Control method for neonatal nursing bed

Abstract

The invention relates to the technical field of neonatal nursing, and particularly provides a control method for a neonatal nursing bed, and the method comprises the following steps: S1, collecting heart rate variability in real time based on a heart rate variability measurement assembly pasted on the body surface of a neonate; s2, acquiring a heart rate variability reference value according to the historical heart rate variability data set; s3, acquiring a heart rate variability threshold value according to the heart rate variability reference value and the heart rate variability error value; s4, analyzing whether the heart rate variability is smaller than a heart rate variability threshold value or not, if yes, executing the step S5, and if not, returning to the step S1; s5, based on a PI controller, obtaining an angle adjustment amount according to a difference value between the heart rate variability and a heart rate variability threshold value; s6, controlling an angle adjusting mechanism to adjust the inclination angle of the bed body according to the angle adjustment amount, and then returning to the step S1; by means of the method, neonatal gastroesophageal reflux can be found in time and the inclination angle of the bed body can be adjusted in time without intervention of medical staff.

Description

Technical Field

[0001] This application relates to the technical field of neonatal care, and more particularly, to a control method for a neonatal care bed. Background Art

[0002] Existing neonatal care beds include a bed body and an angle adjustment mechanism for adjusting the tilt angle of the bed body. The existing technology can assist newborns in reducing gastroesophageal reflux by increasing the tilt angle of the bed body using the angle adjustment mechanism. The existing technology requires medical staff to analyze whether a newborn has gastroesophageal reflux. Since the existing technology relies on medical staff to analyze whether a newborn has gastroesophageal reflux, and medical staff cannot continuously monitor the status of newborns, there is a problem that newborns may continue to feel uncomfortable due to the inability to detect gastroesophageal reflux in newborns in a timely manner and adjust the tilt angle of the bed body in a timely manner. In addition, the existing technology also has a problem that the labor intensity of medical staff is high due to the need to analyze whether a newborn has gastroesophageal reflux and adjust the tilt angle of the bed body manually.

[0003] In response to the above problems, there is currently no effective technical solution. It should be noted that the above information disclosed in this part is only used to understand the background of the inventive concept of the present invention, and therefore may include information that does not constitute the prior art. Summary of the Invention

[0004] The purpose of this application is to provide a control method for a neonatal care bed, which can effectively solve the problems that newborns may continue to feel uncomfortable due to the inability to detect gastroesophageal reflux in newborns in a timely manner and adjust the tilt angle of the bed body in a timely manner, and the labor intensity of medical staff is high due to the need to analyze whether a newborn has gastroesophageal reflux and adjust the tilt angle of the bed body manually.

[0005] In a first aspect, this application provides a control method for a neonatal care bed. The neonatal care bed includes a bed body and an angle adjustment mechanism for adjusting the tilt angle of the bed body. The control method for the neonatal care bed includes the following steps: S1. Real-time collect the heart rate variability based on a heart rate variability measurement component pasted on the body surface of the newborn; S2. Obtain a heart rate variability reference value according to a historical heart rate variability data set, where the historical heart rate variability data set is a set of all historical heart rate variabilities within a preset time period before the current time node; S3. Obtain a heart rate variability threshold according to the heart rate variability reference value and a heart rate variability error value, where the heart rate variability threshold is the difference between the heart rate variability reference value and the heart rate variability error value; S4. Analyze whether the heart rate variability is less than the heart rate variability threshold. If so, execute step S5. If not, return to step S1; S5. Obtain an angle adjustment amount based on the difference between the heart rate variability and the heart rate variability threshold by means of a proportional-integral (PI) controller, where the angle adjustment amount is greater than 0; S6. Control an angle adjustment mechanism to adjust the tilt angle of the bed according to the angle adjustment amount, and then return to step S1.

[0006] A control method for a neonatal care bed provided by the present application can analyze whether a neonate has gastroesophageal reflux based on heart rate variability, and adaptively adjust the tilt angle of the bed according to the difference between the heart rate variability and the heart rate variability threshold when gastroesophageal reflux occurs. That is, the present application can timely detect neonatal gastroesophageal reflux and timely adjust the tilt angle of the bed without the intervention of medical staff. Therefore, the present application can effectively solve the problems of continuous discomfort of neonates caused by the inability to timely detect neonatal gastroesophageal reflux and timely adjust the tilt angle of the bed, and the high labor intensity of medical staff caused by the need to analyze whether a neonate has gastroesophageal reflux and adjust the tilt angle of the bed manually.

[0007] Optionally, step S3 includes: S31. Obtain neonatal individual characteristic information, where the neonatal individual characteristic information includes age and weight change amount, and the weight change amount is the difference between the current actual weight of the neonate and the birth weight; S32. Query a pre-constructed mapping relationship table of individual characteristics and variability error according to the neonatal individual characteristic information to obtain a heart rate variability error value; S33. Obtain a heart rate variability threshold according to the heart rate variability reference value and the heart rate variability error value.

[0008] Optionally, the neonatal individual characteristic information further includes the feeding method.

[0009] Optionally, step S32 includes: S321. Query a pre-constructed mapping relationship table of individual characteristics and variability error according to the neonatal individual characteristic information to obtain a preliminary heart rate variability error value; S322. Obtain the current state of the neonate based on video surveillance analysis, where the current state of the neonate includes the sleep state and the activity state S323. When it is analyzed that the neonate is in the sleep state, multiply the preliminary heart rate variability error value by a preset first adjustment coefficient to obtain a heart rate variability error value; S324. When it is analyzed that the neonate is in the activity state, multiply the preliminary heart rate variability error value by a preset second adjustment coefficient to obtain a heart rate variability error value.

[0010] Optionally, step S322 includes: A1. Analyze whether there is an activity phenomenon in the newborn based on video monitoring, and obtain the physiological parameters of the newborn through a physiological parameter acquisition component. The physiological parameters include respiratory rate and heart rate. A2. If it is analyzed that the newborn has an activity phenomenon and the physiological parameters are greater than or equal to the preset physiological parameter threshold, then take the activity state as the current state of the newborn. A3. If it is analyzed that the newborn does not have an activity phenomenon and / or the physiological parameters are less than the preset physiological parameter threshold, then take the sleep state as the current state of the newborn.

[0011] Optionally, step S2 includes: S21. Calculate the first quartile, the third quartile, and the interquartile range of the historical heart rate variability dataset based on all historical heart rate variabilities. S22. Determine the outlier range according to the first quartile, the third quartile, and the interquartile range. The upper limit value of the outlier range is the sum of the product of the third quartile and the interquartile range and the first preset weight, and the lower limit value of the outlier range is the difference between the first quartile and the product of the interquartile range and the second preset weight. S23. Identify and remove the historical heart rate variabilities in the historical heart rate variability dataset that exceed the outlier range as abnormal data. S24. Take the mean of all historical heart rate variabilities in the historical heart rate variability dataset after removing abnormal data as the heart rate variability baseline value.

[0012] Optionally, step S5 includes: S51. Calculate the standard deviation of heart rate variability based on all historical heart rate variabilities in the historical heart rate variability dataset. S52. Query the pre - constructed mapping relationship between the standard deviation of heart rate variability, the proportional control coefficient, and the integral control coefficient to obtain the target proportional control coefficient and the target integral control coefficient. S53. Take the target proportional control coefficient as the proportional control coefficient of the proportional - integral PI controller, and take the target integral control coefficient as the integral control coefficient of the proportional - integral PI controller. S54. Based on the proportional - integral PI controller, obtain the angle adjustment amount according to the difference between the heart rate variability and the heart rate variability threshold.

[0013] Optionally, the control method for the newborn care bed further includes the step: S7. When the heart rate variability is greater than the heart rate variability threshold and the tilt angle of the bed body is greater than 0, control the angle adjustment mechanism to reduce the tilt angle of the bed body until the difference between the heart rate variability and the heart rate variability threshold is less than or equal to the preset difference or the tilt angle of the bed body is reduced to 0.

[0014] Optionally, step S7 includes: S71. When the heart rate variability is greater than the heart rate variability threshold and the tilt angle of the bed body is greater than 0, control the angle adjustment mechanism to reduce the tilt angle of the bed body on the condition that the reduction rate of the tilt angle is less than or equal to the preset angle reduction rate until the difference between the heart rate variability and the heart rate variability threshold is less than or equal to the preset difference or the tilt angle of the bed body is reduced to 0.

[0015] Optionally, step S6 includes: S61. Analyze whether the angle adjustment amount is greater than the angle adjustment upper limit value. If so, control the angle adjustment mechanism to adjust the tilt angle of the bed body according to the angle adjustment upper limit value, and then return to step S1. If not, control the angle adjustment mechanism to adjust the tilt angle of the bed body according to the angle adjustment amount, and then return to step S1.

[0016] As can be seen from the above, a control method for a neonatal care bed provided by the present application can analyze whether a neonate has gastroesophageal reflux based on heart rate variability, and adaptively adjust the tilt angle of the bed body according to the difference between the heart rate variability and the heart rate variability threshold when gastroesophageal reflux occurs. That is, the present application can timely detect neonatal gastroesophageal reflux and timely adjust the tilt angle of the bed body without the intervention of medical staff. Therefore, the present application can effectively solve the problems of continuous discomfort of neonates caused by the inability to timely detect neonatal gastroesophageal reflux and timely adjust the tilt angle of the bed body, and the large labor intensity of medical staff caused by the need to manually analyze whether a neonate has gastroesophageal reflux and adjust the tilt angle of the bed body. Description of the Drawings

[0017] Figure 1 It is a flowchart of a control method for a neonatal care bed provided by an embodiment of the present application. Detailed Embodiments

[0018] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and illustrated herein can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application provided in the drawings below is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present application.

[0019] It should be noted that similar reference numerals and letters denote similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of the present application, terms such as "first" and "second" are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0020] In a first aspect, the present application provides a control method for a neonatal care bed. The neonatal care bed includes a bed body and an angle adjustment mechanism for adjusting the tilt angle of the bed body. The control method for the neonatal care bed includes the following steps: S1. Real-time collect the heart rate variability based on a heart rate variability measurement component pasted on the body surface of the neonate; S2. Obtain a heart rate variability reference value according to a historical heart rate variability data set, where the historical heart rate variability data set is a set of all historical heart rate variabilities within a preset time period before the current time node; S3. Obtain a heart rate variability threshold according to the heart rate variability reference value and the heart rate variability error value, where the heart rate variability threshold is the difference between the heart rate variability reference value and the heart rate variability error value; S4. Analyze whether the heart rate variability is less than the heart rate variability threshold. If so, execute step S5; if not, return to step S1; S5. Based on a proportional-integral (PI) controller, obtain an angle adjustment amount according to the difference between the heart rate variability and the heart rate variability threshold, where the angle adjustment amount is greater than 0; S6. Control the angle adjustment mechanism to adjust the tilt angle of the bed body according to the angle adjustment amount, and then return to step S1.

[0021] Since step S1 real-time collects the heart rate variability based on a heart rate variability measurement component pasted on the body surface of the neonate, this embodiment is equivalent to non-invasively monitoring the heart rate variability of the neonate in real time. The heart rate variability is the change of the difference between successive heartbeat cycles, and the heart rate variability can reflect the physiological stress state of the neonate.

[0022] The historical heart rate variability dataset of step S2 can be stored in a cloud server or a local server. The historical heart rate variability dataset is a set of all historical heart rate variabilities within a preset time period before the current time node, that is, the historical heart rate variability dataset contains the heart rate variability data of the neonate over a period of time in the past. The preset time period of this embodiment can be set according to actual needs, such as the previous 1 hour, the previous day, etc. The heart rate variability baseline value of this embodiment is calculated based on this historical dataset. The heart rate variability baseline value represents the normal level of the neonate's heart rate variability. This embodiment can calculate the heart rate variability baseline value by calculating the mean of the historical heart rate variability dataset. The heart rate variability baseline value calculated based on this method can characterize the average state of the neonate's heart rate variability.

[0023] The heart rate variability error value in step S3 can be an experience value. The heart rate variability error value is a value for correcting the heart rate variability baseline value. This embodiment obtains the heart rate variability threshold by subtracting the heart rate variability error value from the heart rate variability baseline value. The heart rate variability threshold is used as the critical value for judging whether the neonate's heart rate variability is abnormal.

[0024] Since heart rate variability is the change in the difference between successive heartbeat cycles, heart rate variability can reflect the physiological stress state. When gastroesophageal reflux occurs, the physiological stress of the neonate increases and the sympathetic nerve becomes active. At this time, the heart rate variability will continue to decrease. Therefore, step S4 can analyze whether the neonate has gastroesophageal reflux by analyzing whether the heart rate variability is less than the heart rate variability threshold. Step S4 compares the real-time collected heart rate variability with the heart rate variability threshold. If the heart rate variability is less than the heart rate variability threshold, it is considered that the neonate's heart rate variability is abnormal and the neonate has gastroesophageal reflux. At this time, the tilt angle of the nursing bed needs to be adjusted to assist the neonate in reducing gastroesophageal reflux. If the heart rate variability is greater than or equal to the heart rate variability threshold, it is considered that the neonate's heart rate variability is normal and the neonate does not have gastroesophageal reflux. At this time, there is no need to adjust the tilt angle of the nursing bed.

[0025] The proportional-integral (PI) controller in step S5 is an existing control algorithm. This control algorithm adjusts and controls according to the deviation between the set target value and the actual value. In this solution, the input of the PI controller is the difference between the heart rate variability and the heart rate variability threshold, and the output is the angle adjustment amount. Since it is necessary to assist the neonate in reducing gastroesophageal reflux by increasing the tilt angle of the bed body of the neonate nursing bed when gastroesophageal reflux occurs, the angle adjustment amount of this embodiment is greater than 0. This embodiment can use the proportional-integral (PI) controller to achieve precise calculation and control of the angle adjustment amount to ensure the smoothness and accuracy of the adjustment of the bed body tilt angle.

[0026] The angle adjustment mechanism in step S6 receives the angle adjustment amount from the proportional-integral (PI) controller and adjusts the tilt angle of the bed body of the neonatal care bed according to this angle adjustment amount. Specifically, the angle adjustment mechanism in this embodiment can be a driving device such as a motor, a hydraulic cylinder or a pneumatic cylinder that can precisely adjust the tilt angle of the bed body. Taking the angle adjustment mechanism as a pneumatic cylinder as an example, the piston end of the angle adjustment mechanism is hinged to the bed body, and the tilt angle of the bed body can be adjusted by extending or retracting the piston end in this embodiment. After the angle adjustment is completed, the system returns to step S1 to continue the heart rate variability monitoring and control, forming a closed-loop control.

[0027] A control method for a neonatal care bed provided by this application can analyze whether a neonate has gastroesophageal reflux based on heart rate variability, and adaptively adjust the tilt angle of the bed body according to the difference between the heart rate variability and the heart rate variability threshold when gastroesophageal reflux occurs. That is, this application can timely detect neonatal gastroesophageal reflux and timely adjust the tilt angle of the bed body without the intervention of medical staff. Therefore, this application can effectively solve the problems of continuous discomfort of neonates caused by the inability to timely detect neonatal gastroesophageal reflux and timely adjust the tilt angle of the bed body, and the large labor intensity of medical staff caused by the need to manually analyze whether a neonate has gastroesophageal reflux and adjust the tilt angle of the bed body.

[0028] In some specific embodiments, the heart rate variability measurement component can select an electrocardiogram sensor, which is fixed on the surface of the neonatal chest skin by pasting electrode patches to collect electrocardiogram signals in real time and extract heart rate variability parameters from them. The historical heart rate variability data set can be set to collect the heart rate variability data in the previous 2 hours before the current time node. The heart rate variability reference value can be calculated as the mean value of the historical heart rate variability data set. The heart rate variability error value can be preset as a fixed value, and the proportional control coefficient and integral control coefficient of the proportional-integral (PI) controller can be pre-tuned according to empirical values or experimental data. The angle adjustment mechanism can adopt a lead screw mechanism driven by a motor to achieve precise adjustment of the tilt angle of the bed body, and the angle adjustment range can be set to 0 - 15 degrees. When the heart rate variability is less than the heart rate variability threshold, the angle adjustment amount calculated by the PI controller is used to control the motor to drive the lead screw to rotate, thereby increasing the tilt angle of the bed body. For example, the initial angle is 0 degrees, and after one adjustment, the angle can be increased to 3 degrees.

[0029] In some preferred embodiments, step S3 includes: S31. Obtain neonatal individual characteristic information, where the neonatal individual characteristic information includes age and weight change amount, and the weight change amount is the difference between the current actual weight of the neonate and the birth weight; S32. Query the pre-constructed mapping relation table of individual characteristics and variability error according to the neonatal individual characteristic information to obtain the heart rate variability error value; S33. Obtain the heart rate variability threshold according to the heart rate variability reference value and the heart rate variability error value.

[0030] In step S31, the neonatal individual characteristic information is obtained. The individual characteristic information includes age and weight change. The age can be obtained from the birth record of the neonate. The weight change is the difference between the current actual weight and the birth weight of the neonate. The actual weight is measured by a weighing device, and the birth weight is obtained from the birth record of the neonate. The weight change is calculated by subtracting the birth weight from the actual weight. In step S32, the pre-constructed mapping relation table of individual characteristics and variability error is queried to obtain the heart rate variability error value. This mapping relation table reflects the corresponding relationship between individual characteristics and the heart rate variability error value. The mapping relation table can be pre-stored in the system in the form of a data table. For example, the mapping relation table can include age ranges, weight change ranges, and the corresponding heart rate variability error values. When the age and weight change of the neonate are determined, the system searches in the mapping relation table according to this individual characteristic information to obtain the corresponding heart rate variability error value. Since the weight change can reflect the health and growth of the neonate, and the age, health, and growth of the neonate are all related to the allowable fluctuation range of heart rate variability, this embodiment is equivalent to adaptively adjusting the heart rate variability error value according to the age and weight change of the neonate, so that the heart rate variability threshold is more in line with the actual physiological state of the neonate, thereby effectively improving the accuracy of the heart rate variability threshold, and further making the control method of the neonatal care bed more intelligent and individualized.

[0031] In some specific embodiments, it is assumed that the pre-constructed mapping relation table of individual characteristics and variability error is stored in the system. The mapping relation table includes the age range, weight change range, and the corresponding heart rate variability error value of the neonate. For example, the age range can be divided into "0 - 7 days", "8 - 14 days", "15 - 30 days", etc., and the weight change range can be divided into "below - 200g", "-200g to +100g", "+100g and above", etc. Each combination of age range and weight change range corresponds to a preset heart rate variability error value.

[0032] In some preferred embodiments, the neonatal individual characteristic information further includes the feeding method. The feeding methods in this embodiment preferably include breast feeding, formula feeding, and mixed feeding. Different feeding methods can affect the digestive system status and metabolic level of the neonate, and thus affect heart rate variability. For example, neonates fed breast milk may be more easily digested than those fed formula milk, and there may be differences in their physiological states, which can be reflected in heart rate variability. This embodiment is equivalent to making the input of more comprehensive neonatal individual characteristic information possible when querying the pre-constructed mapping relation table of individual characteristics and variability errors according to the neonatal individual characteristic information by adding the dimension of the feeding method of neonatal individual characteristic information, so as to obtain a heart rate variability error value that better fits the actual situation of the neonate. Therefore, this embodiment can further improve the accuracy of the heart rate variability error value, and thus further improve the accuracy of the heart rate variability threshold. In some specific embodiments, the pre-constructed mapping relation table of individual characteristics and variability errors can be configured to include the multi-dimensional mapping relation between the feeding method, age, weight change amount, and heart rate variability error value. For example, the mapping relation table can record that for a neonate who is breast-fed, 7 days old, and has a weight change amount of +100 g, the corresponding heart rate variability error value is X1; for a neonate who is formula-fed, 7 days old, and has a weight change amount of +100 g, the corresponding heart rate variability error value is X2.

[0033] In some preferred embodiments, step S32 includes: S321. Query the pre-constructed mapping relation table of individual characteristics and variability errors according to the neonatal individual characteristic information to obtain a preliminary heart rate variability error value; S322. Obtain the current state of the neonate based on video surveillance analysis. The current state of the neonate includes the sleep state and the activity state; S323. When it is analyzed that the neonate is in the sleep state, multiply the preliminary heart rate variability error value by a preset first adjustment coefficient to obtain the heart rate variability error value; S324. When it is analyzed that the neonate is in the activity state, multiply the preliminary heart rate variability error value by a preset second adjustment coefficient to obtain the heart rate variability error value.

[0034] The process of obtaining the preliminary heart rate variability error value in step S321 is the same as the process of obtaining the heart rate variability error value in step S32 above. The process of obtaining the current state of the newborn based on video monitoring analysis in step S322 can be as follows: Use a camera to collect video images of the newborn, and then analyze the activity state of the newborn through image processing technology. For example, the amplitude and frequency of the newborn's limb movements in the video image can be analyzed to determine whether the newborn is in a sleeping state or an active state. As an implementation manner, when it is analyzed that the amplitude and frequency of the newborn's limb movements are small, it can be determined that the newborn is in a sleeping state; on the contrary, when it is analyzed that the amplitude and frequency of the newborn's limb movements are large, it can be determined that the newborn is in an active state. The preset first adjustment coefficient and the preset second adjustment coefficient in this embodiment are both parameters for adjusting the preliminary heart rate variability error value, and the first adjustment coefficient and the second adjustment coefficient can be set according to the actual application scenario and experimental data. As an example, considering that the heart rate variability of the newborn is relatively stable in the sleeping state, while the heart rate variability fluctuates greatly in the active state, therefore, the first adjustment coefficient can be set to a value less than 1, such as 0.8, to reduce the heart rate variability error value in the sleeping state; the second adjustment coefficient can be set to a value greater than 1, such as 1.2, to increase the heart rate variability error value in the active state. Since the current state of the newborn is associated with the heart rate variability, and this embodiment can analyze the current state of the newborn and adjust the preliminary heart rate variability error value based on the current state of the newborn, this embodiment can make the obtained heart rate variability error value more accurate, provide a more accurate basis for the subsequent obtained heart rate variability threshold, and make the control of the newborn care bed based on the heart rate variability threshold more accurate and effective.

[0035] In some preferred implementation manners, step S322 includes: A1. Analyze whether there is an activity phenomenon of the newborn based on video monitoring, and obtain the physiological parameters of the newborn based on the physiological parameter acquisition component, where the physiological parameters include respiratory rate and heartbeat; A2. If it is analyzed that the newborn has an activity phenomenon and the physiological parameters are greater than or equal to the preset physiological parameter threshold, then take the active state as the current state of the newborn; A3. If it is analyzed that the newborn does not have an activity phenomenon and / or the physiological parameters are less than the preset physiological parameter threshold, then take the sleeping state as the current state of the newborn.

[0036] In step A1, to analyze whether a newborn has any activity through video monitoring, it can be achieved by analyzing whether there are pixel changes in the video frame sequence through image processing technology. For example, when motion detection algorithms such as background difference method or optical flow method analyze that there are obvious motion regions or motion trajectories in the video image, it is determined that the newborn has activity. The physiological parameter acquisition component can be a heart rate sensor and a respiratory sensor. The heart rate sensor can be a non-contact electrocardiogram sensor or a photoplethysmogram sensor, and the respiratory sensor can be a thermistor respiratory sensor or a piezoelectric respiratory sensor. In this embodiment, the respiratory rate and heart rate data of the newborn can be obtained through the physiological parameter acquisition component. The preset physiological parameter threshold in step A2 is a physiological parameter critical value preset for distinguishing the activity state and sleep state of the newborn. The preset physiological parameter threshold can be set according to the physiological characteristics of the newborn and clinical experience. For example, the upper limit value of the average respiratory rate and the upper limit value of the average heart rate of the newborn can be used as the preset physiological parameter threshold. In step A3, when the video monitoring does not detect any activity or the physiological parameters collected by the physiological parameter acquisition component are lower than the preset physiological parameter threshold, it is determined that the newborn is in a sleep state. This embodiment can more accurately distinguish the activity state and sleep state of the newborn by combining video monitoring and physiological parameters, avoiding the situation of misjudging a newborn who has slight activity and is actually in a sleep state as being in an active state due to only using video monitoring for state discrimination. Therefore, this embodiment can effectively improve the accuracy of newborn state determination, making the subsequent heart rate variability error value determined based on the newborn state more accurate and enhancing the reliability and effectiveness of the newborn care bed control method. For example, based on the video monitoring system analyzing the video image of the newborn, it is detected that the newborn has slight arm movement. At the same time, the physiological parameter acquisition component collects the respiratory rate of the newborn as 30 breaths per minute and the heart rate as 100 beats per minute. The preset physiological parameter threshold is set as the respiratory rate threshold of 35 breaths per minute and the heart rate threshold of 120 beats per minute. Since the respiratory rate of the newborn, 30 breaths per minute, is less than the respiratory rate threshold of 35 breaths per minute and the heart rate of 100 beats per minute is less than the heart rate threshold of 120 beats per minute, even though the video monitoring analyzes that the newborn has slight activity, the control system still determines that the newborn is in a sleep state. In another example, based on the video monitoring system analyzing the video image of the newborn, it is detected that the newborn has obvious crying and limb movement. At the same time, the physiological parameter acquisition component collects the respiratory rate of the newborn as 45 breaths per minute and the heart rate as 140 beats per minute. Since the respiratory rate of the newborn, 45 breaths per minute, is greater than the respiratory rate threshold of 35 breaths per minute and the heart rate of 140 beats per minute is greater than the heart rate threshold of 120 beats per minute, and the video monitoring also analyzes that the newborn has activity, the control system determines that the newborn is in an active state.

[0037] In some preferred embodiments, step S2 includes: S21. Calculate the first quartile, the third quartile, and the interquartile range of the historical heart rate variability dataset based on all historical heart rate variabilities; S22. Determine the outlier range according to the first quartile, the third quartile, and the interquartile range. The upper limit value of the outlier range is the sum of the product of the third quartile and the interquartile range and the first preset weight, and the lower limit value of the outlier range is the difference between the first quartile and the product of the interquartile range and the second preset weight; S23. Identify and remove the historical heart rate variabilities in the historical heart rate variability dataset that exceed the outlier range as abnormal data; S24. Take the mean value of all historical heart rate variabilities in the historical heart rate variability dataset after removing the abnormal data as the heart rate variability reference value.

[0038] The first quartile of this embodiment is preferably the historical heart rate variability (Q1) at the 25% position after sorting the historical heart rate variability in the historical heart rate variability dataset from small to large. The third quartile of step S2 is preferably the historical heart rate variability (Q3) at the 75% position after sorting the historical heart rate variability in the historical heart rate variability dataset from small to large. The interquartile range (IQR) of step S2 is preferably the difference between the historical heart rate variability at the 75% position and the historical heart rate variability at the 25% position after sorting the historical heart rate variability in the historical heart rate variability dataset from small to large. Since the first quartile, the third quartile, and the interquartile range of this embodiment are all values obtained based on the sorted data, the first quartile, the third quartile, and the interquartile range are not sensitive to the data distribution, that is, the outlier range determined according to the first quartile, the third quartile, and the interquartile range can effectively resist the interference of extreme values. Therefore, in this embodiment, the outlier range can be determined first according to the first quartile, the second quartile, and the interquartile range, and then the historical heart rate variability that exceeds the outlier range can be removed by the method of rejection to remove the historical heart rate variability that significantly deviates from the normal range, thereby effectively improving the data quality of the historical heart rate variability dataset, and further effectively improving the accuracy of the heart rate variability reference value, so as to provide a more reliable basis for the subsequent control method. In some specific embodiments, assume that the historical heart rate variability dataset contains 10 data points: [50, 55, 60, 58, 62, 53, 10, 57, 61, 59]. First, in step S21, the data is sorted to get [10, 50, 53, 55, 57, 58, 59, 60, 61, 62]. The first quartile Q1 is calculated to be 52.25, the third quartile Q3 is 60.25, and the interquartile range IQR is 8. The first preset weight and the second preset weight are both set to 1.5. The upper limit value of the outlier range is 72.25, and the lower limit value of the outlier range is 40.25. Step S23 identifies and removes the outliers that exceed the range of [40.25, 72.25], that is, the data 10 is removed. The cleaned historical heart rate variability dataset is [50, 55, 60, 58, 62, 53, 57, 61, 59]. Finally, in step S24, the mean value of the cleaned dataset is calculated, and the heart rate variability reference value is obtained to be approximately 56.11.

[0039] In some preferred embodiments, step S5 includes: S51. Calculate the heart rate variability standard deviation according to all the historical heart rate variabilities in the historical heart rate variability dataset; S52. Query the pre-constructed mapping relationship between the heart rate variability standard deviation, the proportional control coefficient, and the integral control coefficient to obtain the target proportional control coefficient and the target integral control coefficient according to the heart rate variability standard deviation; S53. Take the target proportional control coefficient as the proportional control coefficient of the proportional-integral (PI) controller, and take the target integral control coefficient as the integral control coefficient of the proportional-integral (PI) controller; S54. Based on the proportional-integral (PI) controller, obtain the angle adjustment amount according to the difference between the heart rate variability and the heart rate variability threshold.

[0040] In step S51, the standard deviation of heart rate variability is calculated as an indicator reflecting the degree of fluctuation of historical heart rate variability data. Specifically, the standard deviation calculation formula can be used to sum the squares of the deviations of each historical heart rate variability data in the historical heart rate variability dataset from the dataset mean, and then take the square root after dividing by the dataset capacity, thereby obtaining the standard deviation of heart rate variability. In step S52, the pre-constructed mapping relation table is used to query the target proportional control coefficient and the target integral control coefficient. This mapping relation table can be indexed by the standard deviation of heart rate variability and stores the proportional control coefficient and the integral control coefficient values corresponding to different standard deviation values of heart rate variability. This mapping relation table can be determined in advance through experimental data, clinical experience, or model simulation, etc. After obtaining the standard deviation of heart rate variability, this embodiment can obtain the corresponding target proportional control coefficient and target integral control coefficient through a table lookup operation. In step S53, the target proportional control coefficient and the target integral control coefficient are respectively set as the proportional control coefficient and the integral control coefficient of the proportional-integral PI controller, thereby realizing the adaptive tuning of the PI controller parameters. In step S54, the proportional-integral PI controller with set parameters is used to calculate the angle adjustment amount. The difference between the heart rate variability and the heart rate variability threshold is used as the input signal of the PI controller, and through the proportional and integral operations of the PI controller, the angle adjustment amount is obtained. Specifically, this embodiment can evaluate the individual differences and fluctuations of neonatal heart rate variability by calculating the standard deviation of heart rate variability of the historical heart rate variability dataset. If the standard deviation of heart rate variability is large, it indicates that the neonatal heart rate variability fluctuates greatly and the heart rate stability is relatively poor; if the standard deviation of heart rate variability is small, it indicates that the neonatal heart rate variability fluctuates little and the heart rate stability is relatively good. Therefore, this embodiment can make the parameters of the PI controller adaptively adjusted according to the characteristics of the neonatal's own heart rate variability by introducing the standard deviation of heart rate variability into the parameter determination process of the PI controller. For neonates with large heart rate variability fluctuations, relatively small proportional control coefficients and integral control coefficients can be set to reduce the sensitivity of angle adjustment and avoid frequent adjustment of the bed tilt angle; for neonates with small heart rate variability fluctuations, relatively large proportional control coefficients and integral control coefficients can be set to improve the sensitivity of angle adjustment, so that the bed tilt angle can be adjusted in time, thereby effectively improving the stability and effectiveness of the angle adjustment process and enhancing the intelligent and personalized level of the neonatal care bed.In some specific embodiments, the pre - constructed mapping relation table of the standard deviation of heart rate variability, the proportional control coefficient, and the integral control coefficient can be configured in the following form: When the standard deviation of heart rate variability is within the first preset range (e.g., 0 - 5 ms), the target proportional control coefficient is set to the first value (e.g., 0.5), and the target integral control coefficient is set to the second value (e.g., 0.1); when the standard deviation of heart rate variability is within the second preset range (e.g., 5 - 10 ms), the target proportional control coefficient is set to the third value (e.g., 0.8), and the target integral control coefficient is set to the fourth value (e.g., 0.2); when the standard deviation of heart rate variability is within the third preset range (e.g., greater than 10 ms), the target proportional control coefficient is set to the fifth value (e.g., 1.2), and the target integral control coefficient is set to the sixth value (e.g., 0.3).

[0041] In some preferred embodiments, the control method for the neonatal care bed further includes the steps: S7. When the heart rate variability is greater than the heart rate variability threshold and the tilt angle of the bed body is greater than 0, control the angle adjustment mechanism to reduce the tilt angle of the bed body until the difference between the heart rate variability and the heart rate variability threshold is less than or equal to the preset difference or the tilt angle of the bed body is reduced to 0.

[0042] When the dynamic heart rate variability is greater than the heart rate variability threshold and the tilt angle of the bed body is greater than 0, this embodiment controls the angle adjustment mechanism to reduce the tilt angle of the bed body until the difference between the heart rate variability and the heart rate variability threshold is less than or equal to the preset difference or the tilt angle of the bed body is reduced to 0. This embodiment can form a two - way adjustment mechanism for angle adjustment through step S7. Specifically, when the dynamic heart rate variability is less than the heart rate variability threshold, steps S5 and S6 are executed to increase the tilt angle of the bed body; when the dynamic heart rate variability is greater than the heart rate variability threshold, step S7 is executed to reduce the tilt angle of the bed body. Specifically, when the dynamic heart rate variability of the neonate is monitored and found to be higher than the heart rate variability threshold, and when the care bed is currently in a tilted state (tilt angle greater than 0), the control system initiates the reduction adjustment of the tilt angle of the bed body. Therefore, this embodiment is equivalent to effectively reducing the tilt angle of the care bed in a timely manner after the gastroesophageal reflux is alleviated to improve the comfort of the neonate.

[0043] In some preferred embodiments, step S7 includes: S71. When the heart rate variability is greater than the heart rate variability threshold and the tilt angle of the bed body is greater than 0, control the angle adjustment mechanism to reduce the tilt angle of the bed body under the condition that the tilt angle reduction rate is less than or equal to the preset tilt angle reduction rate until the difference between the heart rate variability and the heart rate variability threshold is less than or equal to the preset difference or the tilt angle of the bed body is reduced to 0.

[0044] The preset angle reduction rate of this embodiment can be set according to the physiological characteristics of the newborn and the performance of the nursing bed. For example, the preset angle reduction rate can be set to reduce by 0.5 degrees per second. Since when the heart rate variability is greater than the heart rate variability threshold and the tilt angle of the bed body is greater than 0, this embodiment controls the angle adjustment mechanism to reduce the tilt angle of the bed body under the condition that the tilt angle reduction rate is less than or equal to the preset angle reduction rate, so this embodiment can effectively avoid the situation that the newborn feels uncomfortable due to the too fast tilt angle reduction rate of the bed body, thus effectively improving the comfort of the newborn during the process of reducing the tilt angle of the bed body.

[0045] In some preferred embodiments, step S6 includes: S61. Analyze whether the angle adjustment amount is greater than the angle adjustment upper limit value. If so, control the angle adjustment mechanism to adjust the tilt angle of the bed body according to the angle adjustment upper limit value, and then return to step S1. If not, control the angle adjustment mechanism to adjust the tilt angle of the bed body according to the angle adjustment amount, and then return to step S1.

[0046] When the angle adjustment amount is analyzed to be greater than the angle adjustment upper limit value, the angle adjustment mechanism will adjust the tilt angle of the bed body according to the angle adjustment upper limit value rather than the angle adjustment amount. Thus, the single - tilt angle adjustment range of the bed body is limited within the angle adjustment upper limit value. The angle adjustment upper limit value can be preset to a reasonable value to ensure the comfort and safety of the newborn. By introducing the angle adjustment upper limit value, the single - adjustment action of the angle adjustment mechanism is restricted within a safe range, avoiding the situation that the tilt angle of the bed body suddenly changes due to the too large single - angle adjustment amount, resulting in a decrease in the comfort and safety of the newborn on the neonatal nursing bed. After completing the adjustment of the tilt angle of the bed body based on the angle adjustment upper limit value or the angle adjustment amount, the control method returns to step S1 to perform the next dynamic heart rate variability acquisition and subsequent control processes, realizing continuous monitoring and adjustment. In some specific embodiments, the angle adjustment upper limit value is set to a fixed value such as 3 degrees, 5 degrees or 8 degrees, etc. These values are determined based on the consideration of the physiological characteristics of the newborn and the safety of the nursing bed. As a preferred embodiment, the angle adjustment upper limit value can be set as an adjustable parameter so that medical staff can adjust it according to the specific conditions or clinical needs of different newborns. For example, for newborns with a lighter weight or a more fragile physiological condition, the angle adjustment upper limit value can be set smaller to achieve a more gentle angle adjustment. The angle adjustment upper limit value can also be set according to the design parameters and safety standards of the nursing bed to ensure that the single - angle adjustment of the bed body is always within a safe range.

[0047] As can be seen from the above, a control method for a neonatal care bed provided by the present application can analyze whether a neonate has gastroesophageal reflux based on heart rate variability, and adaptively adjust the tilt angle of the bed according to the difference between the heart rate variability and the heart rate variability threshold when gastroesophageal reflux occurs. That is, the present application can timely detect neonatal gastroesophageal reflux and timely adjust the tilt angle of the bed without the intervention of medical staff. Therefore, the present application can effectively solve the problems of continuous discomfort of neonates caused by the inability to timely detect neonatal gastroesophageal reflux and timely adjust the tilt angle of the bed, and the heavy labor intensity of medical staff caused by the need to manually analyze whether a neonate has gastroesophageal reflux and adjust the tilt angle of the bed.

[0048] In the embodiments provided by the present application, it should be understood that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0049] The above are only the embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A control method for a neonatal care bed, the neonatal care bed comprising a bed body and an angle adjustment mechanism for adjusting the inclination angle of the bed body, characterized in that: The control method for the neonatal care bed comprises the following steps: S1, real-time collection of heart rate variability based on a heart rate variability measurement component attached to the body surface of the newborn; S2. Obtaining a heart rate variability baseline value according to a historical heart rate variability data set, wherein the historical heart rate variability data set is a collection of all historical heart rate variabilities within a preset time period before a current time node; S3, obtaining a heart rate variability threshold value according to the heart rate variability reference value and the heart rate variability error value, wherein the heart rate variability threshold value is a difference between the heart rate variability reference value and the heart rate variability error value; S4, analyzing whether the heart rate variability is less than the heart rate variability threshold, if so, executing step S5, if not, returning to step S1; S5. Obtaining an angle adjustment amount according to a difference between the heart rate variability and the heart rate variability threshold based on a proportional-integral (PI) controller, wherein the angle adjustment amount is greater than 0; S6. Control the angle adjustment mechanism to adjust the tilt angle of the bed according to the angle adjustment amount, and then return to step S1.

2. The control method for a neonatal care bed according to claim 1, characterized in that: Step S3 includes: S31, obtaining individual characteristic information of the newborn, wherein the individual characteristic information of the newborn includes age and weight change, wherein the weight change is the difference between the current actual weight of the newborn and the birth weight; S32, querying a pre-constructed mapping relationship table between individual characteristics and variability errors according to the individual characteristic information of the newborn to obtain a heart rate variability error value; S33. Obtain a heart rate variability threshold value according to the heart rate variability reference value and the heart rate variability error value.

3. The control method for a neonatal care bed according to claim 2, characterized in that: The individual characteristic information of the newborn also includes feeding methods.

4. The control method for a neonatal care bed according to claim 2, characterized in that: Step S32 includes: S321, querying a pre-constructed mapping relationship table between individual characteristics and variability errors according to the individual characteristic information of the newborn to obtain a preliminary heart rate variability error value; S322, obtaining the current state of the newborn based on video monitoring analysis, where the current state of the newborn includes a sleeping state and an active state; S323, when it is analyzed that the newborn is in a sleeping state, multiplying the preliminary heart rate variability error value by a preset first adjustment coefficient to obtain a heart rate variability error value; S324. When it is analyzed that the newborn is in an active state, the preliminary heart rate variability error value is multiplied by a preset second adjustment coefficient to obtain a heart rate variability error value.

5. The control method for a neonatal care bed according to claim 4, characterized in that: Step S322 includes: A1. Analyze whether the newborn has any activity based on video monitoring, and obtain the newborn's physiological parameters based on the physiological parameter acquisition component, wherein the physiological parameters include respiratory rate and heart rate; A2. If it is analyzed that the newborn has the activity phenomenon and the physiological parameter is greater than or equal to the preset physiological parameter threshold, the activity state is taken as the current state of the newborn; A3. If it is analyzed that the newborn does not have the activity phenomenon and / or the physiological parameter is less than the preset physiological parameter threshold, the sleeping state is regarded as the current state of the newborn.

6. The control method for a neonatal care bed according to claim 1, characterized in that: Step S2 includes: S21, calculating the first quartile, the third quartile and the interquartile range of the historical heart rate variability data set according to all the historical heart rate variability; S22. Determine an outlier range according to the first quartile, the third quartile and the interquartile range, wherein an upper limit of the outlier range is a sum of the product of the third quartile and the interquartile range and a first preset weight, and a lower limit of the outlier range is a difference between the product of the first quartile and the interquartile range and a second preset weight; S23, identifying the historical heart rate variability in the historical heart rate variability data set that exceeds the abnormal value range as abnormal data and removing it; S24. The mean of all historical heart rate variability values ​​in the historical heart rate variability data set after the abnormal data is eliminated is used as the heart rate variability baseline value.

7. The control method for a neonatal care bed according to claim 1, characterized in that: Step S5 includes: S51, calculating the heart rate variability standard deviation according to all historical heart rate variabilities in the historical heart rate variability data set; S52, according to the heart rate variability standard deviation, query the pre-constructed mapping relationship between the heart rate variability standard deviation and the proportional control coefficient and the integral control coefficient to obtain the target proportional control coefficient and the target integral control coefficient; S53, using the target proportional control coefficient as the proportional control coefficient of the proportional-integral PI controller, and using the target integral control coefficient as the integral control coefficient of the proportional-integral PI controller; S54. Obtain an angle adjustment amount based on a proportional-integral (PI) controller according to a difference between the heart rate variability and the heart rate variability threshold.

8. The control method for a neonatal care bed according to claim 1, characterized in that: The control method for the neonatal care bed also includes the steps of: S7. When the heart rate variability is greater than the heart rate variability threshold and the inclination angle of the bed is greater than 0, control the angle adjustment mechanism to reduce the inclination angle of the bed until the difference between the heart rate variability and the heart rate variability threshold is less than or equal to a preset difference or the inclination angle of the bed is reduced to 0.

9. The control method for a neonatal care bed according to claim 8, characterized in that: Step S7 includes: S71. When the heart rate variability is greater than the heart rate variability threshold and the inclination angle of the bed is greater than 0, control the angle adjustment mechanism to reduce the inclination angle of the bed while satisfying the inclination angle reduction rate being less than or equal to a preset angle reduction rate, until the difference between the heart rate variability and the heart rate variability threshold is less than or equal to the preset difference or the inclination angle of the bed is reduced to 0.

10. The control method for a neonatal care bed according to claim 1, characterized in that: Step S6 includes: S61. Analyze whether the angle adjustment amount is greater than the angle adjustment upper limit value. If so, control the angle adjustment mechanism to adjust the inclination angle of the bed according to the angle adjustment upper limit value, and then return to step S1. If not, control the angle adjustment mechanism to adjust the inclination angle of the bed according to the angle adjustment amount, and then return to step S1.

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