44 results about "Elderly falls" patented technology

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data fusion processing method and system, and the method comprises the steps: collecting a multi-modal data set containing the activities of the old through an intelligent old-age care robot; constructing a timestamp calibration matrix based on the multi-modal data set, and carrying out dynamic weight correction on the timestamp calibration matrix by adopting a three-modal data envelope delay compensation algorithm to obtain a three-modal data stream with a synchronous time sequence; performing hierarchical feature extraction through a nested feature extractor to obtain a three-mode tumble feature vector group; performing vector fusion on the three-mode tumble feature vector group to obtain tumble fusion feature vectors; and inputting the tumble fusion feature vector into a multi-modal forward prediction model to perform tumble risk trajectory prediction, and outputting a tumble detection result, the method realizes prediction analysis of body posture changes in a key time window before the old man tumble, and improves the ability to distinguish a slow getting-up action and a real tumble behavior of the old man.

The invention discloses an old people falling risk assessment and dynamic protection intervention system and method, and relates to the technical field of computer vision, and the system comprises a hardware composition module, a software and algorithm module, an intervention mechanism module, a system integration and optimization module, and an application scene module. The hardware composition module is used for collecting motion postures, gaits and environment data of old people in real time through a multi-mode sensor, hardware support is provided for risk assessment and protection, deep fusion of vision, inertia and environment data is achieved through an attention mechanism, spatial-temporal features are rapidly extracted in combination with a lightweight CNN-LSTM network, and the accuracy of risk assessment and protection is improved. And then the dynamic relationship between the mass center and the supporting surface is accurately calculated based on a biomechanical model, so that the real-time performance of attitude analysis and the accuracy of fall risk assessment are effectively improved, the prediction accuracy of the system on the fall risk of the old people is greatly improved and the false alarm rate is reduced while the consumption of calculation resources is reduced, and a reliable basis is provided for a hierarchical protection strategy.

The invention relates to the technical field of intelligent fall detection, in particular to an old people fall detection and early warning system based on multi-modal data fusion, which comprises a dynamic weight fusion unit, a precursor trigger unit, a multi-level decision unit and a verification feedback unit, the dynamic weight fusion unit collects three-axis acceleration, surface electromyographic signals and millimeter wave radar data, multi-source data association analysis is achieved through a double-variable attenuation model and dynamic threshold calibration, and the precursor trigger unit activates electromyographic high-frequency sampling and joint three-dimensional space included angle monitoring when the exercise intensity is abnormal. And the multi-level decision-making unit constructs a tumble probability algorithm based on a random forest model, and performs three-level verification in combination with acceleration standard deviation, ground contact point density and historical mode matching, thereby realizing accurate discrimination of tumble events, providing a high-reliability intelligent solution for safety protection of old people, reducing the risk of tumble injury, and improving the safety of the old people. And the first-aid response efficiency is improved.

The invention relates to the technical field of indoor old people safety monitoring, and discloses an indoor old people tumble detection system based on a hybrid mode, which comprises a video acquisition module, an audio acquisition module, an identity recognition module, a tumble detection module, an emotion recognition module and an alarm module. According to the method of the indoor old people tumble detection system based on the mixed mode, video and audio data are creatively fused, multi-dimensional monitoring of the state of the old people, capturing of action postures of the old people by the video acquisition module and obtaining of voice and environment sound information by the audio acquisition module are achieved, and the two complements each other; abnormal sound in the audio, such as collision sound of falling and shouting sound, can assist in judgment, the multi-dimensional data acquisition and analysis mode greatly reduces misjudgment and missed judgment caused by single data, the accuracy and reliability of detection are remarkably improved, and the safety of old people is comprehensively guaranteed.

The invention discloses an intelligent monitoring method and system for preventing an old person from falling down based on multiple sensors, and relates to the technical field of falling monitoring, and the method comprises the following steps: merging behavior data, physiological data and environmental data into a data set; obtaining a feature combination from the related features based on an association rule, and quantifying the influence of the feature combination on the falling probability of the elderly; based on a machine learning algorithm, learning a relationship between the related features and whether the old person falls down, and predicting an old person falling probability corresponding to each sample of the new data set; optimizing the falling probability of the elderly based on the quantification result; if the optimized falling probability of the old people exceeds a certain threshold value, executing a corresponding early warning scheme; the system comprises a data acquisition module, a feature combination acquisition module, a probability prediction module, a probability optimization module and an early warning execution module. According to the invention, the fall probability corresponding to each sample can be obtained, the quantitative influence of the detected feature combination is superposed to the prediction result, and the accuracy of fall risk assessment is further improved.

The invention discloses a household elderly falling monitoring system and method based on feature fusion, and relates to the technical field of falling monitoring. The technical key points are as follows: firstly, the reasonability and effectiveness of an obtained detection result are guaranteed to obtain first gait image recognition of the home elderly to estimate the posture of the home elderly, the improved OpenPose network and CoordConv are utilized to replace VGG-16 into EfficientNet, the first gait of the home elderly is accurately monitored, the balance influence proportion is calculated for different gaits, and the balance influence proportion is calculated for the different gaits. According to the method, the probability that the old people have falling behaviors is judged to generate different risk signals, falling behaviors and postures under medium and high risk signals are further analyzed in a targeted mode, through deflection analysis, changes of human body postures are monitored in real time, characteristics of different falling postures are understood, different falling postures are independently and accurately modeled, and a second falling coefficient is generated; the tumble degree is accurately identified, and a foundation is laid for optimization of a subsequent model.

The invention discloses an old people fall monitoring and multi-terminal linkage emergency response system, and relates to the technical field of medical treatment, and the system comprises the steps: carrying out the non-inductive collection of human body dynamic information in a weak light visual blind area environment; the voice interaction verification judgment module comprises a far-field voice excitation unit; and setting a priority queue management mechanism, and confirming a behavior perception output tumble instruction. According to the invention, closed-loop management of fall monitoring and emergency treatment of the old people is realized, and the system has remarkable beneficial effects; the privacy of the user is effectively protected, and the false alarm rate and the invalid scheduling of nursing resources are obviously reduced; the event discrimination accuracy and the individual suitability are improved, and it is ensured that high-risk events are not missed; the timeliness and accuracy of emergency response are improved, and safe, intelligent and humanized technical support is provided for intelligent old-age care and home health monitoring.

The invention relates to the field of home tumble detection, aims to solve the problems that a home tumble detection system for old people is single in detection means, is easily interfered, affects the detection accuracy, and is difficult to carry out two-way communication at the first time after tumble, and particularly relates to a home tumble detection system based on two-way communication of the Internet of Things. According to the invention, the falling situation possibly existing in the home environment is collected through three modes of millimeter wave radar, sound detection and image collection, so that the accuracy of judging the falling situation is improved, the falling situation caused by missed judgment and misjudgment is avoided, the safety of the detected old people is ensured, and after the falling situation occurs, the safety of the old people is ensured. According to the system, the two-way communication function is automatically accessed through the system, so that air two-way communication is realized by means of the fixed sound acquisition assembly and the sound playing assembly, and when the elderly fall down, the elderly can be timely contacted with guardians or managers, so that the falling elderly can be timely rescued.

The invention discloses an old man falling alarm system based on a multi-modal artificial intelligence model. The old man falling alarm system comprises a data acquisition module, a multi-modal fusion module, an artificial intelligence analysis module, an alarm module and a background management system. The data acquisition module acquires video image data, wearable equipment data and environment data of the elderly; the multi-modal fusion module carries out fusion processing on data of different modals to generate a multi-modal feature vector; the artificial intelligence analysis module analyzes the feature vectors based on a multi-modal artificial intelligence model, and outputs the probability and confidence of falling of the old; when it is judged that the old fall down and the confidence reaches a preset threshold, the alarm module starts to give an alarm; and the background management system configures, manages and monitors the system. According to the invention, by fusing various modal data, efficient and accurate detection and timely alarm of the falling condition of the elderly are realized by using a deep learning technology, a powerful guarantee is provided for the safety and health of the elderly, and the method is suitable for the scenes of old-age care institutions, community old-age care service centers, families of the elderly living alone and the like.

The invention discloses an intelligent old-age care supervision system based on an AI intelligent agent, and the system comprises a wearable biological information collection intelligent agent which uses a GD32 chip as a core processing module and is used for collecting and receiving the basic information of a supervised person; the fixed environment variable acquisition agent uses an ESP8266 chip as a core processing module and is used for acquiring and receiving environment variable information; the fixed video acquisition and analysis intelligent agent is used for acquiring posture information of people in a public area and judging abnormal states of the people through AI identification; and the information processing center is used for summarizing information acquired by the three intelligent agents and sending the information to the friendly interaction intelligent agent for a user to call and check. The method is high in applicability, low in misjudgment rate of fall detection of the old people and accurate in position identification.

The invention discloses an old people abnormal behavior remote monitoring and real-time early warning system, which comprises an equipment end, a cloud end and a mobile end, and is characterized in that the cloud end is in data connection with the equipment end and the mobile end, and the cloud end comprises an equipment information input module, an abnormal behavior analysis module, a real-time early warning module and a data storage module; the data storage module is in data connection with the equipment information input module, the abnormal behavior analysis module and the real-time early warning module. The main shoe monitoring end and the slave shoe monitoring end are additionally arranged at the bottom of the insole, so that the problem of missing wearing is solved, and the accuracy of abnormal behavior recognition is improved through data acquisition of double equipment ends; and the real-time early warning module performs graded early warning based on an analysis result of the abnormal behavior analysis module, so that monitoring resources are reasonably allocated, a complete safety guarantee system from data acquisition and analysis to early warning response is formed, the monitoring problem of falling and missing of the old people is effectively solved, and a comprehensive and efficient technical guarantee is provided for the safety of the old people.

The present invention relates to a method and system for evaluating the risk of falls for the elderly, belonging to the field of image processing and application of machine learning models. The evaluation method includes: (1) receiving the monitoring video data obtained by a camera installed in the monitored place; (2) cropping a human body image containing at least human joints from each frame of the monitoring video data, and processing the human body image into a human body image to be recognized with a predetermined standard size; (3) based on a pre-trained human pose estimation network model, obtaining the joint coordinate data in the human body image to be recognized; (4) based on the joint coordinate data of consecutive multiple frames of images, evaluating the risk of the elderly falling. Evaluating the risk of falling based on the human pose obtained by the machine learning model effectively reduces the usage of detection sensors, and reduces the trouble brought by wearing and the impact on the actions of the elderly. It can be widely applied to fields such as elderly care, etc.

The utility model discloses an old man falling alarm device which comprises a waistband body, an alarm mechanism is arranged at the front end of the waistband body, the alarm mechanism comprises a fixing plate fixedly connected to the front end of the waistband body, and a guide frame is fixedly connected to the front end of the fixing plate. The lower side, close to the guide frame, of the front end of the fixing plate is slidably connected with an alarm device body. According to the falling alarm device for the old people, the waistband main body penetrates through the inner side of the buckle belt ring and drives the alarm device main body to move towards one side, so that the alarm device main body is more tightly clamped by means of the butt joint surface and the contact surface, and the purpose of conveniently wearing the alarm device main body on the waist of the old people is achieved; and the waistband main body is a necessity for the old people to wear clothes, so that the old people cannot forget to wear the alarm device main body, and when the old people walk, the movement range of the alarm device main body is small, and the alarm device main body cannot be triggered easily.

The invention relates to the field of old people behavior monitoring, and discloses machine vision old people abnormal behavior protection equipment which comprises a protection garment for joint protection, a safety helmet, a fourth joint sleeve, a shoulder pad, a fifth joint sleeve, a third joint sleeve, a ring sleeve, a first joint sleeve and a second joint sleeve. The safety helmet, the fourth joint sleeve, the fifth joint sleeve, the third joint sleeve, the ring sleeve, the first joint sleeve, the second joint sleeve, the first monitoring mechanism and the safety mechanism are in signal communication through the Bluetooth module, and the third joint sleeve, the ring sleeve, the fourth joint sleeve, the first joint sleeve, the second joint sleeve and the safety helmet are each provided with a camera. Motion data are measured through a plurality of sensors, then accurate data of behaviors of the elderly are obtained, when the elderly suffers from diseases, the outer expansion sleeve can be instantly triggered, important joint parts are protected, damage to the elderly is reduced, and the overall survival rate is increased.

The invention discloses an intelligent detection system and method for avoiding missing report of falling events of the elderly living at home, the intelligent detection system is composed of a plurality of sets of detection devices installed on the ceiling and the wall surface of a living space of the elderly, each set of detection device works independently, and each detection device comprises a multi-modal data acquisition module. Collecting movement, sound and environment data of the elderly; the data preprocessing module is used for carrying out denoising, filtering and feature extraction on the data; the tumble detection module judges whether the elderly tumble based on a deep learning algorithm; the alarm module is used for giving an alarm to an emergency contact person when falling is detected; and the storage module is used for storing various data. The detection method comprises the steps of data acquisition, preprocessing, fall judgment, alarm processing and data storage. The invention belongs to the technical field of smart home and health monitoring, and particularly relates to an intelligent detection system and method for avoiding missing report of falling events of the elderly living at home.

The present application relates to the technical field of intelligent monitoring, in particular to an old people fall monitoring and early warning method and system based on multi-source information fusion, the system comprising a visual perception module, a wearable sensing module, a central processing unit and an early warning module. The method is as follows: collecting human posture video stream, human waist three-axis acceleration and three-axis angular velocity, foot pressure, heart rate and blood oxygen physiological data in the monitoring area; respectively analyzing visual posture information, inertial sensing and foot pressure data, and extracting key motion features; comprehensively analyzing the features of the three types of information sources, especially using the MediaPipe framework to efficiently analyze human posture, combining with wearable sensor data, adopting a multi-modal feature fusion strategy based on attention mechanism, improving the accuracy of multi-source information fusion, so as to accurately judge the stability of the current posture of the old people and the fall risk level, and timely trigger the early warning, improving the accuracy and safety of the monitoring.

The application discloses an old people fall risk assessment and dynamic protection intervention system and method, relates to the technical field of computer vision, and comprises a hardware component module, a software and algorithm module, an intervention mechanism module, a system integration and optimization module and an application scenario module.The hardware component module is used for collecting the motion posture, gait and environment data of old people in real time through a multi-modal sensor, and providing hardware support for risk assessment and protection.The application realizes the deep fusion of visual, inertial and environment data through an attention mechanism, combines a light-weight CNN-LSTM network to quickly extract space-time features, and then accurately calculates the dynamic relationship between the center of mass and the support surface based on a biomechanical model, thereby effectively improving the real-time performance of posture analysis and the accuracy of fall risk assessment, reducing the consumption of computing resources, greatly improving the prediction accuracy of the system for the fall risk of old people, reducing the false alarm rate, and providing a reliable basis for a graded protection strategy.

The invention discloses a nursing home old man falling identification method and device based on a multi-modal large model, and the method comprises the steps: firstly obtaining an old man target bounding box coordinate set, and extracting a target region based on the old man target bounding box coordinate set to obtain an old man behavior image set; and then matching a question text for each old person behavior image in the old person behavior image set, inputting the question text into a multi-modal large model, and performing fall detection on the old person behavior image set to obtain a fall analysis answer list. And then semantic analysis and classification operation are performed on the tumble analysis answer list to obtain a tumble sample index set, and tumble event positioning and visual marking are performed on the nursing home video monitoring image based on the tumble sample index set and the old man target bounding box coordinate set to obtain a nursing home old man tumble identification annotation graph. Compared with a traditional detection method, the method has higher recognition accuracy and lower missing report rate.

The invention discloses a YOLOv11 attitude trajectory smoothing and abnormity early warning algorithm based on dynamic window weighted average, belongs to the technical field of computer vision, relates to human body attitude trajectory processing and abnormal behavior early warning, and is suitable for scenes such as smart pension and physical training. According to the technical scheme, the method is characterized in that a multi-task YOLOv11 model is constructed; a CSPDarknet53 backbone network and an attitude dedicated feature enhancement layer (APEL) are fused, and a target detection frame and 17 joint key points are synchronously output; dynamic window weighted smoothing: calculating the window size according to the centroid velocity v, and carrying out weighted average on continuous frame key points; according to the hierarchical early warning decision tree, three-level abnormal early warning is achieved through the joint angle (vector dot product formula) and the centroid speed. The method is mainly used for early warning of falling down of old people, standard detection of movement of athletes and other scenes, 35FPS is achieved on Jetson Nano, and the accuracy rate of key points is 92.3%.

The invention discloses a smart home travel safety monitoring system, and relates to the technical field of home-based care for the aged, a road network is established, the number and time of pedestrians on the road network are collected by using a monitoring device on a road, and the movement data of the pedestrians on the road network are analyzed by using big data; obtaining a relationship between pedestrians and time on each road in the road network; the position information and destination information of the old people are obtained, the probability that pedestrians pass through each road on the road network when the old people move on the road network is predicted, and the old people are guided to go to the destination to guide the path of the old people going out and select a proper route in combination with the lighting conditions on the road. The old people are helped to walk safely in the external environment; pedestrians on the road are used for carrying out auxiliary monitoring on the condition of the old people, the pedestrians provide help for the old people when the old people fall down, and harm caused by falling down to the old people is reduced.

The present invention discloses an intelligent elderly care method based on WiFi, and the steps are as follows: collect CSI data of the monitoring area through WiFi devices; preprocess the CSI data to determine whether it belongs to a static environment or a dynamic environment; when it is determined to be a static environment, train the preprocessed CSI data through GA-SVM to obtain a trained target model after machine learning, and use the trained target model to identify "staring blankly, sleeping, no one" in the CSI data, and then calculate the heart rate and breathing conditions during sleep; when it is determined to be a dynamic environment, train the preprocessed CSI data through GA-SVM to obtain a trained target model after machine learning, and use the pre-trained target to identify "elderly falling", "indoor intrusion", and other situations in the preprocessed CSI data; the static data processor and the dynamic data processor store the results in the memory, and train and update the pre-trained target model.

This invention belongs to the technical field of geriatric clinical rehabilitation equipment, specifically relating to an adjustable geriatric clinical exercise device, including a base, an upper limb exercise component and a lower limb exercise component mounted on the upper surface of the base, and an anti-fall component mounted on the base. The anti-fall component includes a first fixed seat mounted on the upper surface of the base, on which a hydraulic push rod is fixedly mounted. A seat is detachably mounted on the top of the hydraulic push rod, and the rear end of the seat is detachably connected to the backrest. Armrests are also mounted on both sides, and the bottom of the armrests is rotatably connected to the bottom of the seat. The invention incorporates an anti-slip pad on the base to increase the device's stability. The combination of a guardrail, a flexible layer, and a pressure alarm sensor forms comprehensive fall protection. The flexible layer cushions impacts, and the pressure alarm sensor provides timely warnings, reducing the risk of falls and injuries to the elderly. The armrests are foldable, providing support for the elderly to stand up, further enhancing safety.

The utility model relates to the technical field of elderly care, in particular to an elderly care system based on voice control and image processing, which comprises a high-definition camera; the background terminal is in communication connection with the high-definition camera, the background terminal comprises a central processing unit, an analysis module and a first communication module, and the central processing unit is electrically connected with the analysis module and the first communication module; the monitoring unit is used for receiving information sent by the background terminal and realizing remote monitoring; each action of the old people at home can be captured through the high-definition camera, so that all-weather real-time monitoring of the activity state of the old people is realized, various human body tumble states collected in advance are recorded into the analysis module, and the image of the high-definition camera is compared with the human body state through the central processing unit, so that the human body tumble state can be monitored in real time. Signals are transmitted to the monitoring unit through the first communication module, alarm information is quickly sent to preset contact persons such as family members or nursing personnel, and danger caused after the old people fall down is avoided.

The application discloses an old people fall early warning method and system based on non-complete sequence and physical checking, and the method comprises the following steps: obtaining the reference standing height of a person to be detected; buffering the video stream containing the person to be detected to a ring buffer with a capacity of N frames frame by frame, updating the buffer and triggering detection after a new frame is entered; performing skeleton key point extraction and normalization on each frame of image to obtain a skeleton sequence; cutting a sub-sequence from the skeleton sequence as a truncated early skeleton sub-sequence and resampling to obtain a standardized early sequence; inputting the standardized early sequence into a trained physical perception double-flow graph convolution network to obtain an initial fall probability of the person to be detected; calculating physical state space features, and combining the initial fall probability to perform hierarchical physical constraint posterior checking to obtain a frame determination result; and issuing a fall warning information when a plurality of continuous frames are determined as falling. The application can warn before falling occurs, and can also maintain high recall rate and high accuracy.

The invention discloses an old people falling risk assessment method and system based on human body posture estimation, relates to the technical field of computers, and solves the technical problem that old people falling risk assessment according to gait data is not accurate enough. And calculating and processing the gait features in the video frame, and obtaining an old people falling risk assessment result according to the gait features. No professional equipment or sensor wearing is needed, and the elderly can complete video shooting and risk assessment autonomously to adapt to family scenes. The elderly and family members have an opportunity to take corresponding prevention measures in advance, and serious consequences caused by falling down are avoided to the greatest extent; and the falling risk of the old people can be accurately classified and evaluated.

The invention provides a safety supervision system and method for elderly people living alone based on YOLOv11, and belongs to the technical field of intelligent monitoring, and the system comprises a camera module, a behavior recognition module, an anomaly detection and early warning module, a front posture distinguishing module and an alarm module. According to the method, a behavior recognition function of a YOLO11n-pose model is utilized, a frame interval detection frame height comparison algorithm and time analysis are combined, behaviors of the elderly living alone are monitored and recognized, whether the elderly are in a safe state or not is judged, the abnormal behavior that the elderly fall down or do not go into a room for a long time is found in time, and an alarm is given. According to the YOLOv11-based safety supervision system and method for the elderly people living alone, the activity state of the elderly people in a living area is monitored by deploying the camera, behaviors of the elderly people are recognized in real time in a high-precision and low-delay manner, various abnormal scenes are covered, an abnormal alarm is generated, an activity log is recorded, and the safety guarantee level of the elderly people living alone is effectively improved.

The invention discloses a wearable falling alarm for the old, and belongs to the technical field of alarms, the alarm is worn on the waist or the arm of the old, and whether the alarm is started or not is controlled through sliding of conductive metal beads in a trigger; the conductive metal bead in the trigger slides under the action of kinetic energy generated when the conductive metal bead falls down or the gravity of the conductive metal bead to enable two contacts of the trigger to be communicated, so that a power supply of the alarm unit is switched on to give an alarm to attract attention of outsiders and provide help, and the old people who fall down can be rescued as soon as possible.

The invention belongs to the technical field of anti-falling devices. In order to solve the problem that an existing anti-falling device is poor in use effect, the walking aid for preventing the old people from falling is provided and comprises a movable base, a height adjusting connecting rod, a stability maintaining mechanism and a waist surrounding belt. The upper end of the height adjusting connecting rod is connected with the stability maintaining mechanism, and the lower end is connected with the movable base. Through the stability maintaining mechanism, when an old person falls forwards, the old person can be pulled backwards, two arms of the old person are supported, the body balance of the old person is maintained, when the old person falls backwards, the back of the old person can be supported, and the purpose of maintaining the body balance is achieved; the walking aid provided by the invention can maintain the body balance of the old people when the old people fall forwards or backwards, and the use effect is good.

The present application relates to the technical field of fall detection, and relates to an old person fall detection and alarm system based on a multi-modal sensor.The present application collects the height of the center of gravity from the ground in real time, combines the allowable height deviation of the detection object, analyzes whether the center of gravity is moving downward, calculates the azimuth of the center of gravity displacement based on the displacement vector of the center of gravity in the reference rectangular coordinate system, constructs a real-time center of gravity feature parameter set, obtains the azimuth of the hands and the distance from the hands to the center of gravity within a set time period, combines the center of gravity feature parameter set and the historical normal behavior data set of the detection object, determines whether the current state is reasonable, if not, analyzes the overall offset consistency based on the azimuth of the center of gravity offset and the azimuth of the hands, combines the time sequence of the height of the center of gravity from the ground and the height of the hands from the ground to determine whether a fall has occurred, reduces the false alarm and missed alarm probability, improves the recognition accuracy of the detection system, and guarantees the personal safety of the old people.

The present invention relates to the technical field of elderly status monitoring, and specifically to an elderly status analysis method based on image recognition, which includes the following steps: S1. Obtain the first image data of the elderly in real time from multiple angles, as well as the physiological data of the elderly including body size data, heart rate record, and blood pressure record; S2. Identify the identity of the elderly, extract the initial key points in the first image data, and determine whether there are errors; if so, proceed to step S3; if not, mark the initial key points as determined key points, mark the first image data as the second image data, and proceed to step S4; S3. Correct the initial key points in the first image data to generate the second image data; The present invention takes into account that the changes in heart rate and blood pressure may occur when the elderly fall, so the calculation accuracy of the comprehensive risk degree value is further improved, so that medical staff can take corresponding treatment measures in a timely manner according to the comprehensive risk degree value.