2278 results about "Vital signs" patented technology

The invention relates to the technical field of physiological information monitoring, in particular to a non-contact vital sign data monitoring method and system.The non-contact vital sign data monitoring method comprises the following steps that on the basis of a millimeter wave radar sensor, thoracic cavity micro-motion data of a target individual are collected in real time, thoracic cavity motion signals caused by heartbeat and respiration are separated, and the respiration frequency and change are recognized; and breathing mode data is obtained. According to the method, chest micro-motion and infrared radiation data of a target individual are collected and analyzed in real time through a millimeter wave radar sensor and an infrared sensor, accurate monitoring of vital signs is achieved, the abnormal detection threshold value is adjusted in combination with the physiological state of the individual, and the monitoring result fits the actual health condition of the individual; through joint analysis of a plurality of vital signs, comprehensive evaluation of the health state is realized, in combination with health trend prediction, data support is provided for preventive health management and early disease diagnosis, the practicability of a monitoring system is improved, and the application range of a non-contact monitoring technology in a complex environment is expanded.

A driver or occupant health and alert monitoring and assistance system and associated techniques or methods include, among other features, monitoring vital signs of the driver and when these vital signs are determined to be outside of a predetermined range or pattern, alerting the driver via an infotainment system of a potential abnormal health condition and requesting a response from the driver regarding the same. Should the driver not respond within a predetermined period of time, the techniques further provide for taking emergency corrective action including autonomously slowing down the vehicle to a stop, alerting the emergency 911 system of the driver's situation, and / or alerting predetermined emergency contacts of the driver's potential abnormal health condition and vehicle state.

The present application discloses a multifunctional health monitoring method and system based on a millimeter wave radar, and a computer readable storage medium. The method comprises: receiving an echo signal in a space, and on the basis of the echo signal, calculating point cloud data of a user and phase change data caused by the respiration and heartbeat of the user; calculating behaviors of the user on the basis of the point cloud data; calculating vital signs of the user on the basis of the phase change data, wherein the vital signs comprise respiration rate and heart rate; and on the basis of the behaviors and / or the vital signs, determining that the user triggers a set alarm event, and on the basis of the type of the triggered alarm event, performing a corresponding alarm operation.

The invention discloses a cardiothoracic surgery postoperative complication assessment method and system based on data analysis, and relates to the technical field of medical treatment, and the main scheme is as follows: collecting basic operation data, preoperative examination data, basic information data of a patient and real-time postoperative recovery data of the patient, calculating the complexity of the operation according to the basic operation data, and determining the postoperative complication of the patient according to the calculated complexity; the method comprises the steps of calculating preoperative complication indexes through preoperative examination data, calculating basic information indexes through patient information data, calculating vital sign comprehensive indexes through real-time postoperative recovery data, jointly calculating complication risk scores through the indexes, comparing the complication risk scores with a complication risk score threshold set, and then judging risk levels. And formulating a management strategy and generating an evaluation report. According to the system, the accuracy and systematicness of postoperative complication evaluation are improved, and powerful support is provided for clinical management.

The invention discloses a patient nursing strategy optimization system for nursing in the department of hepatobiliary and pancreatic, and relates to the technical field of nursing strategy optimization. The nursing strategy optimization platform is in communication connection with a patient information management module, a knowledge base module, a nursing evaluation module, a nursing strategy plan making module, a nursing implementation module, a communication and cooperation module and a nursing effect evaluation module. According to the patient nursing strategy optimization system for nursing in the department of hepatobiliary and pancreatic, data in multiple aspects of physiology, psychology, society and the like of a patient are comprehensively collected and analyzed through the integrated nursing evaluation module, and key indexes such as vital signs, pain degree, nutritional status, sleep quality and the like are monitored and evaluated in real time; in addition, a personalized nursing plan provided by the system is combined with specific requirements and illness state changes of the patient, so that the nursing service better fits the actual condition of the patient.

The invention discloses a wounded rescue life detection and positioning method based on deep learning. The method comprises the steps that S1, multi-view RGB-D images, laser radar point clouds, millimeter wave vital sign radar data and infrared thermal image data are collected and subjected to time synchronization; s2, outputting a pixel-level semantic mask; s3, generating a semantic point cloud of the wounded; s4, forming a metric-semantic double-map data set; s5, generating vital sign scores and writing the vital sign scores into a metric-semantic double-map data set; s6, updating wounded dynamic anchor point attributes in the measurement-semantic double-map data set; and S7, based on the updated metric-semantic double-map data set, calculating the three-dimensional coordinate and rescue priority sequence of each wounded dynamic anchor point, and generating an obstacle avoidance optimized navigation path for avoiding the risk semantic region. The rescue efficiency and safety are effectively improved.

The invention relates to the technical field of medical information, in particular to an intelligent prediction model and method for postoperative complications of anesthetized patients, and the method comprises the steps: collecting preoperative to postoperative complete-cycle clinical data of a patient through a medical data interface; analyzing operation codes to generate risk features, extracting vital sign dynamic features, and establishing a complication probability mapping relation through a multi-modal fusion network; combining the complication probability and pharmacokinetic parameters to construct an optimization model, and solving an individualized anesthetic dosage interval by using a gradient descent algorithm; vital signs are dynamically monitored in the operation, a dose re-optimization mechanism is triggered, the infusion rate is adjusted, and a closed-loop control link is formed; and generating a visual decision report. According to the method, through deep integration of complete-cycle clinical data and multi-modal feature modeling, preoperative physiological parameters, operation coding semantic information and intraoperative vital sign dynamic modes are subjected to fusion analysis, a nonlinear mapping relation between dosage and complication probability is constructed, and the risk prediction precision and individualized adaptability are remarkably improved.

The invention provides an AI-based smart old-age care service method, system and device, and a storage medium, and relates to the technical field of smart old-age care. The method comprises the following steps: collecting vital sign data, behavior data and environment data of the elderly, and carrying out standardization processing on past case data to obtain standard case data; performing management fusion on the standard case data, the vital sign data, the behavior data and the environment data to obtain multi-dimensional health data; inputting the multi-dimensional health data into a preset health state evaluation model to obtain health state information; matching the health state information with maintenance knowledge in a preset knowledge base to obtain a plurality of alternative maintenance schemes, and inputting the plurality of alternative maintenance schemes into a preset maintenance effect prediction model to obtain a plurality of maintenance effect prediction results; and taking the alternative maintenance scheme with the highest evaluation score as a target maintenance scheme. Through the alternative scheme, the effect of the pension service is improved.

The invention relates to the technical field of medical equipment and intelligent learning, in particular to a critical patient vital sign dynamic monitoring and intelligent early warning system which comprises a multi-modal data acquisition module, a medical equipment linkage control module and an intelligent early warning decision module. A dual-channel Kalman filtering state updating model, a biological impedance monitoring model and an anti-interference and signal optimization model are established in the multi-modal data acquisition module, and multi-dimensional physiological monitoring information of the critical patient is obtained through the models; a physiological information standardization model and a safety control mechanism are arranged in the medical equipment linkage control module, and then multi-dimensional physiological monitoring information is cooperatively controlled to output multi-modal vital sign data; and the intelligent early warning decision module receives and analyzes the multi-modal vital sign data to obtain power spectral density, and predicts the disease development condition of the critical patient in combination with the depth prediction model and the multi-modal vital sign data. All the modules work cooperatively to achieve dynamic monitoring and intelligent early warning of vital signs of critical patients.

The invention discloses a vital sign and sleep monitoring method and system based on a millimeter-wave radar, the method utilizes the millimeter-wave radar to realize non-contact sleep monitoring, and the millimeter-wave radar is firstly used to collect radar echo signals obtained by vibration of a chest wall in a sleep state; determining the position of a monitored object and extracting a vital sign signal of the target; then accurately detecting a first amplitude body movement and a second amplitude body movement of the target; whether an apnea event happens to the target is detected in real time, and alarm information is prompted under the condition that the event happens for a long time; three sleeping postures of supine, side-lying and prostrate are detected; and finally, sleep state monitoring is formed, a user sleep quality analysis report is formed in combination with the monitored information, and health suggestions are given. The method is simple, convenient, accurate, economical and efficient, is suitable for various application scenes such as families, hospitals and nursing homes, and provides all-weather health monitoring services for users.

The invention relates to an intensive care cross-institution collaborative data processing method, which comprises the following steps of: defining a hierarchical data model based on the intensive care field, and modeling various types of information including vital signs, clinical diagnosis and laboratory detection results of a patient according to a time sequence; a self-adaptive standardized interface is designed, and data formats from different hospitals or devices are identified and converted; real-time dynamic tags are introduced for patient data through a tagging technology; various health data, including vital signs, medical history and treatment feedback, of a critical patient are converted into a multi-dimensional health track, and sequential analysis is used for conducting continuous modeling on the condition of the patient; a deep learning and reinforcement learning method is combined to establish a disease evolution deduction engine; the disease development is predicted according to historical data, and the influence of different intervention measures on the disease is simulated to intelligently recommend a treatment strategy; and according to the dynamic deduction result, generating a plurality of treatment schemes, and dynamically adjusting the intervention strategy according to the real-time response of the patient.

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 discloses a hospital project intelligent management and collaborative operation optimization system based on an AI algorithm technology, and the system comprises a multi-modal data collection module which is used for obtaining hospital operation data, medical equipment data, patient vital sign data and medical staff operation records in real time; the data fusion engine, the dynamic resource scheduling module, the risk prediction unit and the collaborative work platform provide intelligent task distribution, conflict detection and emergency plan generation functions; the self-adaptive optimization controller is used for continuously updating system parameters through an online learning mechanism; the method breaks through the limitation of traditional single-mode data processing, constructs a cross-department data association model through a graph neural network, integrates heterogeneous data sources such as radiation images, inspection reports and equipment logs, realizes dynamic mapping of diagnosis and treatment paths and resource states, introduces a space-time attention mechanism, and improves the diagnosis and treatment efficiency. Equipment operation cycle characteristics and patient movement tracks are subjected to combined modeling, so that the sensing precision of the system is improved to a minute level.

The invention relates to the technical field of data monitoring and analysis, and discloses a hemodialysis care unit extracorporeal circulator monitoring method and system and a storage medium. The method comprises the steps that vital signs and identity features of a patient are collected, a sign feature data set is obtained through fusion processing, and initial dialysis parameters are generated; analyzing the sign feature data set to obtain blood vessel state parameters; adjusting a circulation system based on the blood vessel state parameters and the initial dialysis parameters to obtain circulation control data; monitoring a dialysate path by using the circulation control data, and generating a liquid path operation parameter; the circulation control data and the liquid path operation parameters are integrated to analyze the system state, and equipment early warning indexes are formed; and evaluating equipment operation according to the early warning index and generating an automatic control instruction. According to the method, deep integration of the physical sign characteristic data of the patient and the equipment operation data is realized, a data-driven objective decision basis is provided for the dialysis process, and the safety and effectiveness of dialysis treatment are improved.

The invention relates to the field of intelligent health situation monitoring, in particular to an elderly health situation monitoring and early warning system and device based on digital twinning, and the system comprises a data collection module, a digital twinning module, a risk prediction module and an early warning module. Wherein the data acquisition module acquires physiological and environmental data in real time; the digital twin module constructs an environment and individual model which dynamically reflects the physical state of the old people, predicts the environment risk and adjusts the state of the model; the risk prediction module evaluates health risks according to the vital sign data and a twinborn model; and the early warning module customizes a health management scheme or sends early warning to nursing personnel according to the evaluation result. The system has the advantages of real-time performance and accuracy, realization of personalized health management, effective prediction of environmental risks, timely early warning and presentation of technical advantages. Meanwhile, physiological and environmental factors are comprehensively considered, comprehensive health management support is provided for old people, and health monitoring and risk early warning efficiency is remarkably improved.

The invention discloses pre-hospital emergency medical aid emergency help integrated equipment and system. The method comprises the following steps: S1, acquiring key vital sign data of a patient by using medical equipment; s2, calling basic information and medical history of the patient through equipment; s3, transmitting the collected data to a server of a remote data processing center in real time; s4, performing comprehensive analysis on the received vital sign data and the medical history information of the patient by applying a pre-trained deep learning model on the server, and generating a prediction report; s5, displaying a prediction result on a portable device or a remote monitoring station in real time by using a data visualization technology; s6, based on a visual prediction result and medical history analysis, guiding first-aid personnel to take targeted emergency medical measures; and S7, continuously monitoring the vital sign response of the patient and any health change related to the medical history, and updating the treatment strategy according to the further analysis of the deep learning model.

The invention discloses an automatic input vital sign detection method and system, and relates to the technical field of medical information processing, and the method comprises the steps: collecting vital sign data of a target individual, and carrying out the synchronous processing; a vital sign data set is formed through cleaning and multi-channel data fusion; uploading the encrypted data to a medical information system to finish classified storage and updating of the data; performing feature extraction and analysis to generate a health risk prediction result; health management suggestions are generated, and data display is completed through a dynamic feedback mechanism. According to the method, the privacy and the integrity of the vital sign data are guaranteed through a dynamic updating and verification mechanism in a mode of combining symmetric encryption and asymmetric encryption, and the risks of data leakage, data tampering and version management disorder in a traditional system are overcome. By introducing an intelligent analysis method combining a classification model and a regression model, the limitation of traditional single-dimensional static assessment is solved, and a comprehensive health risk report is generated by integrating logic association among multi-modal feature data.

The invention discloses an AI nursing whiteboard system and method based on a large model agent, the system comprises a nursing whiteboard terminal, an AI intelligent chain platform and a database, the nursing whiteboard terminal comprises an AI nursing assistant, and a whiteboard control system is arranged in the nursing whiteboard terminal. Through the functions of integration of multi-source data, intelligent early warning, accurate question and answer and the like, various diagnosis and treatment data, such as vital signs, medical advices, nursing records and the like, of a patient can be rapidly integrated and presented in a clear and visual mode, a large amount of time and energy are saved, the patient can rapidly know the condition of the patient, and the diagnosis and treatment efficiency is improved. Vital signs and illness state changes of a patient can be monitored in real time, early warning can be immediately given out once abnormal conditions such as heart rate and blood pressure exceeding normal ranges occur, a nurse can know the abnormal conditions in the first time and take corresponding measures, the response speed to emergency situations of the patient is greatly increased, and when the nurse encounters a question in the aspect of medical knowledge, the nurse can timely take corresponding measures. Inquiry may be made by an AI care assistant.

The invention discloses a multi-modal emotion analysis method and system, and the method comprises the steps: carrying out the heart rate variability analysis of a collected vital sign signal, so as to extract a time domain feature, a frequency domain feature and a nonlinear feature corresponding to a heart rate variability signal obtained after the analysis, fusing the time domain feature, the frequency domain feature and the nonlinear feature to generate a heart rate variability feature corresponding to the vital sign signal; calculating a power spectrum and a power spectrum entropy corresponding to the collected electroencephalogram signals, and extracting electroencephalogram characteristics corresponding to the electroencephalogram signals according to the power spectrum and the power spectrum entropy; extracting skin electric characteristics corresponding to the collected skin electric signals; respectively inputting the heart rate variability feature, the electroencephalogram feature and the galvanic skin feature into a pre-trained emotion recognition model, and outputting a plurality of emotion analysis results; and acquiring an emotion regulation scheme matched with each emotion analysis result to output a corresponding emotion regulation strategy, thereby improving the accuracy of the emotion analysis and regulation strategy.

The invention discloses a micro-pressure oxygen cabin control system and a control method thereof, and relates to the technical field of medical treatment, and the system comprises an oxygen cabin operation monitoring module which is used for monitoring and obtaining information data of a micro-pressure oxygen cabin in real time during working, comprise the running state of the micro-pressure oxygen cabin, the air pressure value in the oxygen cabin, the oxygen concentration in the oxygen cabin and the vital sign state of a user in the oxygen cabin; and a data processing and analyzing module. According to the micro-pressure oxygen cabin control system and the control method thereof, a data processing and analysis module, especially a coupling relation analysis unit, is introduced, an oxygen and pressure dynamic change model is constructed by utilizing an algorithm, the safety of equipment and a user is protected, and the safety of the equipment and the user is improved through an intelligent and automatic control means. The problems of an existing micro-pressure oxygen cabin are effectively solved, the treatment effect and the use safety are improved, and the more advanced and reliable micro-pressure oxygen cabin control system and the control method thereof are provided for the medical field.

The invention provides an intelligent critical patient multi-dimensional management system, and relates to the technical field of intelligent critical patient management. Comprising a data integration and cleaning module, a patient information management module, an algorithm and analysis module, a treatment scheme recommendation module and an early warning and intervention module, patient information can be mainly integrated through the data integration and cleaning module, and therefore previous treatment information of a patient can be rapidly detected. Advanced monitoring equipment and algorithms are integrated, the system can monitor vital signs of a patient in real time, early warning is immediately given out once abnormal data are detected, a quick response opportunity is provided for medical staff, the system not only monitors single vital signs such as the heart rate and the blood pressure, but also can comprehensively analyze multiple physiological parameters, and the medical staff can conveniently and quickly monitor the vital signs. Therefore, the health condition of the patient can be evaluated more comprehensively, and the trend change of data of the patient can be analyzed.

The invention relates to the technical field of biomechanical measurement and analysis, in particular to a home textile recommendation method and system based on child health data monitoring and analysis. The method comprises the following steps: collecting an original point cloud coordinate set of a sleeping posture of a child; extracting a cervical curvature parameter group according to the extracted cervical curvature parameter group of the skeleton key point spatial distribution diagram; performing cervical vertebra and body position analysis according to the cervical vertebra curvature parameter group to obtain cervical vertebra curvature-body position holographic data; collecting a static pressure baseline data set according to the cervical curvature-body position holographic data; performing body position-pressure correlation analysis according to the static pressure baseline data set to obtain a body position-pressure mapping relation table; and generating a pressure distribution index map according to the body position-pressure mapping relation table. According to the method, the physical sign data, the pressure distribution and the physiological curvature change of the children in the natural sleep state are comprehensively collected and analyzed, so that high-precision, whole-process and personalized healthy home textile recommendation is realized.

The invention relates to the technical field of diving monitoring, solves the problem that the safety of a diver cannot be effectively monitored in the prior art, and provides a diver safety monitoring method and system based on data fusion analysis. The method comprises the following steps: acquiring real-time physical sign data of a diver and real-time environment data of the diver; analyzing the real-time environment data to obtain an environment influence factor used for reflecting the influence degree of the current environment on the physical sign data; according to the environmental impact factor, correcting the real-time sign data to obtain corrected target sign data; performing fusion analysis on the target sign data to obtain a real-time state analysis result of the diver; and performing comparison and safety assessment on the real-time state analysis result and a preset diver health state template corresponding to the real-time environment data to obtain a safety assessment result. According to the invention, the safety of the diver can be effectively monitored.

The invention provides a first-aid method and system based on 5G communication and an edge device. The method comprises the following steps: determining a communication protocol target adaptation protocol with a 5G base station based on the edge device; obtaining multi-modal data according to the target adaptation protocol based on the edge device, and performing priority classification on the multi-modal data to obtain a priority classification result; evaluating the 5G network resources of the edge device, determining a resource evaluation result, and determining a cloud transmission strategy based on the resource evaluation result and the priority classification result; performing association analysis on the vital sign data, the medical image data and the voice record data to obtain a calculation task and task complexity, processing the calculation task based on the task complexity and a cloud transmission strategy, and determining a data processing result; and data processing results are transmitted to the first-aid smart center and the mobile terminal in a grading manner. According to the method and the device, the compatibility problem caused by non-uniformity of device interfaces is solved, and the edge device processing capability is improved.

The present invention relates to a smart wearable wristband with electrical electrodes for non-invasive neurostimulation, comprising: a main unit for fitting around a wrist and measuring user's vital signs; and a stimulation portion for stimulating median nerve or ulnar nerve located below Neiguan PC 6 or JianShi PC 5. According to the present invention, hypertension or stress can be effectively alleviated.

The invention discloses an intelligent infusion monitoring management system, and relates to the technical field of medical equipment, and the system comprises the steps: collecting patient data, synchronizing the patient data to a central monitoring system through a block chain technology, and generating a personalized infusion scheme; based on a personalized infusion scheme, a medical worker uses a mobile device to carry out identity verification on a patient and obtain medicine information, and a voice assistant function is integrated; the monitoring module is responsible for monitoring vital signs of a patient and the temperature of a ward by using wearable equipment and a temperature sensor, and adjusting a personalized infusion scheme and liquid temperature according to the vital signs and the temperature of the ward; the risk value prediction module is responsible for predicting risk values of blockage and leakage according to the adjusted personalized infusion scheme and the liquid temperature and setting a graded alarm mechanism; patients and family members receive the infusion progress through the intelligent infusion monitor. According to the invention, the block chain technology and the advanced artificial intelligence algorithm are integrated, so that the health data and medicine information of the patient can be efficiently managed and analyzed.

The invention discloses an aeronautical medical rescue assessment method and system based on an MR technology, and relates to the technical field of aeronautical medical rescue. Comprising a multi-dimensional data acquisition module, a fusion data display module, a task real-time evaluation module, a task automatic adjustment module, a remote cooperation adjustment module and a task record evaluation module. According to the scheme, data such as vital signs of a patient, the flight state of an aircraft, rescue worker operation and medical resource configuration are comprehensively integrated through multi-dimensional data acquisition, a comprehensive data source ensures that a decision maker can obtain comprehensive, real-time and accurate situation awareness, and information one-sidedness caused by a single data source in the prior art is avoided; the data are cleaned and fused through a Kalman filter, and the precision and timeliness of the data are remarkably improved.

The invention belongs to the technical field of medical informatization, and provides an anesthesia medication management system with biological recognition, comprising: a biological recognition module verifies the identities of medical staff and patients through a vein recognition technology; the multi-source data acquisition module acquires vital sign data and equipment parameters of a patient in real time to obtain multi-source data; the intelligent drug management module authorizes to store and take drugs according to the biological recognition result and generates an electronic anesthesia prescription; the processing decision module receives the multi-source data and generates a medicine adjusting instruction through a preset rule and an algorithm in combination with an electronic anesthesia prescription; the execution control module carries out secondary confirmation on the medicine adjusting instruction through biological recognition and then sends the medicine adjusting instruction to anesthetic infusion equipment to be executed; according to the method, through the electroencephalogram double-frequency index, the entropy index and the hemodynamic parameters, in combination with the anesthesia depth evaluation result and the personalized drug metabolism and effect model, the medication scheme is dynamically adjusted, the optimal infusion rate is generated, the anesthesia depth control is more accurate, and the fluctuation range is narrowed.

The invention relates to a system for realizing pain scoring of an ICU (intensive care unit) patient. The system comprises a data acquisition module for acquiring a facial micro-expression video of the ICU patient in real time by using a high-definition camera; the micro-expression recognition module analyzes the collected micro-expression video image; the vital sign analysis module analyzes the collected vital sign data in real time; the pain scoring model is obtained by training through a machine learning algorithm, and the pain degree of the patient is quantitatively scored; and the result output module outputs the pain scoring result to the medical staff in real time. According to the system and method for achieving ICU patient pain scoring, the processor and the computer readable storage medium of the system and method, the objectivity and accuracy of pain scoring are improved through the artificial intelligence technology. By analyzing the combination of micro-expressions and vital signs, the sensitivity and specificity of pain assessment are improved. Real-time monitoring and continuous evaluation of pain scores are realized, and timely adjustment of a treatment scheme is facilitated.

The invention discloses a postoperative drainage device for clinical nursing and a monitoring system, and belongs to the technical field of biomedical engineering. The problems that an existing device manually monitors drainage liquid data, consequently, the monitoring precision is insufficient, and the complication probability cannot be predicted are solved, drainage liquid and patient vital sign data are collected in real time through the micro spectrograph, the character multi-parameter sensor, the micro pressure sensor and the intelligent bracelet and transmitted to the remote monitoring platform, and the monitoring accuracy is improved. Medical staff can check real-time data of a patient at any time and adjust a treatment scheme in time, so that the nursing efficiency and quality are improved; multi-parameter data such as color, metering and property of drainage liquid and vital sign data are analyzed through a risk prediction model, intervention measures are dynamically adjusted according to different complication types and probabilities in combination with patient condition changes and real-time monitoring data, accurate intervention on the complication is achieved, and the occurrence rate and severity of the complication are reduced.