6818results about "Respiratory organ evaluation" patented technology

A system for remotely monitoring and managing health statuses of a plurality of users includes software instructions storable on a memory device usable by a computing device, the software instructions causing a hardware processor of the computing device to receive a plurality of sets of health-related information from a plurality of mobile computing devices of a plurality of users. The health-related information includes physiological information derived from wearable devices of the users indicative of an onset of symptoms associated with an infection, contact tracing data, and diagnosis data. The hardware processor determines exposure levels based on at least the contact tracing data, and determines user-specific risk states based on physiological information, diagnosis data, and exposure levels.

A system is disclosed for continuous and intermittent monitoring of physiological parameters. The system comprises at least one sensor coupled to a wearable device configured to measure physiological parameters of a user; a non-transitory data store storing data collected from the at least one sensor and computer-executable instructions; and a processor in communication with the at least one sensor and the non-transitory data store, wherein the computer-executable instructions, when executed by the processor, configure the processor to: continuously measure the physiological parameters by sampling the at least one sensor at a sampling rate to obtain repeated measurements at series of intervals configured to capture important physiological events; compare measurements of the physiological parameters to a threshold to determine a likelihood of a physiological event; and alert the user when an occurrence of the physiological event is likely.

The invention provides a field intelligent injection pump automatic control method and system based on physiological parameter monitoring, and relates to the technical field of automatic control. The physiological parameter information is input into an intelligent decision module, a personalized drug administration scheme is generated based on a reinforcement learning algorithm in combination with a physiological function state report and a drug action mechanism knowledge base, and the personalized drug administration scheme comprises a drug ratio, an injection rate and an injection time sequence; the personalized administration scheme is sent to the intelligent injection module, and the injection step is executed; the energy management unit performs energy collection and storage through a flexible photovoltaic film and a phase change energy storage material, establishes a multi-objective optimization function based on drug storage conditions and environmental parameters, and performs intelligent distribution and adaptive adjustment on the power consumption of the injection pump system; the state of the injection pump pipeline is evaluated based on multi-mode sensing information, dynamic safety evaluation is conducted in combination with real-time physiological monitoring, and graded protection is triggered.

The invention discloses an intelligent exercise health assessment method and system based on multi-sensor fusion, and relates to the technical field of intelligent health assessment and monitoring, and the method comprises the steps: collecting multi-modal exercise data and physiological signals, and carrying out the preprocessing of the collected data; carrying out data fusion and extracting key motion and health features; constructing an intelligent exercise health evaluation model, and analyzing the health state and the exercise performance of the user; personalized health feedback is generated based on an evaluation result, and real-time early warning is provided; and dynamically updating parameters of the evaluation model, and performing model optimization according to a specific motion scene and health requirements of the user. According to the method, various sensor data can be effectively fused, accurate health scores and real-time early warning can be generated, the health requirements of the user in different exercise scenes are met, the accuracy, adaptability and intelligent level of evaluation are remarkably improved, meanwhile, health improvement suggestions with high pertinence are provided for the user, and the user experience is improved. The method has remarkable technical advantages and application value.

A sensor system for monitoring patients is provided. The sensor system includes a wireless charging dock, one or more patient sensors, and a processing module. The patient sensor is configured to collect patient physiological data and send the data to the processing module. The processing module wirelessly transmits the patient physiological data to a patient monitor system. The wireless charging dock is wirelessly and removably coupled to the processing module to wirelessly provide power for the processing module. The wireless charging dock is magnetically coupled to the processing module.

The invention relates to the technical field of flight training evaluation, and discloses an electroencephalogram feature and physiological index fused flight training evaluation system. The system comprises a physiological signal acquisition module which synchronously captures multichannel electroencephalogram original signals and body surface physiological index data, and the body surface physiological index data comprises an electrocardiograph R-R interval sequence, respiratory wave frequency amplitude and galvanic skin response amplitude; the multi-modal fusion module is used for analyzing an electrocardiograph R-R interval sequence to generate a heart rate variability feature vector and establishing dynamic association mapping of an electroencephalogram entropy value and a physiological feature vector; the cognitive state modeling module is used for generating a cognitive load index according to the dynamic association mapping and constructing a cognitive stability quantization matrix; the self-adaptive feedback module is used for receiving related data and dynamically adjusting simulated flight scene parameters; and the evaluation output module is used for integrating the data to generate a comprehensive training evaluation report containing a neurophysiological coordination degree score and an operation accuracy rating. According to the system, comprehensive evaluation and dynamic training adjustment of the cognitive state of the pilot are realized.

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 dynamic monitoring system for the senile cardiac patient comprises a multi-mode sensor module, a data processing module, an analysis module and an alarm module. The multi-modal sensor module collects parameters to generate a multi-modal physiological signal; the data processing module generates a preprocessing signal; the analysis module performs real-time analysis on the pre-processed signal based on a preset elderly electrocardiogram characteristic model to generate a graded alarm instruction; the alarm module triggers corresponding local reminding, remote notification or first-aid linkage response according to the graded alarm instruction, wherein the first-aid linkage response automatically sends the patient position and key physiological data to a specified first-aid center through the first-aid communication module; wherein the self-adaptive adjustment of the multi-mode sensor module comprises the step of dynamically optimizing signal gain according to a skin contact state. According to the dynamic monitoring system for the senile cardiac patients, the problems that dynamic monitoring of the senile cardiac patients is poor in real-time performance, high in false alarm rate and delayed in first-aid response can be solved.

The invention relates to the technical field of health monitoring, and discloses a patient health monitoring method and system based on dynamic electrocardiogram analysis, and the method comprises the steps: synchronously obtaining multi-lead dynamic electrocardiogram data continuously collected by a patient within a preset duration, and related physiological parameters of exercise intensity, respiratory rate and body position change; performing dynamic self-adaptive preprocessing on the dynamic electrocardiogram data based on the associated physiological parameters to obtain standardized electrocardiosignals; extracting a multi-dimensional characteristic parameter set from the standardized electrocardiosignal, and sampling according to a preset time window to generate a characteristic parameter sequence with a timestamp; and inputting the characteristic parameter sequence into a dynamic optimization analysis model capable of iteratively updating parameters through real-time physiological parameter feedback, and performing graded evaluation on the health state of the patient to generate an evaluation result. The system corresponds to the method. By adopting the method and the system, the accuracy of dynamic electrocardiogram monitoring and the evaluation refinement level meet the dynamic monitoring requirement of cardiovascular health.

The invention provides a PPG optical sleep and respiration monitoring method and system based on a gravity sensing intelligent ring, and the method comprises the steps: synchronously collecting a PPG signal and a three-dimensional gravity acceleration signal of a hand through the intelligent ring, carrying out the adaptive filtering of the PPG signal, extracting a time-domain pulse wave and a frequency-domain heart rate variability feature, and carrying out the detection of the PPG signal. Body movement intensity and body position change frequency are calculated according to the gravitational acceleration signals, and body movement feature vectors are generated. And then fusing the pulse wave characteristics, the heart rate variability characteristics and the body movement characteristics, constructing a multi-dimensional physiological parameter matrix, analyzing and dividing sleep stages based on spatial-temporal correlation of the matrix, and detecting abnormal respiratory rhythm. And finally, dynamically adjusting a judgment threshold value of the abnormal respiratory rhythm, and outputting a sleep quality evaluation result and a respiratory event alarm. According to the invention, the accuracy and real-time performance of sleep monitoring can be improved.

The invention provides an intelligent tumor nursing monitoring system. The intelligent tumor nursing monitoring system comprises a data acquisition module, a data processing module, a dynamic evaluation module, a multi-stage early warning module and a self-adaptive control module. The data acquisition module acquires physiological sign data, tumor local image data and body fluid biochemical data of a patient in real time through a multi-source biosensor. And the data processing module performs space-time alignment, noise suppression and cross-modal feature association on the multi-modal data to generate a comprehensive feature map. The dynamic evaluation module calculates the tumor progress rate and the complication risk coefficient based on the map, and generates a personalized risk prediction curve. And the multi-stage early warning module triggers an early warning signal according to the curve and generates a risk traceability report. And the adaptive control module generates a personalized scheme based on the early warning signal and the traceability report and controls the medical equipment to execute closed-loop feedback adjustment. The nursing quality and the treatment effect of tumor patients can be improved, and the medical risk is reduced.

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 instrument control, in particular to a lung biopsy sampling needle stabilizing method and system based on dynamic respiration compensation. The stabilizing method comprises the following steps: acquiring a respiratory movement signal of a patient in real time through a multi-source biosensor, wherein the multi-source biosensor comprises an optical fiber strain sensor array, a diaphragm myoelectricity sensor and a respiratory airflow sensor which are deployed at the chest and abdomen. According to the method, millimeter-microsecond-level accurate and stable control over the lung biopsy sampling process is achieved by establishing a cooperation mechanism of a breathing phase-displacement nonlinear mapping model and a macro-micro composite driving framework. The biological rationality of displacement prediction is improved through deep coupling of multi-mode breathing signals, and the chest wall non-uniform deformation gradient is analyzed in space based on cross topology deployment of an optical fiber strain sensor in an intercostal space area in combination with cross-scale feature fusion of myoelectricity and airflow sensing.

The invention discloses a multi-mode pet health monitoring and motion artifact elimination method and system based on a millimeter wave radar, and relates to the technical field of pet health monitoring, and the method comprises the following steps: S001, building a unified time baseline and an energy fingerprint auditing surface, constructing an energy distribution model from a chest to a tail, and taking the model as a reference for artifact evolution, identifying a signal spectrum coupling trend in a time-frequency domain; and S002, based on the energy distribution model, performing causal playback on continuous time sequence signals acquired by the millimeter-wave radar, extracting a pseudo-motion energy nucleus caused by tail or limb movement, and calibrating a phase anchor point and a space observation area of a respiratory signal. According to the method, multi-source physiological data are fused, pure respiratory signals are extracted through energy distribution modeling, causal playback, phase anchor point calibration and three-dimensional resampling, risk assessment is achieved based on physiological credibility tensor, and the accuracy and anti-interference capacity of health monitoring are improved by combining phase conjugate traction and a space-time regulation strategy.

A medical device controller operating in conjunction with a medical device determines one or more current versions of executable code associated with one or more processors in a medical device. Medical devices may include infusion pumps, other patient treatment devices as well as vital signs monitors. The medical device controller determines one or more current versions of executable code and configuration information associated with the one or more processors in the medical device. The medical device controller further determines which of the processors in the medical device require updated executable code, and which of the processors in the medical device require updated configuration information. The medical device controller distributes to the medical device as required at least one of the updated executable code and the updated configuration information. The medical device deploys the distributed updates, and activates the updates at a clinically appropriate time.

The invention discloses a sleep state monitoring and analyzing system based on multi-sensor fusion, and relates to the technical field of health monitoring, the sleep state monitoring and analyzing system comprises a multi-modal sensor module used for collecting multi-dimensional data related to a sleep state, the multi-dimensional data comprises a bio-electricity signal, a physiological parameter, body movement data and an environment parameter, and the multi-modal sensor module is used for collecting the multi-dimensional data; the multi-modal sensor module comprises a non-contact sensor, a flexible electronic skin sensor and a bio-electricity signal sensor, and the data fusion and processing module is used for carrying out preprocessing, dynamic self-adaptive fusion and federal learning modeling on collected original data. According to an existing contact type sleep state monitoring scheme, more flexible and accurate sleep state data are realized through a non-contact type monitoring sensor in cooperation with dynamic adjustment of sleep monitoring content and adjustment of weights of various sensors for monitoring the sleep state; and a more accurate and intuitive reference report is provided for the sleep state and the health state of the subsequent user.

The invention discloses a medical-level VSM dynamic monitoring system and method, and relates to the technical field of vital sign monitoring, and the system is characterized in that a data processing module is connected with an inertial measurement module and a display and interaction module; the power management module is respectively connected with the inertial measurement module, the data processing module and the display and interaction module; the display and interaction module is used for acquiring basic information of a to-be-monitored user; the sensing module is used for measuring vital sign signals and sending the vital sign signals to the data processing module; the inertia measurement module is used for measuring inertia signals and sending the inertia signals to the data processing module; the data processing module is used for acquiring vital sign parameters of the to-be-monitored user according to the inertial signal and the vital sign signal, acquiring vital sign standard parameters according to the basic information and the inertial signal, and comparing the vital sign parameters with the vital sign standard parameters to generate a monitoring report; and sending the monitoring report to the display and interaction module to output the monitoring report. The security of the personal information of the user can be protected.

A wearable device consists of a smart band and a display unit. The smart band comprises a microbial biosensor, a particulate matter sensor, an enviro sensor, a physiological sensor, a biofluid sensor, a biokinetics sensor, a lifestyle sensor, and a single board computer. The microbial biosensor detects, measures, and monitors microorganisms, and a sterilizer kills pathogens. The particulate matter sensor detects, measures, and monitors a set of suspended particles in the surrounding air. The enviro sensor monitors environmental conditions surrounding the user. The biofluid sensor detects, measures, and monitors biological fluid parameters of the user. The physiological sensor detects, measures, and monitors physiological parameters of the user. The biokinetics sensor detects, measures, and monitors physical activities of the user. The lifestyle sensor detects, measures, and monitors healthy lifestyle activities of the user. The wearable device allows for continuous monitoring of user health for accurate clinical outcomes and wellness programs.

The invention relates to a multi-device cooperative control method and device based on a user state and an air conditioner. The method comprises: monitoring a sleep state of a user; and determining a sleep stage of the user based on the sleep state, and performing cooperative control on each device according to a control strategy matched with the sleep stage. The method and the device can adapt to the difference of dynamic sleep stages, and control the devices to operate cooperatively according to the control strategies matched with the different sleep stages, so that the self-adaptive adjustment and parameter adjustment precision of the devices is improved, and the problems of poor sleep mode adjustment effect and low adaptability in the prior art are solved.

The embodiment of the invention provides a multifunctional vital sign monitoring method and system. The method comprises the following steps: acquiring real-time vital sign signals of a user in different physiological states, and forming a multi-dimensional monitoring data set in combination with environmental parameters; dynamically adjusting the monitoring frequency and precision of real-time vital sign signals by applying an intelligent regulation and control algorithm according to the multi-dimensional monitoring data set, generating a data collection strategy, and collecting vital sign data based on the data collection strategy; based on the vital sign data, an instant evaluation engine is used for analyzing and processing monitored vital sign abnormal conditions, and a health condition evaluation result and personalized health suggestions are generated; and safely synchronizing the health condition evaluation result and the personalized suggestion to a medical monitoring system of the user to realize remote medical service and continuous health management. According to the technical scheme provided by the embodiment of the invention, the accuracy and efficiency of health monitoring are improved.

The invention provides a system and method for assisting rehabilitation in sleep, and the system comprises an electroencephalogram monitoring assembly, a heart rate and breath monitoring assembly, a myoelectricity monitoring assembly and a body movement monitoring assembly which are used for obtaining physiological signals of an object; the intervention component is used for performing predetermined intervention operation when being activated; the control component is used for determining a sleep stage according to the physiological signal of the object; when the sleep stage is a deep sleep stage, determining an intervention priority score according to a day rehabilitation training target, a focus position and a stability score of the current deep sleep stage of the object; when the intervention priority score is larger than or equal to a preset threshold value, the object is in the deep sleep period at present, and the stability of the current deep sleep period meets a preset stability condition, a corresponding intervention assembly is activated. By means of the technical scheme, accurate intervention can be achieved in the sleep process, and the rehabilitation process of a patient is accelerated by effectively connecting intervention in the sleep stage with a rehabilitation training target in the daytime.

The invention provides an anonymization processing method and system for emotion data in a vehicle. The anonymization processing method comprises the steps of collecting multi-mode emotion data; correspondingly carrying out local preprocessing and multi-modal alignment processing on the multi-modal emotion data; corresponding sensitive emotion feature information in the preprocessed multi-modal emotion data is extracted through a lightweight recognition algorithm, and the sensitive emotion feature information at the recognized position is packaged in a unified mode; performing desensitization processing on various types of emotion modal data, and encapsulating and synchronizing desensitization results with labels; deep emotional feature extraction is performed on the image, the voice and the physiological signal through a multi-modal fusion model, and the extracted three types of deep features are fused to obtain an emotional representation vector; and inputting the emotion representation vector into a pre-trained emotion recognition model, and outputting the current emotion state of the passenger, including the specific emotion category and the corresponding confidence coefficient. According to the method, emotion analysis and transmission are performed after data anonymization is realized, and accurate judgment of the system on the emotion state is not influenced while privacy security of the user is ensured.

The invention relates to a data fusion processing method and system for an intelligent sensor based on MEMS, and the method comprises the following steps: carrying out the non-stationary signal decomposition of a multi-modal physiological parameter time sequence data stream, and obtaining a key vital sign feature sequence; performing phase coupling degree calculation on the key vital sign feature sequence to obtain an inter-organ function correlation intensity matrix; performing high-dimensional feature space projection on the critical patient based on the inter-organ function association intensity matrix to obtain a pathological state feature map of the patient; performing nonlinear kinetic analysis on the pathological state characteristic spectrum of the patient to obtain a disease progress dynamic trajectory; and performing clinical decision support reasoning based on the disease progress dynamic trajectory to obtain personalized treatment suggestions, thereby solving the technical problem that the existing analysis method is often limited to observation of a single physiological parameter or simple statistical analysis and lacks the ability to understand the disease mechanism from the overall perspective.

The invention relates to the technical field of disease risk assessment, in particular to a prediction support system for a high risk level of a sepsis patient. According to the abnormal degree of the physiological data in the abnormal time period of each category and the time interval between the abnormal time period and the adjacent abnormal time period, the importance degree of each category is obtained; selecting a feature category and a target time period based on the importance, and obtaining the severity of the target time period according to the importance difference between all categories in the target time period and the feature category and the number of categories with abnormal physiological data in the target time period; and acquiring a high risk value according to the change trend and the time interval of the severity of the target time period and the adjacent target time period, and adjusting and analyzing all types of physiological data in the time period by using the high risk value so as to predict the high risk of sepsis of the to-be-detected patient. According to the method, the difference of the physiological data in the time period in which the abnormal expression has the difference is dynamically expanded through the high-risk value, so that the accuracy of predicting the high-risk condition of sepsis is improved.

An image processing apparatus is disclosed. The image processing apparatus of the present invention comprises: an image receiving unit for receiving a first image and a second image of the same object taken at different times; a processor for obtaining transformation information by registering the first image on the basis of the second image, obtaining a first segment image corresponding to an area of the object from the first image, and generating a second segment image corresponding to an area of the object of the second image by transforming the obtained first segment image according to the transformation information; and an output unit for outputting the second segment image.

The invention discloses an operating driver fatigue detection method based on multi-source information fusion, and relates to a data acquisition module, a data analysis processing module, a fatigue state classification module, a fatigue state real-time detection and evaluation module and a fatigue early warning module, and the fatigue state classification module is used for constructing a fatigue state classification model. The fatigue state real-time detection and evaluation module is connected with the data analysis and processing module and is used for summarizing real-time driver physiology, driving behaviors and external environment characteristics; and the lower connection fatigue state classification module inputs the multi-source indexes into a fatigue state classification model to obtain a fatigue state grade. And the fatigue early warning module is used for issuing early warning information to the driver according to the fatigue state grade of the driver so as to relieve the fatigue of the driver. And an automatic driving module of the vehicle is started when necessary, so that the driving safety of a driver is protected. According to the invention, real-time monitoring, evaluation and prevention of the fatigue of the operating driver are realized.

The invention provides a small sample respiratory disease pattern recognition method based on a diffusion model. The method comprises the following steps: firstly, for common respiratory disease types, collecting and classifying data of three respiratory modes: normal respiration, pause respiration and deep respiration which respectively correspond to respiratory diseases such as normal respiration, obstructive apnea and asthma in medicine; secondly, learning an internal distribution rule of data by using a radio frequency diffusion model, and generating diversified and high-quality radio frequency time sequence data on the basis of limited real samples so as to expand a data set; and thirdly, combining the generated data with real data, extracting an accurate respiration signal through a one-dimensional time sequence extraction algorithm, and converting a time sequence into a two-dimensional image by utilizing a Gramian Angle Field (GAF) method, so that the time dependency relationship of the time sequence is intuitively presented in the image. And finally, a convolutional neural network (CNN)-Transform deep learning model is adopted to carry out feature extraction and classification on the image data, and then multi-scale feature analysis and breathing mode recognition are carried out. According to the method, the recognition precision and generalization ability of the model are remarkably improved under the small sample condition, and an accurate and reliable solution is provided for non-contact breathing mode recognition.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.

The invention discloses a multi-unit radar collaborative motion state and vital sign monitoring system and a multi-unit radar collaborative motion state and vital sign monitoring method. The system comprises an edge sensing node and an upper computer which are in communication connection, the edge sensing node has complete vital sign wireless sensing, signal resolving and communication capabilities; the upper computer is used for receiving information such as a target ID, a target position, a target vital sign and a target abnormal state sent by the edge sensing node; the edge sensing node is composed of a microcontroller module, a radar sensor module, a DSP module, an FPGA neural network reasoning module, a communication module and a power supply module. The microcontroller module is in communication connection with the radar sensor module, the DSP module, the FPGA neural network reasoning module and the communication module. The radar sensor module is in communication connection with the DSP module; the DSP module is in communication connection with the FPGA neural network reasoning module; the power supply module supplies power to the edge sensing nodes. According to the method, accurate tracking of multi-target vital signs and positions is carried out, and accurate tracking of multi-target health states is realized.

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.