Method of adjusting a base flow under high level ppep, control device and storage medium

Abstract

The embodiment of the present disclosure provides a method, a control device and a computer readable storage medium for adjusting a basic flow under a high level PEEP, wherein the method comprises calculating an estimated value of a current flow based on a prior estimation model of the basic flow; measuring a current flow value; data fusion is performed on the estimated value of the current flow and the measured current flow value to obtain an optimal estimated value of the flow; and adjusting the basic flow according to the deviation between the optimal estimated value of the flow and a set flow value. The embodiment of the present disclosure considers the noise caused by the oscillation of the exhalation valve diaphragm under the high level PEEP in the working process of the breathing machine, and corrects the errors in the model and the calculation in real time according to the environmental factors, so as to ensure the rapidity and stability of the data fusion process, and improve the control precision and accuracy of the basic flow rate.

Description

Technical Field

[0001] This disclosure relates to the field of ventilator technology, and more specifically, to a method, control device, and computer-readable storage medium for regulating basal flow at high levels of PEEP (positive end-expiratory pressure). Background Technology

[0002] The BiPAP mode on a ventilator allows for spontaneous breathing at both high and low PEEP (positive end-expiratory pressure) levels. During the expiratory phase, the expiratory valve, turbine, and flow valve adjust the PEEP levels according to the set baseline flow rate, facilitating the expulsion of CO2 from the tubing, providing the necessary oxygen concentration, and assisting in triggering ventilation. During high-level expiration, the expiratory valve must maintain a high PEEP pressure while simultaneously opening to ensure unobstructed baseline flow. Due to the valve's internal structure, under high PEEP pressure and a certain baseline flow rate, the expiratory diaphragm may vibrate, causing instability in the baseline flow rate and resulting in oscillations, which introduce noise during monitoring and adjustment. Similar issues arise in other modes when high PEEP levels are present. Current ventilators do not have corresponding measures to address this problem under high PEEP conditions.

[0003] Furthermore, when adjusting the basal flow rate, significant disturbances can cause large fluctuations in the basal flow rate. If the expiratory valve is closed directly at a high PEEP level, there will be no basal flow, which can have negative effects on the patient's ventilation process.

[0004] Therefore, for the reasons mentioned above, a method for regulating the base flow rate at high PEEP levels is needed to avoid large fluctuations in base flow rate regulation under high PEEP pressure. Summary of the Invention

[0005] This disclosure provides a method and control device for adjusting the basal flow rate of a ventilator at a high level of PEEP, in order to solve one or more of the above-mentioned technical problems.

[0006] According to a first aspect, embodiments of this disclosure provide a method for adjusting a base flow rate under a high level of PEEP, comprising: calculating an estimate of the current flow rate based on a prior estimation model of the base flow rate; measuring the current flow rate; performing data fusion on the estimate of the current flow rate and the measured current flow rate to obtain an optimal estimate of the flow rate; and controlling the adjustment of the base flow rate based on the deviation between the optimal estimate of the flow rate and a set flow rate value.

[0007] The embodiments of this disclosure take into account the instability of the baseline flow rate caused by the vibration of the breathing diaphragm under high-level PEEP. When controlling the baseline flow rate, the optimal estimate of the flow rate is calculated first, instead of directly using the measured flow rate value, thereby avoiding the impact of breathing diaphragm vibration on the baseline flow rate control.

[0008] Optionally, according to an embodiment of the first aspect of this disclosure, the state equation for the basic flow control is:

[0009]

[0010] in Let u(k) be the base flow rate at time k; A is the system state matrix, which represents the relationship between the flow rate at the current time and the flow rate at the previous time; u(k) is the control quantity applied to the exhalation valve; control matrix B is the control matrix associated with the exhalation valve, which represents the correspondence between the control quantity and the flow rate change. The process noise is used; the prior estimation model for the basic flow rate is obtained based on the state equation of the control.

[0011] ,

[0012] in This means using the optimal estimate of the flow rate at time k-1 to predict the estimated flow rate at time k; This is the optimal estimate of the flow rate at time k-1.

[0013] The embodiments disclosed above establish a priori estimation model for basic flow based on basic flow control, so as to facilitate the calculation of the estimated value of basic flow.

[0014] Optionally, the system state matrix A represents the linear relationship between the flow rate at the current moment and the flow rate at the previous moment; the control matrix B represents the relationship between the change in voltage of the exhalation valve and the change in flow rate.

[0015] Optionally, according to an embodiment of the first aspect of this disclosure, the optimal estimate of the flow rate is obtained by fusing the estimated current flow rate and the measured current flow rate using the Kalman filtering method.

[0016] Optionally, according to an embodiment of the first aspect of this disclosure, at a high level of PEEP, process noise... This includes noise caused by interference with flow stability due to diaphragm oscillations. The covariance of this process noise is Q, and the coefficient of the covariance Q increases as the PEEP pressure increases.

[0017] Optionally, according to an embodiment of the first aspect of this disclosure, the covariance Q is calculated using the following equation:

[0018]

[0019]

[0020] in, It is a coefficient related to changes in PEEP pressure. The deviation of the currently set PEEP pressure from the default PEEP pressure setting during ventilation. This is a correction factor for the PEEP pressure difference to ensure... .

[0021] Optionally, according to an embodiment of the first aspect of this disclosure, the process of fusing the estimated current flow rate and the measured current flow rate to obtain the optimal estimate of the flow rate includes: calculating the covariance of the flow rate error according to a covariance calculation model; calculating the Kalman gain coefficient; and calculating the optimal estimate of the flow rate based on the estimated current flow rate, the measured current flow rate, and the Kalman gain coefficient, wherein the Kalman gain coefficient... ,in For state The error covariance, where H is the measurement system parameter, H T Let H be the transpose of H, and R be the covariance of the measurement error. R is corrected in real time based on one or more of the changes in temperature, humidity, altitude, and zero-point voltage.

[0022] Optionally, according to an embodiment of the first aspect of this disclosure, R is calculated using the following equation:

[0023]

[0024]

[0025] in It is the correction factor for R. This represents the change in current temperature relative to the standard ambient temperature. This represents the change in current altitude relative to standard altitude. This represents the change in the current zero-point voltage of the flow sensor relative to the zero-point voltage under standard conditions. This represents the change in current relative humidity relative to standard ambient humidity. , , , As the corresponding environmental correction factor, ensure .

[0026] Optionally, according to an embodiment of the first aspect of this disclosure, wherein controlling the adjustment of the base flow based on the deviation between the optimal estimate of the flow and the set flow value includes adjusting the base flow using PI control.

[0027] According to a second aspect, embodiments of this disclosure provide a control device for regulating a base flow rate at a high level of PEEP, comprising at least one processor and at least one storage device storing program code, characterized in that the program code is adapted to be loaded and executed by the processor to perform the method described in any of the preceding claims.

[0028] According to a third aspect, embodiments of this disclosure provide a computer-readable storage medium having computer instructions stored thereon, wherein, when the computer instructions are executed by a processor, the method for regulating base flow under high PEEP as described in any of the preceding claims is implemented.

[0029] The above embodiments take into account the noise caused by the oscillation of the expiratory valve diaphragm under high PEEP during ventilator operation, and correct the errors in the model and calculation in real time according to environmental factors, thereby ensuring the speed and stability of the data fusion process and improving the control precision and accuracy of the baseline flow rate.

[0030] Implementing any apparatus of this disclosure does not necessarily require achieving all of the advantages described above simultaneously. Other features and advantages of this disclosure will be set forth in the following description and will be apparent in part from the description and embodiments, or may be learned by practicing this disclosure. The objects and advantages of embodiments of this disclosure may be realized and obtained by means of the structures pointed out in the description, claims, and drawings. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments will be briefly introduced below. Obviously, the drawings described below only relate to some embodiments of this disclosure, and are not intended to limit this disclosure.

[0032] Figure 1 This is a general system block diagram of basic flow regulation according to an embodiment of the present disclosure;

[0033] Figure 2 This is a flowchart of a method for adjusting base flow under high PEEP according to an embodiment of the present disclosure;

[0034] Figure 3 According to an embodiment of this disclosure, it is used for Figure 2 The flowchart of the data fusion method shown is shown.

[0035] Figure 4A and Figure 4BThese are the baseline flow waveforms obtained using existing baseline flow regulation methods and baseline flow regulation methods according to the above embodiments of this disclosure, respectively, under high-level PEEP. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. Various different embodiments can be combined with each other to constitute other embodiments not shown in the following description. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0037] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, “an” or “a” and similar terms do not necessarily indicate a quantity limitation. Terms such as “comprising” or “including” mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. Terms such as “connected” or “linked” are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as “upper,” “lower,” “left,” and “right” are used only to indicate relative positional relationships, which may change accordingly when the absolute position of the described object changes.

[0038] Figure 1 A general block diagram of a base flow regulation system according to one embodiment of the present disclosure is shown. The system 100 includes an air-oxygen drive 101, a flow sensor 102, a proximal respiratory flow and pressure sensor 103, an expiratory valve 104, and a base flow regulation controller (not shown). The system may also include more or fewer components as needed.

[0039] The ventilator system is supplied with ventilation airflow by an air-oxygen drive unit 101, which may include a flow valve and / or turbine connecting an air source and an oxygen source. During expiration, when the patient's expiratory airflow is essentially complete, decreasing to near 0 L / min (a positive value can be preset), and before a new inhalation occurs, basal flow rate adjustment begins. A given PEEP pressure level is provided by the air-oxygen drive unit, and the expiratory valve 104 opens to a certain degree, ensuring that the PEEP pressure level is not released by the expiratory valve and that the basal flow rate reaches the set value. A flow sensor 102 detects the basal flow rate, while a proximal respiratory flow and pressure sensor 103 detects the patient's inhaled and exhaled gas flow rates and airway pressure to determine whether the patient's expiration process is essentially complete; this sensor can be located downstream of the flow sensor. For example, when the expiratory flow rate decreases to a preset value (e.g., a preset ratio of expiratory flow rate to peak expiratory flow rate), basal flow rate adjustment can begin.

[0040] Generally speaking, the exhalation valve can be used as a basic flow rate adjustment device, and there is a certain correlation between the voltage of the exhalation valve and the flow rate.

[0041] The functionality of the basic flow regulation controller can be implemented entirely or partially through software, such as embedded system software within a microcontroller (MCU). This controller can send control signals to relevant components or obtain necessary data from them, and implement the flow described below. The controller may include one or more processing units and a memory. The processing units include, but are not limited to, one or more of a microcontroller (MCU), a central processing unit (CPU), a digital signal processor (DSP), and a field-programmable gate array (FPGA). The memory may include non-volatile memory storing operating instructions and / or data.

[0042] Figure 2 This is a flowchart of a method for adjusting the base flow rate under a high level PEEP according to an embodiment of the present disclosure.

[0043] First, the state equation for expiratory basal flow control needs to be established based on the basal flow regulation system. The state equation for the discrete system can be described as:

[0044] (1)

[0045] is the baseline flow rate at time k; u(k) is the control variable, which can be the voltage change for an exhalation valve whose opening is adjusted by voltage. The process noise, at high PEEP pressures, is caused by diaphragm oscillations interfering with and affecting flow stability, resulting in noise of a certain amplitude and frequency. The covariance of this process noise is denoted by Q, and its magnitude is determined by the noise's amplitude-frequency characteristics. As the PEEP pressure increases, the coefficient for calculating the noise covariance Q can be appropriately increased. Adjusting the covariance Q can help the control process reach a stable state more quickly.

[0046] More specifically, the correction for the covariance Q of the process noise can be achieved using the following equation:

[0047] (2)

[0048] The process noise covariance is corrected by the change in PEEP pressure. It is a correction factor related to PEEP pressure changes, which can improve the speed and stability of the control process. You can choose to have it directly related to the PEEP pressure or related to the deviation of the PEEP pressure from the default PEEP pressure setting during ventilation. It can be calculated using the following equation (3):

[0049] (3)

[0050] in, The deviation of the currently set PEEP pressure from the default PEEP pressure setting during ventilation; This is a correction factor for the PEEP pressure difference to ensure... .

[0051] A is the system state transition matrix, which represents the relationship between the current flow rate and the previous flow rate at the exhalation valve. For the exhalation valve, this relationship can be linear. B is the control matrix, which can be determined by the relationship between the control quantity of the exhalation valve and the change in flow rate. When the exhalation valve controls the flow rate through voltage, the control quantity is the change in voltage.

[0052] A priori estimation model for the basic flow rate is established based on this state equation:

[0053] (4)

[0054] in This can be expressed as using the optimal estimate of the flow rate at time k-1 to predict the estimated flow rate at time k. This is the optimal estimate of the flow rate at time k-1.

[0055] After the basic flow regulation begins (step 201), the estimated value of the current flow is calculated based on the prior estimation model of the basic flow. (Step 202). In step 203, the flow rate value Z(k) measured by the flow sensor is obtained. In step 204, the estimated value of the current flow rate and the measured flow rate value are fused to obtain the optimal estimate of the current flow rate.

[0056] Figure 3 According to an embodiment of this disclosure, it is used for Figure 2 The flowchart of the data fusion method shown is shown.

[0057] This embodiment uses the Kalman filter algorithm for data fusion.

[0058] Based on the prior estimation model, construct the error covariance equation for the flow rate, and calculate the prior value of the error covariance at time k from the error covariance at time k-1 and the covariance of the process error (step 301).

[0059] (5)

[0060] For state The error covariance, For state The error covariance. Based on equations (2) and (3), Q is corrected according to the PEEP pressure.

[0061] In step 302, the Kalman gain is calculated. To obtain the Kalman gain, a measurement equation is established.

[0062] (6)

[0063] H represents the measurement system parameters. Measurement error. Flow sensors may be affected by environmental factors such as temperature, humidity, altitude, and zero-point drift of electronic components, resulting in measurement error. The covariance of the flow sensor is denoted by R, and its value is determined by a combination of environmental factors. Weighting coefficients can be applied to various environmental factors. For example, when a temperature increase is detected, the measurement error of the flow sensor can be corrected in real time based on changes in temperature or humidity, altitude, and zero-point drift of electronic components. This means that the covariance R of the measurement error can be reasonably corrected. Adjusting the value of the covariance R can help the control process reach a stable state more quickly.

[0064] More specifically, R can be modified using the following equation:

[0065] (7)

[0066] This can be a correction factor obtained by considering one or more factors such as temperature, humidity, altitude, and zero-point drift of electronic components. For example, the following equation considers changes in temperature, humidity, altitude, and zero-point voltage:

[0067] (8)

[0068] in This represents the change in current temperature relative to the standard ambient temperature. This represents the change in current altitude relative to standard altitude. This represents the change in the current zero-point voltage of the flow sensor relative to the zero-point voltage under standard conditions. This represents the change in current relative humidity relative to standard ambient humidity. , , , As the corresponding environmental correction factor, ensure .

[0069] Therefore, the accuracy of basic flow rate control can be improved by taking into account the influence of environmental factors.

[0070] Calculate the Kalman gain according to the following equation (step 302):

[0071] (9)

[0072] Based on the following optimization estimation equation, the current flow rate is estimated... The current measured value of flow rate Z(k) and the gain coefficient K g (k) Calculate the optimal estimate (Step 303):

[0073] (10)

[0074] In addition, the updated flow error covariance is calculated using the following equation. This is for use in the next calculation.

[0075] (11)

[0076] Back Figure 2The flowchart illustrates that after obtaining the optimal estimate, in step 205, the baseline flow rate of the exhalation valve can be controlled based on the deviation between the optimal estimated flow rate and the set flow rate value. Various suitable methods can be used to control the exhalation valve, such as PI and PID control. In this embodiment, PI regulation is preferred because it helps reduce and eliminate errors, has strong anti-interference capabilities, avoids the influence of noise on the control system, and achieves the goal of quickly reaching the set value. In step 206, the baseline flow rate adjustment is determined by comparing it with the set flow rate value. If it differs from the set flow rate value, the process returns to step 202 to continue adjusting the flow rate. The baseline flow rate adjustment for the current cycle will end when the next inhalation cycle begins.

[0077] In ventilator ventilation mode, a basic flow control method based on Kalman filtering is adopted. The estimated value of the current flow rate and the measured flow rate value are fused. In this process, the covariance Q of the process control error is corrected according to the degree of oscillation of the expiratory valve diaphragm caused by PEEP pressure, and the covariance R of the observation error is corrected in real time according to environmental factors. This ensures the speed and stability of Kalman filter control and improves the control accuracy of the basic flow rate.

[0078] The above methods can be implemented entirely or partially through software programs. They can be used to adjust the basic flow by sending control signals to relevant components or by obtaining the required data from relevant components.

[0079] Figure 4A It directly adopts the basic flow waveform of the existing PID control process. Figure 4B This is the basic flow waveform obtained by using the basic flow adjustment method described in the above embodiments. From Figure 4A It can be seen that the baseline flow rate exhibits significant oscillations during adjustment. In practical applications, these oscillations may be exacerbated depending on the specific patient condition. Figure 4B As can be seen, this patented technology can effectively reduce flow fluctuations and quickly reach a stable flow state. The above-described embodiments of this disclosure reach a stable state faster during the adjustment process, demonstrating good performance in terms of stability, adjustment accuracy, and adjustment speed.

[0080] The above description is merely an exemplary embodiment of this disclosure and is not intended to limit the scope of protection of this disclosure, which is determined by the appended claims.

Claims

1. A control method for adjusting the base flow rate under high PEEP, comprising: The estimated value of the current flow is calculated based on the prior estimation model of the basic flow. Measure the current flow rate; The optimal estimate of the current flow rate is obtained by fusing the estimated current flow rate with the measured current flow rate. The basic flow rate is adjusted based on the deviation between the optimal estimated flow rate and the set flow rate value, wherein the state equation for the basic flow rate control is: , in Let u(k) be the base flow rate at time k; A is the system state matrix, which represents the relationship between the flow rate at the current time and the flow rate at the previous time; u(k) is the control quantity applied to the exhalation valve; control matrix B is the control matrix related to the exhalation valve, which represents the correspondence between the control quantity and the flow rate change. The process noise is used; the prior estimation model for the basic flow rate is obtained based on the state equation of the control. , in This means using the optimal estimate of the flow rate at time k-1 to predict the estimated flow rate at time k; This is the optimal estimate of the flow rate at time k-1. At high PEEP levels, process noise This includes noise caused by interference with flow stability due to diaphragm oscillations. The covariance of this process noise is Q, and its magnitude is determined by the amplitude-frequency characteristics of the noise. As the PEEP pressure increases, the coefficient of the covariance Q increases. The covariance Q is calculated using the following equation. in, It is a coefficient related to changes in PEEP pressure. The deviation of the currently set PEEP pressure from the default PEEP pressure setting during ventilation. This is a correction factor for the PEEP pressure difference to ensure... .

2. The control method for adjusting the base flow under high-level PEEP according to claim 1, wherein the optimal estimate of the flow is obtained by data fusion of the estimated value of the current flow and the measured current flow value using the Kalman filter method.

3. The control method for adjusting the base flow rate under high-level PEEP according to claim 2, wherein the optimal estimate of the flow rate is obtained by data fusion of the estimated current flow rate and the measured current flow rate includes calculating the covariance of the flow error according to a covariance calculation model; calculating the Kalman gain coefficient; and calculating the optimal estimate of the flow rate based on the estimated current flow rate, the measured current flow rate, and the Kalman gain coefficient, wherein the Kalman gain coefficient... ,in For state The error covariance, where H is the measurement system parameter, H T Let H be the transpose of H, and R be the covariance of the measurement error. R is corrected in real time based on one or more of the changes in temperature, humidity, altitude, and zero-point voltage.

4. The control method for adjusting the base flow rate under high-level PEEP according to claim 3, wherein R is calculated by the following equation: in It is the correction factor for R. This represents the change in current temperature relative to the standard ambient temperature. This represents the change in current altitude relative to standard altitude. This represents the change in the current zero-point voltage of the flow sensor relative to the zero-point voltage under standard conditions. This represents the change in current relative humidity relative to standard ambient humidity. , , , As the corresponding environmental correction factor, ensure .

5. The control method for adjusting the base flow rate under high PEEP according to claim 1, wherein adjusting the base flow rate based on the deviation between the optimal estimated value of the flow rate and the set flow rate value includes adjusting the base flow rate using PI control.

6. A control device for adjusting a base flow rate at a high level of PEEP, comprising at least one processor and at least one storage device, the storage device storing program code, characterized in that, The program code is adapted to be loaded and run by the processor to perform the control method according to any one of claims 1 to 5.

7. A computer-readable storage medium having computer instructions stored thereon, wherein, When the computer instructions are executed by the processor, the control method for adjusting the base flow under a high level of PEEP as described in any one of claims 1 to 5 is implemented.

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