Ward patient sign information acquisition and intelligent internet-of-things control method

Abstract

The invention relates to the technical field of medical Internet of Things, and provides a ward patient sign information acquisition and intelligent Internet of Things control method, which comprises the following steps: acquiring patient multi-dimensional sign data by adopting a wearable sensor and sickbed peripheral monitoring equipment, and transmitting the data to a hospital data center through Internet of Things gateway hybrid networking after data processing; the data center analyzes the data by adopting a dynamic threshold model and generates early warning; and according to the early warning type and the emergency degree, intelligent equipment in the ward is controlled, and a control strategy is optimized according to equipment feedback. According to the invention, a traditional fixed threshold monitoring mode is changed, personalized accurate monitoring is realized, and misjudgment and missed judgment are avoided; multi-device cooperative collection and intelligent control linkage are achieved, and the ward environment and the device state are accurately adjusted; through data mining and prediction, the health risk of the patient is intervened in advance, a monitoring, control and optimized closed-loop system is formed, the medical monitoring accuracy and reliability and the ward intelligent management level are remarkably improved, and the personalized rehabilitation requirements of the patient are met.

Description

Technical Field

[0001] The present invention relates to the technical field of medical Internet of Things, and specifically to a method for collecting vital sign information of patients in a ward and controlling it through intelligent Internet of Things. Background Art

[0002] With the rapid development of medical information technology and intelligent systems, hospital ward management is gradually transitioning from traditional manual models to automated and intelligent ones. Real-time monitoring and effective control of patient vital signs are crucial for disease diagnosis, treatment planning, and patient recovery. With the continued maturity of technologies such as the Internet of Things and big data, their application to ward management, enabling efficient collection and intelligent control of patient vital signs, has become a key development direction for improving medical service quality and optimizing the allocation of medical resources.

[0003] However, the current technology for monitoring and controlling the vital signs of patients in wards has many shortcomings. Most solutions use fixed thresholds to determine whether the patient's vital signs are normal, which cannot fully consider the impact of individual differences in patients and environmental changes on vital sign data, and can easily lead to misjudgments or missed judgments. In terms of data processing, data mining and analysis capabilities are weak, making it difficult to extract valuable information from large amounts of vital sign data, and unable to achieve accurate predictions of patients' health status. In addition, the coordination between devices in the ward is poor, and monitoring and control are independent of each other. It is impossible to adjust the ward environment and equipment status in a timely and accurate manner according to changes in patients' vital signs, making it difficult to meet patients' personalized rehabilitation needs, which to a certain extent limits the improvement of medical service quality. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a method for collecting vital signs information of patients in wards and intelligent IoT control, which solves the problems of inaccurate monitoring, unintelligent control and inability to meet personalized needs caused by fixed thresholds, rough data processing and poor equipment coordination in traditional ward monitoring.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for collecting vital sign information of patients in a ward and controlling it through intelligent IoT, comprising the following steps: Step 1: The patient's body temperature, heart rate, blood pressure, and blood oxygen saturation data are collected in real time through a vital sign collection terminal installed in the ward. The vital sign collection terminal includes a wearable sensor and a monitoring device fixed around the bed. The wearable sensor collects physiological signals from the human body surface through flexible electrodes and microsensors, while the monitoring device fixed around the bed collects the patient's sleeping posture and body movement data through pressure sensors and infrared sensors. Step 2: Preprocess the collected vital sign data and use the sliding window filtering algorithm to remove noise in the data. The sliding window filtering algorithm formula is: ,in is the original data sequence, is the filtered data, is the sliding window size; and converts the original data into a standard data format; Step 3: The pre-processed vital sign data is sent to the hospital data center through the IoT gateway in the ward. The IoT gateway uses hybrid networking technology, combining short-range communication networks based on Bluetooth and ZigBee with long-range communication networks based on 5G to achieve stable data transmission. Step 4: The hospital data center receives the vital sign data and analyzes the data using a preset dynamic threshold model. The dynamic threshold model adjusts the normal threshold range of each vital sign indicator in real time based on the patient's historical health information such as age, gender, and underlying diseases. The dynamic threshold upper limit calculation formula is: , the dynamic threshold lower limit calculation formula is: ,in is the standard threshold, is the age effect coefficient, is the gender influence coefficient, is the basic disease impact coefficient, 、 、 is a weight parameter; if the vital sign data exceeds the dynamic threshold range, an early warning message is generated; Step 5: Based on the type and urgency of the warning information, control instructions are sent to the intelligent control devices in the ward through the IoT gateway. When the heart rate data continues to exceed the upper limit of the dynamic threshold, the sound and light alarm devices in the ward are controlled to sound an alarm, and the air conditioning temperature and ventilation volume of the fresh air system in the ward are adjusted to create a comfortable environment for the patient. Step 6: After the intelligent control device executes the control instruction, it will feed back the execution status to the hospital data center. The hospital data center will dynamically adjust the control strategy based on the feedback information and subsequent vital sign data.

[0006] Preferably, in step 1, the wearable sensor is further integrated with a positioning module, which obtains the patient's position information in the ward in real time through the positioning module, and sends the position information together with the vital sign data.

[0007] Preferably, in step 2, when pre-processing the vital sign data, the change rate of the vital sign data at adjacent time points is also calculated, and the change rate calculation formula is: ,in 、 is the data of adjacent time points, is the time interval; if the rate of change If the change rate exceeds the preset threshold, the data point is marked and a secondary filtering process is performed on the marked data point.

[0008] Preferably, in step 4, the dynamic threshold model further modifies the normal threshold range of the physical sign index according to environmental factors such as season and day and night, and the correction formula is: ,in is the threshold value calculated initially, is the environmental factor influence coefficient, is the environmental factor weight parameter.

[0009] Preferably, in step five, when the warning information is generated, the warning information is sent to the nurse station terminal and the patient's family's mobile terminal at the same time. After receiving the warning information, the nurse station terminal and the patient's family's mobile terminal display the patient's real-time vital sign data and location information.

[0010] Preferably, the intelligent control device also includes a bed posture adjustment device. When the patient's sleeping posture data shows that the patient maintains the same bad posture for a long time, the hospital data center sends instructions to the bed posture adjustment device to adjust the inclination angle and height of the bed.

[0011] Preferably, in step 6, the hospital data center uses a reinforcement learning algorithm to dynamically adjust the control strategy based on the feedback information and subsequent vital sign data, and optimizes the control parameters of the intelligent control device through continuous trial and error and reward mechanism. The reward function calculation formula in the reinforcement learning algorithm is: .

[0012] Preferably, the vital sign collection terminal is also provided with an emergency button. When the patient presses the emergency button, the vital sign collection terminal immediately sends an emergency signal to the hospital data center. After receiving the emergency signal, the hospital data center starts the emergency response process.

[0013] Preferably, the hospital data center regularly performs data mining and analysis on the stored vital sign data, establishes a patient's health trend model, and uses a time series prediction algorithm to predict the patient's health status in the future. The time series prediction algorithm formula is: ,in is the predicted value, For historical data, is the coefficient, is the order, is the error term; and the control strategy is adjusted in advance according to the prediction results.

[0014] Preferably, the Internet of Things gateway is provided with a data encryption module to perform end-to-end encryption on the transmitted vital sign data to ensure the security and privacy during data transmission.

[0015] The present invention provides a method for collecting vital sign information of patients in wards and controlling it through intelligent IoT. It has the following beneficial effects: 1. This invention uses a dynamic threshold model combined with the patient's age, gender, underlying diseases and environmental factors to adjust the normal threshold range of physical sign indicators in real time, changing the traditional fixed threshold monitoring mode to achieve truly personalized monitoring. At the same time, it uses a reinforcement learning algorithm to dynamically optimize the control strategy based on control feedback, so that monitoring and control form a closed-loop intelligent system, accurately capture patient health abnormalities and intervene in time, significantly improving the accuracy and reliability of medical monitoring.

[0016] 2. The present invention uses wearable sensors and bedside monitoring equipment to collaboratively collect multi-dimensional vital sign data, covering physiological parameters and behavioral data. The Internet of Things gateway hybrid networking technology ensures stable data transmission and accurately controls smart devices in the ward based on early warning information, breaking through the limitations of single monitoring or extensive control of existing technologies and promoting more intelligent ward management. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] Example: Please see the attached Figure 1 Taking a general ward of a general hospital as an example, a 60-year-old male patient with a 5-year history of hypertension is admitted to the ward. The embodiment of the present invention provides a method for collecting vital sign information of patients in the ward and controlling it through intelligent IoT, including the following steps: Step 1: Vital sign data collection Upon admission, nurses fitted the patient with a wearable sensor, which integrates multiple functional modules. The heart rate module uses flexible electrodes to collect bioelectrical signals from the human body's surface, converting the weak electrical signals generated by the heartbeat into electrical pulses, collecting data once per second. The temperature module uses a high-precision micro-temperature sensor to measure the patient's surface temperature every two minutes. The blood oxygen saturation module utilizes photoplethysmography, which emits and receives light of specific wavelengths to detect changes in hemoglobin concentration in the blood, acquiring blood oxygen saturation data once a minute. Furthermore, the patient's bed is surrounded by a pressure sensor array and an infrared motion sensor. The pressure sensor array consists of 16 pressure sensors, distributed beneath the mattress, to monitor the patient's sleeping position, collecting pressure distribution data every 10 seconds. The infrared motion sensor, mounted on the ceiling directly above the bed, detects the patient's movement using infrared sensing technology and records any movement. Furthermore, a non-contact blood pressure monitoring device uses an oscillometric method to automatically measure the patient's blood pressure daily at 8:00, 12:00, 16:00, and 20:00.

[0020] Step 2: Data preprocessing The vital signs collection terminal transmits the collected raw data to the data preprocessing unit. Taking heart rate data as an example, the raw data sequence is , set the sliding window size , according to the sliding window filtering algorithm , calculate the first filtered data : , the filtered data sequence can be obtained by sequential calculation At the same time, calculate the change rate of the heart rate data at adjacent time points, such as (Time interval is 2 minutes, here we assume that the time interval is simplified for the convenience of calculation). If the preset change rate threshold is 1.5, If the threshold is not exceeded, the data point is normal; if the change rate of a data point exceeds the threshold, the point is marked and subjected to secondary filtering. Finally, all preprocessed vital sign data are converted into a data format that complies with the HL7 standard.

[0021] Step 3: Data Transfer Preprocessed vital sign data is transmitted via an IoT gateway. The IoT gateway combines short-range communication networks based on Bluetooth and ZigBee with long-range communication networks based on 5G. Wearable sensors transmit data to the IoT gateway via Bluetooth, while monitoring devices near the bed aggregate data to the IoT gateway via the ZigBee network. The IoT gateway encapsulates and encrypts the data using the AES-256 encryption algorithm. The encryption key is updated every 24 hours by the hospital data center. The encrypted data is sent to the hospital data center via the 5G network. During transmission, data rates can reach 100Mbps, with an average latency of approximately 30ms.

[0022] Step 4: Data analysis and early warning After receiving the vital sign data, the hospital data center uses the dynamic threshold model to analyze it. For heart rate data, the standard threshold Set to 60-100 times / minute, age affects the coefficient The gender effect coefficient is 0.3 based on the patient's age of 60. For males, the value is 0, and the influence coefficient of basic diseases The value is 0.5, the weight parameter , , According to the dynamic threshold upper limit calculation formula , the upper limit of the dynamic threshold of heart rate is: Similarly, according to the calculation formula of the dynamic threshold lower limit, the heart rate dynamic threshold lower limit can be obtained as: If the patient's heart rate is 125 beats / minute at a certain moment, exceeding the upper limit of the dynamic threshold, a high heart rate warning message will be generated, and the warning level will be emergency. At the same time, environmental factors are taken into consideration. It is summer daytime, and the environmental factors have an impact on the coefficient. The value is 0.05, and the environmental factor weight parameter , according to the dynamic threshold environment correction algorithm formula , correct the upper limit of the heart rate dynamic threshold: .

[0023] Step 5: Intelligent Control When the hospital data center generates an urgent high heart rate warning, it immediately sends control commands to the intelligent control devices in the ward via the IoT gateway. Commands are sent to the audio and visual alarm system, causing it to emit an 80-decibel alarm and flashing red lights; to the air conditioning controller, adjusting the ward temperature from 26°C to 24°C; and to the fresh air system controller, increasing the ventilation volume from 60 cubic meters per hour to 90 cubic meters per hour. Simultaneously, the warning information, the patient's real-time vital signs, and location information are sent to the nurses' station terminal and the patient's family's mobile device. The nurses' station terminal issues pop-up notifications and voice announcements, while the patient's family's mobile device receives push notifications via text message and app. If the patient's sleeping posture data indicates that the patient has remained in the prone position for more than one hour, the hospital data center sends commands to the bed's posture adjustment device to raise the head of the bed by 10 degrees and the legs by 5 degrees to improve the patient's sleeping position.

[0024] Step 6: Control Feedback and Strategy Adjustment After the intelligent control device executes the control command, it will feedback the execution status to the hospital data center. After receiving the temperature adjustment command, the air conditioner controller will start to adjust the temperature and feedback the actual temperature and successful adjustment status to the hospital data center within 30 seconds after the adjustment is completed. The hospital data center uses the reinforcement learning algorithm to dynamically adjust the control strategy based on the feedback information and subsequent vital sign data. If the patient's heart rate drops to 110 beats / minute within 15 minutes after the control measures are implemented, according to the reinforcement learning algorithm reward function , give a +1 reward, and then optimize the control parameters such as the air conditioning temperature adjustment range and the sound and light alarm triggering conditions based on the reward situation. The hospital data center also regularly conducts data mining and analysis on the stored vital sign data, and uses time series prediction algorithms to predict the patient's health status. Assuming that the patient's blood pressure data for the past 7 days is selected as historical data, the order , the coefficients are obtained by fitting calculation , , , according to the time series prediction algorithm formula , predicting the patient's blood pressure for the next day. If an upward trend in blood pressure is predicted, the control strategy is adjusted in advance, such as increasing the frequency of blood pressure monitoring to once an hour and appropriately adjusting the lighting and noise level in the ward to create an environment more conducive to stabilizing the patient's blood pressure. When the patient presses the emergency button on the vital signs collection terminal, the terminal immediately sends an emergency signal with the highest priority to the hospital data center. Upon receiving the emergency signal, the hospital data center initiates the emergency response process, notifying the on-duty doctor and nurse to visit the ward within 2 minutes to check on the patient.

[0025] In the above embodiment, wearable sensors and bedside monitoring equipment are used to collect vital signs data such as patient temperature and heart rate. After pre-processing with algorithms such as sliding window filtering, the data is encrypted and transmitted to the hospital data center using the Internet of Things gateway. The data center uses algorithms such as dynamic threshold models to analyze the data, generate warnings for situations where the threshold is exceeded, and control smart devices such as sound and light alarms and air conditioners in the ward based on the warning type and urgency. After the device executes the instruction and feedbacks the status, the data center combines subsequent vital signs data with algorithms such as reinforcement learning to adjust the control strategy. At the same time, it can also predict the patient's health status through time series prediction algorithms. When the patient presses the emergency button, an emergency response is immediately initiated, realizing the intelligent collection of vital signs information of patients in the ward and precise Internet of Things control in all aspects.

[0026] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for collecting vital signs information of patients in wards and controlling them through intelligent IoT, characterized in that: The following steps are involved: Step 1: The patient's body temperature, heart rate, blood pressure, and blood oxygen saturation data are collected in real time through a vital sign collection terminal installed in the ward. The vital sign collection terminal includes a wearable sensor and a monitoring device fixed around the bed. The wearable sensor collects physiological signals from the human body surface through flexible electrodes and microsensors, while the monitoring device fixed around the bed collects the patient's sleeping posture and body movement data through pressure sensors and infrared sensors. Step 2: Preprocess the collected vital sign data and use the sliding window filtering algorithm to remove noise in the data. The sliding window filtering algorithm formula is: ,in is the original data sequence, is the filtered data, is the sliding window size; and converts the original data into a standard data format; Step 3: The pre-processed vital sign data is sent to the hospital data center through the IoT gateway in the ward. The IoT gateway uses hybrid networking technology, combining short-range communication networks based on Bluetooth and ZigBee with long-range communication networks based on 5G to achieve stable data transmission. Step 4: The hospital data center receives the vital sign data and analyzes the data using a preset dynamic threshold model. The dynamic threshold model adjusts the normal threshold range of each vital sign indicator in real time based on the patient's historical health information such as age, gender, and underlying diseases. The dynamic threshold upper limit calculation formula is: , the dynamic threshold lower limit calculation formula is: ,in is the standard threshold, is the age effect coefficient, is the gender influence coefficient, is the basic disease impact coefficient, 、 、 is a weight parameter; if the vital sign data exceeds the dynamic threshold range, an early warning message is generated; Step 5: Based on the type and urgency of the warning information, control instructions are sent to the intelligent control devices in the ward through the IoT gateway. When the heart rate data continues to exceed the upper limit of the dynamic threshold, the sound and light alarm devices in the ward are controlled to sound an alarm, and the air conditioning temperature and ventilation volume of the fresh air system in the ward are adjusted to create a comfortable environment for the patient. Step 6: After the intelligent control device executes the control instruction, it will feed back the execution status to the hospital data center. The hospital data center will dynamically adjust the control strategy based on the feedback information and subsequent vital sign data.

2. The method for collecting vital signs information of ward patients and controlling them through intelligent IoT according to claim 1, characterized in that: In step 1, the wearable sensor is also integrated with a positioning module, which obtains the patient's position information in the ward in real time through the positioning module and sends the position information together with the vital sign data.

3. The method for collecting vital signs information of ward patients and controlling them through intelligent IoT according to claim 1, characterized in that: In step 2, when pre-processing the vital sign data, the change rate of the vital sign data at adjacent time points is also calculated. The change rate calculation formula is: ,in 、 is the data of adjacent time points, is the time interval; if the rate of change If the change rate exceeds the preset threshold, the data point is marked and a secondary filtering process is performed on the marked data point.

4. The method for collecting vital signs information of ward patients and controlling them through intelligent IoT according to claim 1, characterized in that: In step 4, the dynamic threshold model also modifies the normal threshold range of the physical sign index according to environmental factors such as season and day and night. The correction formula is: ,in is the threshold value calculated initially, is the environmental factor influence coefficient, is the environmental factor weight parameter.

5. The method for collecting vital signs information of ward patients and controlling them through intelligent IoT according to claim 1, characterized in that: In step five, when the warning information is generated, the warning information is sent to the nurse station terminal and the patient's family's mobile terminal at the same time. After receiving the warning information, the nurse station terminal and the patient's family's mobile terminal display the patient's real-time vital sign data and location information.

6. The method for collecting vital signs information of ward patients and controlling them through intelligent IoT according to claim 1, characterized in that: The intelligent control device also includes a bed posture adjustment device. When the patient's sleeping posture data shows that the patient maintains the same bad posture for a long time, the hospital data center sends an instruction to the bed posture adjustment device to adjust the inclination angle and height of the bed.

7. The method for collecting vital signs information of ward patients and controlling them through intelligent IoT according to claim 1, characterized in that: In step 6, the hospital data center uses a reinforcement learning algorithm to dynamically adjust the control strategy based on the feedback information and subsequent vital sign data. Through continuous trial and error and a reward mechanism, the control parameters of the intelligent control device are optimized. The reward function calculation formula in the reinforcement learning algorithm is: 。 8. The method for collecting vital signs information of ward patients and controlling them through intelligent IoT according to claim 1, characterized in that: The vital sign collection terminal is also provided with an emergency button. When the patient presses the emergency button, the vital sign collection terminal immediately sends an emergency signal to the hospital data center. After receiving the emergency signal, the hospital data center starts the emergency response process.

9. The method for collecting vital signs information of ward patients and controlling them through intelligent IoT according to claim 1, characterized in that: The hospital data center regularly conducts data mining and analysis on the stored vital sign data, establishes a patient's health trend model, and uses a time series prediction algorithm to predict the patient's health status in the future. The time series prediction algorithm formula is: ,in is the predicted value, For historical data, is the coefficient, is the order, is the error term; and the control strategy is adjusted in advance according to the prediction results.

10. The method for collecting vital signs information of ward patients and controlling them through intelligent IoT according to claim 1, characterized in that: The IoT gateway is equipped with a data encryption module to perform end-to-end encryption on the transmitted vital sign data to ensure the security and privacy of the data during transmission.

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