36 results about "Abnormal vital signs" patented technology

The invention discloses a nursing data sharing method and system based on the Internet of Things. The method comprises the steps that S1, multi-modal nursing data are collected in real time through a distributed Internet of Things terminal cluster, and a feature signal set is formed; s2, constructing a data coordination engine, executing federal learning preprocessing operation on the feature signal set, and forming a coordination signal flow; s3, dynamically calculating a data contribution degree weight and generating a verification signal chain; s4, monitoring vital sign abnormal indexes in the coordination signal flow in real time, and generating a trigger signal vector; and S5, updating incentive mechanism parameters according to the contribution degree voucher in the verification signal chain, optimizing a service quality strategy based on a network resource allocation scheme in the trigger signal vector, and forming a closed-loop control signal. According to the invention, the problem of data islanding effect in the medical care data sharing process can be 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 patient vital sign anomaly detection method based on an artificial intelligence technology, and relates to the technical field of data processing, and the method comprises the steps: collecting original sign data of a patient; calculating multi-dimensional characteristic parameters; establishing an individual baseline model, and determining a comprehensive reference interval in the model; the vital sign features monitored in real time are constructed into multi-dimensional feature vectors, the multi-dimensional feature vectors are input into the individual baseline model, and the deviation degree of the real-time multi-dimensional feature vectors in the comprehensive reference interval is calculated; performing a clustering analysis to identify an anomalous aggregation region; performing trend analysis, calculating change direction consistency and continuous change amplitude of the multi-dimensional feature vector, and generating a trend analysis result; calculating an accumulated change index in a continuous time window according to a trend analysis result to obtain a dynamic confidence score; when the dynamic confidence score continuously exceeds an adaptive threshold value, determining that a vital sign abnormal event exists; the autonomy and accuracy of the method for detecting the vital sign abnormity of the patient are improved.

The invention relates to a target person detection method and device based on millimeter wave radar, equipment and a medium. According to the method, radar detection configuration is adjusted, a gridding coordinate system is established in a target treadmill monitoring area, millimeter wave radar is used for collecting feature data related to movement of a target person, and movement track changes of the target person are extracted to generate person position information; acquiring breathing and heartbeat signals of a target person, and performing signal filtering and feature extraction to obtain vital sign information; comprehensively evaluating the existence state of the target person according to a preset threshold value and a discrimination standard in combination with the person position information and the vital sign information; and when the evaluation result shows that the target person leaves, the vital signs are abnormal or a cheating behavior exists, judging that the target person is in an abnormal state, and generating a corresponding early warning instruction. By adopting the method, abnormal conditions can be found in time, early warning can be generated, and the monitoring accuracy and response speed are improved.

The invention relates to the technical field of vital sign anomaly detection, in particular to a vital sign anomaly feature extraction method based on a sign signal, which comprises the following steps: firstly, dynamically adjusting the size of a time cabin through variational optimization, taking both transient and steady feature capture into account, and avoiding feature loss caused by a fixed window length; in combination with principal component analysis, Lyapunov exponential chaos quantization and a high-order differential operator, nonlinear features are decoupled from multiple dimensions of a time domain, a frequency domain and a phase domain, the feature interpretability is improved, further, through phase-speed mapping and attention weighted fusion, self-adaptive enhancement and decoupling of spatial-temporal features are achieved, noise interference is suppressed, and the spatial-temporal features are enhanced. The finally generated multi-dimensional feature cube integrates time resolution, frequency precision and phase differential information, the anomaly detection robustness of complex vital signs is remarkably improved, and the defects that a traditional method is single in dimension and insufficient in adaptability in dynamic signal processing are overcome.

The invention relates to the technical field of artificial intelligence, in particular to a diet recommendation management method and system based on the vital sign state of a user, and the method comprises the steps: building a food nutrition information base in advance; generating sign monitoring data of a target user, acquiring vital sign characteristics of the user according to the monitoring data, and calculating the reference intake of personal dietary nutrients at the same time; the user carries out daily healthy diet card punching, diet analysis is carried out, and daily diet recommendation is carried out according to the vital signs of the user and the nutrient elements needing to be ingested every day; acquiring vital sign feedback information of the target user, and updating the daily recommended diet scheme according to the feedback information to obtain an updated daily diet management scheme; and performing health management and vital sign monitoring on the target user according to the updated daily diet management scheme. According to the invention, diet control under the condition of abnormal vital signs of the user can be improved, and life quality is improved.

The invention provides a multi-parameter fusion intelligent internal medicine physical sign monitoring system and method, and relates to the technical field of medical detection, and the method comprises the steps: carrying out the synchronous collection and time-space fusion of multi-source physical sign data, and obtaining a fusion data set; extracting physical sign waveform features from the fusion data set, and performing modal decomposition on the physical sign waveform features to obtain sub-segment modal signals; carrying out fusion modeling through the thoracic displacement track of the monitored object and all the sub-segment modal signals to obtain sign reconstruction parameters, and carrying out echo suppression on the sign reconstruction parameters to obtain a sign joint monitoring field; determining a physical sign matching feature vector according to the physical sign joint monitoring field and the real-time physical sign phase, and performing physical sign mode recognition on the physical sign matching feature vector through a classification model to obtain a trigger factor when the physical sign is abnormal; according to the method and the device, deep fusion and abnormal intelligent identification of multi-source sign data can be realized, so that the clinical adaptability of internal medicine sign monitoring is improved.

The invention discloses a medical big data management method and system based on cloud edge collaboration, and relates to the technical field of cloud edge collaboration. The method comprises the following steps: constructing a cloud data computing center; the cloud data computing center performs feature extraction on cloud data to train a medical diagnosis model; the medical diagnosis model is combined with a federal cloud edge collaborative algorithm to carry out model training on an edge server; the cloud data computing center obtains a detection report or real-time vital signs uploaded by the patient; processing the acquired data, and inputting the processed data into a medical diagnosis model; and outputting a result by the medical diagnosis model, and performing vital sign abnormal value calculation, similarity value calculation and comprehensive similarity value calculation. The medical diagnosis model is trained through the cloud edge coordination algorithm, the vital sign abnormal value calculation, the vital sign similarity value calculation and the comprehensive similarity value calculation are performed according to the output result of the medical diagnosis model, accurate analysis of the patient is realized, and the medical rescue efficiency and quality are improved.

The invention relates to the technical field of medical monitoring, in particular to an intensive care unit early warning system based on artificial intelligence, which comprises a data acquisition module, a data transmission module, a data center, a physical sign alarm module, a health prediction module, a risk early warning module and an auxiliary decision making module, in order to solve the problem of high false alarm rate for monitoring vital sign abnormity of a critical patient, an abnormity detection scoring algorithm is adopted in a sign alarm module, and various vital sign data monitored in real time are scored comprehensively through a risk threshold value, an influence factor and an alarm correct rate; in order to solve the problem that the future vital sign trend of a critical patient is difficult to accurately predict, a health prediction model based on Transform-BiGRU is designed, physical sign features, statistical features and medical features are comprehensively learned by using Transform, and the ability of capturing a bidirectional dependency relationship is further enhanced through BiGRU, so that the accuracy of model prediction is improved.

The application discloses a neonatal intelligent monitoring bed and a vital sign abnormality dynamic identification method, and relates to the technical field of neonatal monitoring beds.The neonatal intelligent monitoring bed comprises a bed frame, a flexible mattress, a bed plate driving unit, a main control unit and a warning unit, is provided with a multi-section adjustable flexible bed plate and a multi-modal sensing unit, and is characterized in that the multi-section adjustable flexible bed plate is divided into a head section bed plate, a trunk section bed plate, a left side turning bed plate and a right side turning bed plate, each section of the bed plate can be independently adjusted in inclination, the bed plate driving unit is in transmission connection with the multi-section adjustable flexible bed plate and is used for driving each section of the bed plate to adjust the inclination.The application realizes reflux preposition prevention and control, accurately captures the precursor pathological characteristics 10-30 seconds before reflux occurs, completely changes the lag mode of the prior art that alarms only after milk overflow occurs, avoids the risk of aspiration and suffocation from the root, and fills the technical blank of pre-prevention and control of neonatal reflux.

The application discloses a kind of based on real-time positioning and physiological data's cruise ship emergency evacuation guiding system and method, including bracelet terminal, monitoring subsystem, server platform and client;Bracelet terminal is worn in passenger or crew hand, including heart rate sensor, navigation display and prompt unit, wireless communication module and control circuit etc..Heart rate sensor is used to collect the heart rate information of wearer, navigation display and prompt unit are used to display route guide and provide vibration or voice prompt.By constructing dynamic electronic fence based on computer vision, combined with real-time personnel position and channel topological information to carry out evacuation simulation, generate and dynamically adjust the optimal evacuation path, and use physiological data such as heart rate to optimize the evacuation guiding strategy, trigger nearby rescue dispatch for people with abnormal vital signs, so as to improve the efficiency and safety of cruise ship emergency evacuation.

The present application provides a luminaire system comprising a lighting assembly of a room for a predetermined number of persons to rest, the lighting assembly comprising a radar for detecting condition information of persons in the room, and a controller for controlling the lighting assembly in a first manner comprising when a vital sign of any person in the room is abnormal, controlling the lighting assembly to work in an alarm mode; when the vital signs of the personnel are normal, determining whether the current personnel number is greater than a predetermined number based on the personnel number information; when the current number of people is greater than the preset number, controlling the lighting assembly to work in a lighting mode; when the current number of people is smaller than or equal to the preset number, whether the people enter a sleep state or not is judged based on the vital sign information; and adjusting the brightness of the lighting assembly according to the proportion of the number of the first persons entering the sleep state in the current person number. When people are in life danger, an alarm prompt is given out, and illumination can be provided according to the number of people and sleep conditions.

The present invention discloses a distress rescue method and system suitable for self-rescue in outdoor scenes. The distress rescue method includes: using a wearable mobile terminal to monitor the vital signs of the person in distress, and uploading abnormal data to a cloud platform when the vital signs are abnormal; the cloud platform sends rescue demand confirmation information to the wearable mobile terminal; if the person in distress chooses to refuse, the cloud platform determines that the person in distress is safe; if the person in distress chooses to accept or the person in distress does not respond, the cloud platform determines that the person in distress is in distress, sends a distress signal to professional rescue personnel, and remotely activates the portable life support equipment carried by the person in distress for first aid. The present invention can report abnormal data to the cloud platform as soon as possible, and perform first aid through the portable life support equipment, thereby extending the time for the person in distress to wait for professional rescue and reducing the difficulty of professional rescue.

Apparatus and methods are disclosed for monitoring, tracking, and storing vital signs using an adjustable wristband and / or ring designed to fit the patient or user. The apparatus includes integrated components such as sensors, a motherboard, and a web-based platform and / or mobile dashboard. The apparatus work together to notify the patient, healthcare team, and family members when vital signs are abnormal. Additionally, the system displays and securely stores vital signs data, making it accessible to authorized individuals during medical care or for daily health monitoring. The apparatus provides real-time notifications to users upon detecting abnormal vital signs and utilizes artificial intelligence (AI) and machine learning models to analyze health data collected from multiple data sources including EMR systems, multiple personal devices and payers to predict risk to critical illness, recommend preventive care options, and deliver proactive, personalized health insights to users, enhancing remote patient management, improving patient outcomes and overall efficiency.

The present invention discloses an intelligent triage and real-time vital sign monitoring transfer stretcher system, belonging to the technical field of intelligent medical devices. The present invention combines the advantages of hardware adaptability, intelligent triage and monitoring, and cloud scheduling integration, greatly improving the overall efficiency and safety of casualty transfer and first aid. The stretcher hardware adopts lightweight composite materials and is equipped with a multi-functional design, adapting to different casualties and scenarios. Multiple sensors achieve high-precision continuous collection of vital sign and environmental data, dynamically adjust the body temperature of the casualty, and ensure transfer safety at the basic level. Through the trained injury triage model, four-level accurate classification of injuries is achieved, and the monitoring threshold can be dynamically adjusted according to the injury condition. Early warnings are given for abnormal vital signs in a timely manner. Relying on cloud-based multi-mode wireless transmission, data interconnection in the whole scenario is realized, docking with the hospital information system, completing multi-terminal linkage scheduling for the entire first aid process, optimizing transfer specifications through data correlation analysis, rationally allocating first aid resources, and achieving seamless connection between pre-hospital and in-hospital first aid.

The embodiment of the invention provides a vital sign monitoring method, a safety belt, a device and a storage medium, and relates to the technical field of vehicles. The method is executed by a vital sign monitoring device, the vital sign monitoring device is arranged on a safety belt of a seat in a vehicle, and the method comprises the steps that under the condition that the safety belt corresponding to the seat is changed from an unfastened state to an inserted state, first prompt information is sent out, and the first prompt information is used for prompting a person on the seat; moving the vital sign monitoring device to the target position; acquiring vital sign data of a driver or a passenger; and under the condition that the vital sign data is abnormal, alarm information is sent out, and the alarm information is used for prompting that the vital signs of the personnel are abnormal. By adopting the technical scheme provided by the embodiment of the invention, the duration of obtaining the required medical assistance by the personnel in the vehicle can be shortened.

Apparatus and methods are disclosed for monitoring, tracking, and storing vital signs using an adjustable wristband and / or ring designed to fit the patient or user. The apparatus includes integrated components such as sensors, a motherboard, and a web-based platform and / or mobile dashboard. The apparatus work together to notify the patient, healthcare team, and family members when vital signs are abnormal. Additionally, the system displays and securely stores vital signs data, making it accessible to authorized individuals during medical care or for daily health monitoring. The apparatus provides real-time notifications to users upon detecting abnormal vital signs and utilizes artificial intelligence (AI) and machine learning models to analyze health data collected from multiple data sources including EMR systems, multiple personal devices and payers to predict risk to critical illness, recommend preventive care options, and deliver proactive, personalized health insights to users, enhancing remote patient management, improving patient outcomes and overall efficiency.

A non-contact method and system for monitoring and warning of abnormal vital signs in firefighters includes: transmitting frequency-modulated continuous waves into the chest cavity using a millimeter-wave radar module integrated into the firefighter's helmet and receiving the echoes; collecting body surface temperature using an infrared thermal imaging module and acquiring environmental and inertial parameters using sensors; extracting chest cavity micro-motion signals from the echoes to estimate heart rate and respiratory rate; extracting body surface temperature features from temperature images; compensating for temperature and smoke attenuation using environmental parameters and adaptively suppressing motion artifacts using motion parameters to obtain pure physiological signals; inputting heart rate, respiratory rate, and body surface temperature features into a cross-modal attention fusion network to generate a fused feature vector; dynamically calibrating based on individualized physiological baselines according to motion intensity and calculating physiological deviation; simultaneously inputting the feature vector sequence into an LSTM to predict the trend; if the deviation exceeds the limit or the predicted trend enters the danger zone, outputting graded warning information with abnormality type and confidence level.

The present invention belongs to the field of intelligent medical data collection and discloses a vital sign data collection system based on artificial intelligence. The present invention collects human bioelectric signals, user information feature data, user medical diagnosis lesion sites and user preferred vital signs to construct a vital sign collection and positioning model. The constructed vital sign collection and positioning model generates real-time user preferred vital signs based on the real-time human bioelectric signals, real-time user information feature data and real-time user medical diagnosis lesion sites received by the bioinformation detector. According to the pathological tissue corresponding to the real-time user preferred vital signs, the pathological tissue is repaired by bioelectromagnetic therapy using bioinformation. The present invention accurately identifies the user's abnormal vital sign data and obtains the pathological location, realizes the optimized collection of vital sign data based on artificial intelligence, reduces the workload of medical personnel, and provides a basis for subsequent bioelectromagnetic therapy to select the pathological site of the vital body for targeted bioelectromagnetic feedback treatment.

The invention belongs to the field of intelligent medical acquisition, and discloses a vital sign data acquisition system based on artificial intelligence. According to the invention, human body bioelectricity signals, user information characteristic data, user medical diagnosis focus parts and user preferred vital signs are collected to construct a vital sign collecting and positioning model; the constructed vital sign collecting and positioning model generates real-time user preferred vital signs according to real-time human body bioelectricity signals, real-time user information characteristic data and real-time user medical diagnosis focus parts received by the biological information detector, and the real-time user preferred vital signs are acquired according to pathological tissues corresponding to the real-time user preferred vital signs; and carrying out biological information repair on the pathological tissue by adopting a biological electromagnetic therapy. According to the method, the abnormal vital sign data of the user is accurately recognized, the pathological position is obtained, artificial intelligent vital sign data optimization collection is realized, the working intensity of medical personnel is reduced, and a basis is provided for selecting a living body pathological part for targeted biological electromagnetic feedback treatment in a subsequent biological electromagnetic therapy.

The utility model provides a nursing alarm circuit and device, the circuit comprises a power supply part, a light control part and a sound control part, the power supply part comprises a voltage conversion unit, a filtering unit and an electromagnetic compatibility unit; the sound control part comprises a stereo audio power amplifier unit and a loudspeaker interface unit; the light control part comprises a signal isolation unit and a relay unit. The device comprises a PCB (Printed Circuit Board), and the nursing alarm circuit is arranged on the PCB. Through more flexible and diversified alarm control functions, the scheme overcomes the problems of single control function, fixed alarm form and the like, ensures timely monitoring and effective warning of vital sign abnormity of the old people, and improves the safety of places such as nursing homes and the like and the health protection level of the old people.

The invention belongs to the technical field of intelligent ward round, and particularly relates to a clinical intelligent ward round system. According to the invention, the monitoring period is dynamically matched according to the admission physiological data, so that high-risk patients can be monitored at higher frequency, unnecessary data acquisition of low-risk patients is reduced, medical care resource allocation is optimized, behaviors such as turning over, getting up and leaving the bed are identified based on bedridden pressure time sequence characteristics, and abnormal behavior modes or behavior mutation can be found. The early-stage abnormal conditions such as getting out of bed, dysphoria and the like can be quickly found, whether the vital signs are abnormal or not is judged, the fluctuation trend and rapid change of the vital signs are recognized, early warning can be provided at the early stage of deterioration of the vital signs, preposed health risk management is achieved, abnormal events are fused, and combined recognition of behavior patterns and vital sign abnormity is adopted, so that the early-stage abnormal conditions of the vital signs are recognized. The method is more reliable than a single index.

The present invention discloses a gynecological ward remote monitoring and emergency management system based on the Internet of Things, comprising: a first subsystem, a second subsystem, an edge computing system, a node control system, a central computing system and an emergency plan library; the first subsystem is used to monitor the patient's vital signs in real time; the system realizes real-time remote monitoring of the health status of patients in the gynecological ward, comprehensive monitoring of ward environmental factors, and intelligent rapid response, processing and management of emergency events through the deep integration of the Internet of Things, edge computing and cloud computing, which not only improves the accuracy and efficiency of patient health monitoring, but also optimizes ward environmental management, realizes 24-hour real-time monitoring of basic vital signs of high-risk patients, promptly discovers abnormal conditions of patients' vital signs and promptly provides treatment, further improves the level of medical services, and provides patients with a safer, more comfortable and efficient treatment environment, thereby realizing personalized treatment plans and predictive care.

The invention relates to an intelligent portable nurse night patrol mirror device which comprises a night patrol mirror body, the night patrol mirror body comprises a shell, an infrared camera, an infrared LED lamp, a display screen, a host module and a power module, the infrared camera and the infrared LED lamp are arranged on a front panel of the shell, and the host module and the power module are arranged in the shell. According to the intelligent portable nurse night patrol mirror device, the low-cost active near-infrared night vision technology is applied to a medical night patrol scene in a miniaturized, monocular display and modular wearing mode for the first time, the effect that the nurse does not disturb sleep of a patient at all during night room patrol work can be achieved, multiple wearing modes are provided, and the intelligent portable nurse night patrol mirror device is convenient to use. And the use requirements of different nurses can be met. By means of the night patrol mirror device, during night patrol, whether a patient has the risk of abnormal vital sign signals or not can be judged without turning on a lamp or assisting with other visible light sources or making contact with the patient, and the influence on sleep of the patient in a ward in the night patrol process can be avoided to the maximum extent.

The invention provides a vital sign real-time monitoring method and system based on intelligent wearable underwear, and the method comprises the steps: obtaining the vital sign data of a user based on a sensor integrated with the intelligent wearable underwear; acquiring the current motion activity state of the user through an accelerometer and a gyroscope which are arranged in the intelligent wearable underwear, and acquiring a vital sign data threshold value of the user based on the current motion activity state of the user, the vital sign data change of the user in a preset time window and the vital sign data of the user in the same historical time period; when the vital sign data of the user exceed a threshold value, giving out early warning, obtaining abnormal vital sign data of the user giving out the early warning, training an abnormity prediction model based on the abnormal vital sign data of the user, and predicting the abnormal condition of the vital signs of the user based on the abnormity prediction model. Through the method and the corresponding system, the vital sign data threshold value is customized for each user by considering the state of the user, and the monitoring accuracy is improved.

According to the fixed radar and unmanned aerial vehicle cooperative vital sign monitoring system provided by the invention, large-range basic monitoring is realized through a millimeter wave radar array, and the specific unmanned aerial vehicle executes accurate re-checking after receiving an abnormal signal. The system adopts a multi-stage response mechanism: when the fixed radar detects abnormal signs, the unmanned aerial vehicle is dispatched to lock a target through millimeter wave echo feature matching, identity binding is performed by using a binocular camera, body temperature data is supplemented in combination with an infrared sensor, and finally an early warning decision is generated through a multi-modal fusion algorithm. The hardware level defines the lowest configuration requirements (including a millimeter wave radar module, a visual identification unit, an infrared temperature measurement assembly and an obstacle avoidance system) of the unmanned aerial vehicle, and the software level designs a cross-device data space-time alignment model and a dynamic task scheduling protocol. Application scenes cover night emergency monitoring of nursing homes, health monitoring of gymnasium groups and non-contact patrol of hospital isolation wards, and the problems that in a traditional scheme, tracking of moving targets is lost, and identity information is unhooked are effectively solved.

The invention discloses an intelligent nursing method and device. Multi-modal target intelligent equipment is integrated on an Internet of Things platform, unified access of multiple brands and multiple types of equipment is realized, and equipment adaptation repeated work and data island cost are reduced. Meanwhile, a binding relationship between the target intelligent device and each responsibility area is established. Risks such as falling, off-bed non-return and abnormal vital signs can be sensed in real time, events from the target intelligent device are pushed to the Internet of Things platform and the terminal in the responsibility area in real time for active early warning, the pain point of response lag of traditional manual patrol is solved, and the safety risk of emergency situations is reduced. And the event can be directionally pushed to the corresponding person in charge, so that the disposition disorder caused by information mass sending is avoided, and the accurate docking demand of the nursing service is ensured. Under the condition that the event is the alarm event, the text of the alarm event is converted into the natural language audio to be broadcasted on the terminal and visually presented to the nursing personnel, so that the cognitive load of the nursing personnel is reduced, and the early warning information is prevented from being missed.

The invention provides an underground environment and human life monitoring method and system, and electronic equipment, and the method comprises the steps: obtaining an underground environment signal and a human life signal of a to-be-monitored site collected by a collection module disposed on a rescue robot according to a first monitoring period, and analyzing the signals to obtain underground environment data and human life data; and a related alarm signal is output according to the underground environment data and the human body life data, and the alarm signal is sent to an information terminal of an overground rescue worker through a UWB receiving module. The change of the underground environment is monitored in real time by comprehensively analyzing the micro-seismic energy data and the rock sound data, whether a dangerous case occurs or not and the rock burst danger level are judged, the blood oxygen saturation degree and the body temperature of a survivor can be monitored, automatic monitoring data are more comprehensive, perfect and intelligent, and the safety of the survivor is improved. The rescue personnel on the well can carry out reasonable, effective, safe and timely rescue according to the vital sign abnormal degree of the survivors, the positions of the survivors and the underground rock burst danger level.

The invention relates to the technical field of vital sign monitoring, in particular to a high-dependence ward patient vital sign abnormity early warning nursing intervention method and system.The method comprises the following steps that heart rate systolic pressure and diastolic pressure blood oxygen data are collected, abnormity or recovery is marked according to intervals, abnormity and recovery time spans are accumulated to form a time structure, and the time structure is calculated; the method comprises the following steps: acquiring heart rate, blood pressure and blood oxygen data, calculating an abnormal occupancy ratio, analyzing cross-cycle progressive increase, extracting a sequence of first abnormal time points, counting continuous anomalies and successive evolution, and outputting early warning intervention information when continuous progressive increase is met and unsynchronized accumulation and sequential counting rules are recovered. The method comprises the following steps: forming a nursing period state sequence, accumulating abnormity and recovering a time span to construct a time structure, combining an abnormity occupation ratio and a preorder period change, extracting a first abnormity time point, sorting and identifying continuous abnormity and evolution, and outputting early warning according to a continuous increasing and unsynchronized accumulation and sequence counting recovering rule. Invalid alarm is reduced; and intervention consistency is improved.

The present invention provides a nursing data sharing method and system based on the Internet of Things. The method comprises: S1: real-time collection of multimodal nursing data through a distributed IoT terminal cluster to form a characteristic signal set; S2: construction of a data coordination engine and execution of federated learning preprocessing operations on the characteristic signal set to form a coordination signal stream; S3: dynamic calculation of data contribution weights and generation of a verification signal chain; S4: real-time monitoring of abnormal vital sign indicators in the coordination signal stream to generate a trigger signal vector; S5: updating incentive mechanism parameters based on the contribution credentials in the verification signal chain, optimizing the service quality strategy based on the network resource allocation scheme in the trigger signal vector, and forming a closed-loop control signal. This invention can solve the data island effect problem that exists in the process of medical nursing data sharing.