracted based on gait cycle alignment, gait stability, gait coordination and physiological conformity indexes are calculated, and coupling sensing of user movement behaviors and physiological states in the dynamic walking assisting process is achieved; the gait stability and the physiological conformity are fused to construct a gait physiological synchronism criterion, a hierarchical adaptive control strategy is implemented accordingly, and the limitation that a traditional supporting robot only depends on kinematics feedback control is broken through, so that on the premise that the user safety is guaranteed, response type physical and mental collaborative intelligent assistance is achieved, and the user experience is improved. And the risk prevention and control capability of rehabilitation training is effectively improved.

The invention relates to the technical field of nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

A heart rate detection earphone and an earphone device are provided. The heart rate detection earphone includes an earphone housing and an earphone functional component. The earphone housing includes an ear hook part, a connection part, and an earphone part. One end of the connection part is connected to the ear hook part, and the other end of the connection part is connected to the earphone part. The earphone functional component includes an earphone circuit board, an earphone battery module, an earphone speaker module, and a heart rate detection module. The earphone battery module is electrically connected to the earphone circuit board. The earphone speaker module is fixedly provided on the earphone part. The heart rate detection module is provided on the earphone part and electrically connected to the earphone circuit board. The heart rate detection earphone has more diverse functions and meets special needs of users.

The invention discloses a heart sound, electrocardio and echocardiogram multi-modal data phase synchronization method, which belongs to the technical field of medical image processing, and comprises the following steps: A1, obtaining an original echocardiogram video and independently recorded heart sound audio data information; a2, preprocessing the acquired data and performing sample automatic construction; a3, a double-path neural network architecture is constructed, electrocardiogram waveform extraction is carried out, and a digital electrocardiogram signal sequence is obtained; a4, periodic feature points of the heart sound and the electrocardio are recognized based on the digital electrocardio signals; and A5, aligning the asynchronous data based on linear phase resampling. According to the method, the video frames, the electric signals and the heart sound signals under different sampling rates are forcibly mapped into the unified phase coordinate system through a linear interpolation algorithm, absolute synchronization of the video frames, the electric signals and the heart sound signals in cardiac pulsation physiological logic is finally achieved, and the influence of individual heart rate differences on data alignment is eliminated.

The invention discloses a VR emotion quantitative management method and system based on physical and psychological interaction, and relates to the field of psychotherapy, and the method comprises the steps: collecting the heart rate variability data, the electrodermal response data and the EEG alpha wave power data of a user in a resting state, and obtaining the EEG alpha wave power data of the user based on the heart rate variability data and the electrodermal response data; generating color, density and physical boundary parameters of the initial emotion energy field; and rendering a dynamic emotion energy field in the VR environment according to the initial emotion energy field parameters, and detecting the distance change between the user and the dynamic emotion energy field through a field density growth algorithm. According to the method, heart rate variability, electrodermal response and EEG alpha wave data are fused by adopting an S-shaped curve function to generate emotion energy field parameters, accurate mapping of physiological states and virtual environments is established, emotion recognition sensitivity is improved, treatment efficiency is improved, field density attenuation rate is dynamically controlled innovatively through parasympathetic nerve activation degree, and the treatment effect is improved. And the synergistic effect of behavior intervention and nerve regulation is realized.

A human body breathing and heartbeat signal estimation method based on a multi-channel frequency modulation continuous wave millimeter wave radar (FMCW) is characterized by comprising the following steps of (1) obtaining an intermediate frequency signal of a multi-channel frequency modulation continuous wave millimeter wave radar (FMCW) system, (2) carrying out distance dimension FFT and phase extraction and unwrapping on a Chirp intermediate frequency signal channel by channel, and (3) carrying out phase extraction and unwrapping on the Chirp intermediate frequency signal channel by channel. (3) calculating signal energy of interested breathing and heartbeat frequency bands in the phase time sequence under all distances, and weighting amplitudes of all distance dimension FFT under all distances to realize accurate positioning of a human body target, and (4) regarding the phase time sequence under the accurate distance and the adjacent distance of each channel as potential multipath signals, and performing fusion enhancement on the signals to realize accurate positioning of the human body target. And (5) processing the enhanced phase time sequences of the channels on the basis of multivariate variational mode decomposition, separating and estimating respiratory signals and heartbeat signals of the human body, and calculating an average respiratory rate and an average heart rate in an observation time period.

The application belongs to the technical field of smart home, wearable device, biological signal processing and artificial intelligence control, and specifically discloses a self-adaptive audio adjustment sleep-aiding method based on sleep state recognition, which comprises the following steps: a non-invasive touch interaction mode is introduced to simplify the starting process of the sleep-aiding process; key physiological indexes such as the heart rate and body movement of a user are continuously monitored, and subtle changes in the data are analyzed to determine the physiological state of the user and the reaction of the user to environmental changes, so that the change in the acoustic environment is ensured to be always within the comfortable range that can be accepted by the user; the amplitude of the volume adjustment is controlled to be below the perception threshold of the user, and a special disturbance observation window is set to evaluate the physiological influence of each fine adjustment operation, so that inappropriate adjustment can be found and cancelled in time before substantial interference is caused. The application significantly improves the reliability of the user experience and sleep-aiding effect.

The invention provides a group psychological data fluctuation early warning method based on VR emotion data processing, which comprises the following steps: acquiring multi-modal physiological signals such as electroencephalogram, heart rate variability and skin conductance of a user and behavior data such as voice, expression and virtual trajectory in real time, performing sliding window processing and feature extraction, fusing into multi-dimensional features, inputting the multi-dimensional features into an emotion recognition model, and performing emotion recognition on the emotion recognition model; generating an individual emotion dynamic sequence; a dynamic heterogeneous graph is constructed by combining the social relation and the emotion similarity, the emotion influence intensity between nodes is judged by using a dual-channel graph attention mechanism, and a propagation path is traced through an integral gradient method, so that interpretable group emotion diffusion mode recognition is realized; according to the method, a typical propagation mode and a time sequence convolutional network are combined, a group emotion intensity evolution trend is predicted, when fluctuation exceeds a threshold value, an emotion propagation path thermodynamic diagram is automatically generated and early warning is pushed, and the interpretability of an emotion propagation path and prediction and response efficiency of group psychological fluctuation are improved.

The invention belongs to the technical field of music recommendation, and provides a personalized music recommendation method, device, equipment and medium based on emotion perception, and the method comprises the steps: collecting user motion monitoring parameters and physiological state data, and carrying out the personalized motion rhythm calculation to obtain a user motion rhythm model; according to the physiological state data and a user exercise rhythm model, heart rate frequency band-beat synchronous matching and intelligent index hierarchical management are carried out, and a music index candidate pool is obtained; performing multi-scale wavelet transform decomposition on the physiological state data stream, and performing user personalized emotion perception to generate a real-time emotion sub-state; performing exercise emotion trend prediction on the user exercise rhythm model according to the real-time emotion sub-state to generate a user exercise emotion situation map; and performing multi-target music recommendation optimization processing from the music index candidate pool according to the user motion emotion situation map to obtain a recommendation result. The method has the beneficial effect that the personalized music recommendation accuracy is improved.

The invention discloses a video pulse wave extraction method based on progressive self-supervised learning, and the method comprises the steps: 1, preprocessing a face video clip, and obtaining an original signal; 2, processing the original signal by using a signal enhancement strategy to obtain an enhanced signal, and forming a positive sample pair with the original signal; 3, constructing a deep learning model capable of adaptively quantifying the contribution weight of the global domain of interest; 4, decomposing a video pulse wave extraction task into a signal extraction sub-task and a noise suppression sub-task, constructing a two-stage time-frequency domain joint loss function, and performing progressive self-supervised training; and 5, calling the pre-trained model to recover a high-quality pulse wave signal from the to-be-detected original signal to realize heart rate monitoring. According to the method, the deep learning model can be self-supervised and trained without a true value signal label, video pulse wave extraction is achieved, meanwhile, complex environment noise is restrained, the generalization performance of the model in a real scene is improved, and development of a non-contact health monitoring technology is promoted.

The invention discloses a flight student pressure assessment system based on fusion of heart rate variability and multi-modal data, and relates to the technical field of flight training. The data acquisition module is used for acquiring heart rate variability data, electroencephalogram data, galvanic skin data and behavior data of a flight student, and constructing a data set after preprocessing; the feature extraction module is used for extracting features from the data set and comprises the steps of extracting time domain features and frequency domain features from heart rate variability, calculating phase synchronism of different electroencephalogram channel signals based on a phase locking value, extracting energy features of each frequency band and decomposing a skin electric signal on different time scales based on wavelet transform; and extracting a baseline value and fluctuation amplitude characteristics. According to the method, the limitation of a single index is broken through by integrating the heart rate variability physiological signal and the limb movement behavior data; the system assists students in cognitive pressure, personalized guidance of coaches and mechanism optimization training, promotes flight training to be changed from experience driving to data driving, and provides early warning measures for high-risk flight students.

The invention discloses a sleep staging method and a storage medium, and belongs to the technical field of sleep staging, and the method comprises the following steps: inputting heart rate data into a pre-constructed sleep staging model according to a time sequence, and sequentially outputting sleep staging results; the sleep staging model comprises a KGNN module, a Sleep MLSTM module, a Sleep SLSTM module and an output module; the KGNN module calculates a context vector according to the heart rate data; the SleepMLSTM module calculates the hidden state of the last time step according to the heart rate data, and adds the hidden state with the context vector to serve as the final output feature of the SleepMLSTM module; the SleepSLSTM module takes the final output feature as an input to generate an output feature; and splicing the final output features of the SleepMLSTM module and the output features of the SleepSLSTM module, and inputting the spliced features into an output module to generate a sleep staging result. The method can solve the problems that an existing sleep staging method is difficult to capture complex physiological correlation between heart rate variability characteristics and is difficult to resist the influence of time-varying noise of original heart rate signals.

The invention discloses an SOR optimization system and method for tourism dance immersion experience, and relates to the technical field of tourism culture experience and man-machine interaction. According to the method, real-time collection and structural integration are carried out on multi-modal data of dance actions, music rhythms, visual symbols and audience attention, micro expressions, breathing and heart rates; establishing a dance stimulation-psychological reaction original data set; constructing a narrative rhythm driving curve NDC and a psychological bearing capacity index ELC based on multi-dimensional stimulation fusion and psychological load modeling; a stimulus-bearing offset index SCI, a culture symbol resonance index CSR and an immersion precursor variable index IPV are further calculated, dynamic evaluation and closed-loop regulation and control of dance stimulation, culture symbol presentation and audience immersion states are achieved, and therefore the immersion experience of dance performance, the culture resonance effect and the sense of participation of audience are improved.

The invention relates to the technical field of data monitoring and management, in particular to an intelligent staff health monitoring and management system based on multi-data fusion, which is provided with a data acquisition module, a data identification module, a time domain analysis module, a data analysis module and a data verification module. A heart rate index fluctuation curve is constructed through a data recognition module, a feature time domain monitoring section is marked through a time domain analysis module, whether the staff has health risk tendency or not is judged through the data analysis module, the feature monitoring moment is determined, and the state tendency category of the staff is judged. A data verification mode is selected through a data verification module to judge whether the staff has health risks or not, then feature time domain monitoring sections which are prone to triggering misjudgment are dynamically recognized according to the staff base line, and the health data verification mode is adaptively adjusted according to the state tendency of the staff; and the efficiency and the reliability of the staff health intelligent monitoring and management system are improved.

The invention discloses a heart rate measurement method and system based on time-frequency fusion and dynamic gating attention, and the method comprises the steps: firstly carrying out the preprocessing of an acquired face video sequence, and obtaining a stable input image sequence; thirdly, extracting a three-dimensional feature map and time sequence features which change along with time through a time-frequency convolutional network; a time-frequency fusion module is constructed, complementary time-frequency features are obtained through time-domain convolution and frequency-domain wavelet decomposition, and feature adaptive fusion is realized by using a dynamic gating mechanism; and the local time relevance of the features is enhanced through a local sliding window attention module. And inputting the three-dimensional convolution features into a prediction module, performing regression to generate a remote photoelectric volume pulse wave signal, and performing frequency domain analysis on the signal to estimate the heart rate. According to the method, pulse related signals can be stably extracted under the conditions of illumination variation and slight head movement, and the method has high heart rate estimation precision and environment robustness and can be used for non-contact vital sign monitoring application.

The invention relates to the technical field of remote operation, in particular to a parent-child safety monitoring method based on artificial intelligence emotion synchronization and positioning guarding, which comprises the following steps: acquiring bracelet positioning, heart rate and acceleration, performing time alignment and integration, generating a child real-time state record, and storing the child real-time state record; the method comprises the following steps: acquiring a risk according to a positioning mapping geo-fence, forming a continuous emotional state, recognizing an emotional consistent section and positioning residence in combination with a track, calculating a continuous time difference, determining a concerned section according to a threshold value, and writing start-stop information according to a time axis to form a guard record. Spatial risks and emotion levels are fused to form a stable time sequence state, a concernable section is recognized by combining emotion consistency and positioning residence, quantitative judgment is carried out according to a continuous time difference value, guard attention is converted into traceable section record from instantaneous alarm, and monitoring judgment accuracy and continuous reference value are improved.

The invention discloses a personalized exercise prescription generation method fusing deep learning, learnable similarity and multi-modal time sequence analysis. The method comprises the steps that firstly, static signs and health risk labels of a user and dynamic signals such as the heart rate, the HRV and the step number collected by wearable equipment in real time are integrated, and unified modeling is conducted through a multi-modal encoder to form individual state vectors; then outputting a preliminary motion scheme by a BERT-Transform text generation model, mapping a user to a hidden space taking a historical execution effect as supervision by using a comparative learning network, and accurately inferring an FITT core parameter through approximate nearest neighbor retrieval and effect weighting; performing consistency optimization on texts and parameters through a large language model, completing risk verification under a safety barrier constructed by a medical rule base, ensuring that output meets clinical taboo and individual tolerance, and finally continuously optimizing long-term health income under medical constraints in combination with an offline reinforcement learning strategy, and cooperating with meta-learning cold start and uncertainty estimation, so as to achieve the purpose of improving the safety of the health management system. Safe, accurate, self-adaptive and explainable personalized exercise prescription generation for new and old users is realized, and the method can be widely applied to intelligent fitness, chronic disease exercise intervention and digital health management scenes.

The invention relates to a multi-sensor fused vital sign and motion posture monitoring system and method. The system comprises a photoelectric volume pulse wave acquisition module, an electrocardio acquisition module, an inertia measurement module, a synchronization module, an artifact suppression module, a contact state recognition module, a data quality evaluation module, a vital sign extraction module and an anomaly detection module. Synchronously acquiring a photoelectric volume pulse wave signal, an electrocardiosignal and motion information; suppressing motion artifacts in the photoplethysmography signal and / or the electrocardiosignal based on the motion information; identifying the contact state and generating a data quality indicator related to the measurement reliability in combination with the de-artifact physiological signal; when the data quality meets a preset condition, heart rate, oxyhemoglobin saturation and / or breathing information are extracted, and vital sign parameters are formed; and carrying out conjoint analysis on the artifact-removed physiological signal and the motion information, and marking heart rate rhythm abnormity and / or posture abnormity. According to the invention, the reliability and availability of vital sign monitoring can be improved.

The application provides a body temperature measurement method and an electronic device, which can improve the accuracy of the measured body temperature. The method comprises: acquiring a target parameter, the target parameter comprising motion data of a user and a current heart rate value of the user; wherein the motion data is used to determine the current motion state of the user; determining the body temperature of the user according to the current motion state of the user and the current heart rate value of the user, and reminding the user of the body temperature.

The invention discloses a smart watch arrhythmia early warning method and system based on deep learning. The method comprises the steps that firstly, PPG signals are collected through a photoelectric volume pulse wave sensor, and denoising and standardization processing are conducted through an adaptive filtering algorithm; then, the heart beat position is recognized through a wavelet transform peak value detection algorithm, and a heart rate variability feature sequence is obtained; converting the one-dimensional heart rate variability feature sequence into a two-dimensional heart rhythm image by using an improved Gramian angular field transformation algorithm; and training an arrhythmia classification model by adopting a ResNet-50 convolutional neural network to realize automatic identification of different types of arrhythmia. And finally, continuous monitoring and timely alarming are realized through a sliding window technology and an early warning mechanism. The key technical problems of low detection precision, poor real-time performance, high equipment cost, poor user experience and the like in the existing arrhythmia detection technology are effectively solved, and an innovative solution is provided for heart rhythm health monitoring of the intelligent wearable equipment.

The invention discloses a children propofol anesthesia depth monitoring method based on permutation entropy and heart rate change, and belongs to the technical field of anesthesia depth monitoring, and the method comprises the following steps: extracting a children electroencephalogram, and carrying out preprocessing and spectral analysis on the children electroencephalogram to obtain permutation entropy; child heart rate data are extracted and processed to obtain the heart rate change degree; inputting the permutation entropy, the heart rate change degree and the age into the mixed prediction model to obtain a composite index, and indicating the narcotic depth of the propofol for the children. According to the children propofol anesthesia depth monitoring method based on the permutation entropy and the heart rate change, the mixed prediction model is constructed by combining the electroencephalogram permutation entropy, the heart rate change degree and the child patient age, and higher accuracy, real-time performance and reliability are shown in children propofol anesthesia depth monitoring; the anesthesia state change can be quickly responded, and the problem of misjudgment caused by age dependence and insufficient linear processing in the prior art is effectively solved.

The invention discloses an emotion classification method and system for heart electromagnetic signals of heart rate variability based on topology analysis, and relates to the technical field of biomedical signal processing and emotion calculation, and the emotion classification method comprises the following steps: preprocessing magnetocardiogram signals or electrocardiogram signals; positioning R waves, extracting an RR interval sequence, and extracting statistics of topological feature points of the persistent graph as topological features; and performing fusion through the time sequence branch, the topological branch and the fusion layer to generate a fusion vector, and classifying the emotional state based on the fusion vector. According to the method, the signal quality and the anti-interference capability are remarkably improved, topological data analysis (TDA) is innovatively introduced, quantitative characterization of a heart rate variability nonlinear dynamic structure is achieved, and the capturing capability of the model to a key physiological mode is enhanced; the problem of overfitting under small samples is effectively relieved, the accuracy, robustness and cross-individual generalization ability of emotion classification are remarkably improved, and a new method is provided for emotion calculation.

1. The name of the design product: electronic device heart rate state curve display graphical user interface. 2. The use of the design product: for electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: for the curve display of the user's heart rate state, the corresponding information parameters can be adjusted by clicking the plus and minus signs in the front view of each design. 7. Other circumstances that need to be explained: the coated area in the view is the content picture, and X represents replaceable text.

The application discloses a body-building rowing machine, which comprises a handle, a transmission mechanism, a water ball, a base, a support frame, a hinged frame, a display mechanism and a seat, the handle is connected with the transmission mechanism, the handle comprises a connecting rod and handles connected at two ends of the connecting rod, a first electrode is inserted into the handle, an end of the first electrode is electrically connected with a PCB, the PCB is arranged in the connecting rod, a battery compartment is arranged in the handle, a battery is arranged in the battery compartment, and the battery supplies power to the PCB, a plurality of color lamp beads are arranged in the water ball, and the PCB controls the light-emitting color of the lamp beads according to a wireless signal emitted by the electric signal of the first electrode. Compared with the prior art, the body-building rowing machine has the beneficial effects that the electric signal on the surface of the hand skin is transmitted to the PCB through the first electrode, the heart rate is calculated by the PCB, the heart rate is transmitted through a wireless signal and displayed in the display mechanism, meanwhile, the light-emitting color of the lamp beads in the water ball is controlled according to the heart rate, and the health condition of the user is prompted.

The utility model discloses a kind of postoperative atrial fibrillation monitoring alarm systems, it is related to the technical field of atrial fibrillation monitoring, and it includes: main control module, detection module, storage module, display module, input-output module and alarm module;Detection module detects the electrocardiosignal, pulse voltage signal and heart sound signal of postoperative patient, sends to main control module;Main control module carries out sampling to electrocardiosignal, pulse voltage signal and heart sound signal, and stores to storage module;According to the instruction of input-output module, the signal after selection sampling is displayed through display module, and the signal stored is selected from storage module and is displayed through display module;Threshold comparison unit of main control module sends signal to alarm module according to heart rate signal, pulse signal, heart sound signal and threshold value, and alarm module sends alarm prompt.The utility model can monitor the sign data of postoperative patient and send alarm, remind medical staff to handle in time, reduce the influence of postoperative atrial fibrillation on patient recovery.

The invention provides a cardiac postoperative respiratory function monitoring system and method. The method comprises the steps that photoelectric volume pulse wave signals of a patient during cardiac postoperative breathing are collected through an intelligent optical sensor; extracting a heart rate variability signal of the patient, and determining a quantitative index of respiratory sinus arrhythmia of the patient through the heart rate variability signal; determining the arterial pressure variability of the patient according to the arterial pressure waveform, and fusing the arterial pressure variability and the quantitative index of the respiratory sinus arrhythmia to obtain the cardiopulmonary coupling strength; according to the cardiopulmonary coupling strength and the shallow and fast breathing index of the patient, combining a pre-trained failure prediction model during breathing machine withdrawal, outputting a failure risk value during machine withdrawal of the patient; and outputting a machine withdrawal decision support report of the patient according to the failure risk value and a preset individualized early warning threshold value. According to the scheme, the cardiac postoperative withdrawal risk can be evaluated based on the dynamic cooperation mechanism between the cardiovascular system and the respiratory system.