Self-adaptive control method, system and equipment for breathing machine and medium

Abstract

The invention discloses a self-adaptive control method, system and equipment for a breathing machine and a medium, and the method comprises the steps: constructing a breathing gap model of a user, a PID algorithm control model of the breathing machine and a proportional valve control model based on breathing data of the user; a transfer function of a control system is obtained according to the constructed breathing gap model, the PID algorithm control model of the breathing machine and the proportional valve control model, and finally system parameters are controlled through an adaptive filtering method, so that the breathing pressure of the user is kept consistent with the target pressure. According to the method, the influence of the breathing gap is considered and fused in a control system, and compared with a traditional control method, the method has a better effect through accurate control and quick response; the self-adaptive filtering method is fused, parameters can be adjusted in real time according to the breathing mode of the user, the optimal breathing support is provided, the breathing data of the user does not need to be known in advance, dynamic adjustment can be conducted according to the real-time breathing data of the user, and breathing comfort is provided for the user quickly and efficiently.

Description

Technical Field

[0001] The present invention belongs to the technical field of ventilator control, and in particular relates to an adaptive control method, system, device and medium for a ventilator. Background Art

[0002] The ventilator is an important life support device used in the ICU and other departments of the hospital to treat patients with respiratory disorders. The ventilator delivers gas to the patient according to the mode and parameters set by the user. Among the many control parameters of the ventilator, flow and pressure are the two most basic and important parameters. Among them, many clinical application scenarios, especially the ventilation mode with capacity as the ultimate control target, require ventilation according to the set flow value, including constant flow value and regularly changing flow value, such as triangular wave flow value. Therefore, for these application scenarios, it is very important to accurately control the ventilation flow according to the set target value.

[0003] In the prior art, the inspiration triggers of invasive and non-invasive ventilators are generally divided into two types: pressure trigger and flow rate trigger. Exhalation depends on the setting of the respiratory rate, which is a fixed value. The respiratory rate of each person is different, and the respiratory rate changes at any time and in different environments. Under a fixed respiratory rate, a human-machine confrontation phenomenon will occur, that is, the air supply of the ventilator does not match the patient's inhalation. For example, when the patient is still inhaling, but the ventilator has reached the set air supply time and shuts off the air supply, the person cannot breathe and suffocates. On the contrary, the patient's inhaled volume is small, and excess oxygen is discharged from the exhaust end, resulting in gas waste. People with chronic obstructive pulmonary disease cannot breathe independently, and the ventilator provides positive pressure to help with inhalation, but cannot exhale effectively during exhalation.

[0004] Therefore, how to design a method that can adaptively control the ventilator according to the user's situation is a technical problem to be solved. Summary of the invention

[0005] Based on this, it is necessary to provide an adaptive control method, system, device and medium for a ventilator to address the problems of the prior art.

[0006] In a first aspect, an embodiment of the present application provides an adaptive control method for a ventilator, comprising the following steps: S1: Collect the user's current breathing data, which includes breathing flow, breathing pressure, breathing frequency and breathing pattern; S2: constructing a breathing gap model of the user based on the breathing data; S3: constructing a PID algorithm control model of a ventilator based on the respiratory data; S4: constructing a proportional valve control model of a ventilator based on the respiratory data; S5: obtaining a transfer function of a control system of a ventilator based on the breathing gap model, the algorithm control model and the valve control model; S6: Control the control system parameters of the ventilator by an adaptive filtering method to ensure that the user's breathing pressure is consistent with the target pressure; Among them, the transfer function of the ventilator control system is expressed by the following formula: (1); in, is the transfer function of the breathing gap model, is the transfer function of the PID algorithm control model, is the transfer function of the proportional valve control model, , and They are expressed by the following formulas: (2); (3); (4); in, is the control system gain of the adaptive ventilator, T is the time constant, is the control input, is the output pressure, , and They are the proportional gain, integral gain and differential gain of the PID algorithm control model respectively. is the gas flow through the proportional valve, is the valve control signal, is the time constant of the proportional valve.

[0007] Preferably, the breathing gap model is expressed by the following formula: (5); in, For users in time The breathing compensation force, , , and The time Respiratory flow, respiratory pressure, respiratory rate and respiratory pattern under normal conditions; , , and They are respiratory flow weight, respiratory pressure weight, respiratory frequency weight and respiratory pattern weight.

[0008] Preferably, the adaptive filtering method is a recursive least squares method or a gradient descent method.

[0009] Preferably, step S6 comprises: S61: Obtaining an error signal based on the user's respiratory flow and the target flow; S62: constructing a cost function based on the error function; S63: Adopting an adaptive filtering method to continuously optimize the cost function until a preset target is met.

[0010] Preferably, the preset target is that the cost function converges to a minimum value or reaches a maximum number of iterations of the adaptive filtering method.

[0011] Preferably, the cost function is expressed by the following formula: (6); in, is the cost function, For time The target flow rate, is the start time of a breathing cycle, The end time of a breathing cycle.

[0012] In a second aspect, an embodiment of the present application provides an adaptive control system for a ventilator, comprising: A breathing data collection unit, used to collect the user's current breathing data, the breathing data including breathing flow, breathing pressure, breathing frequency and breathing pattern; A first model building unit, configured to build a breathing gap model of the user based on the breathing data; A second model building unit, used to build a PID algorithm control model of the ventilator based on the respiratory data; A third model building unit, used for building a proportional valve control model of the ventilator based on the breathing data; A transfer function acquisition unit, used to obtain a transfer function of a control system of a ventilator based on the breathing gap model, the PID algorithm control model and the proportional valve control model; A system control unit, used to control the control system parameters of the ventilator by an adaptive filtering method so as to keep the user's breathing pressure consistent with the target pressure; Among them, the transfer function of the ventilator control system is expressed by the following formula: (1); in, is the transfer function of the breathing gap model, is the transfer function of the PID algorithm control model, is the transfer function of the proportional valve control model, , and They are expressed by the following formulas: (2); (3); (4); in, is the control system gain of the adaptive ventilator, T is the time constant, is the control input, is the output pressure, , and They are the proportional gain, integral gain and differential gain of the PID algorithm control model respectively. is the gas flow through the proportional valve, is the valve control signal, is the time constant of the proportional valve.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the executable instructions to implement the method described in the first aspect above.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is used to execute the method described in the first aspect above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention is based on the user's breathing data, respectively constructs the user's breathing gap model, the PID algorithm control model of the ventilator and the proportional valve control model, and then obtains the transfer function of the entire control system according to the constructed user's breathing gap model, the PID algorithm control model of the ventilator and the proportional valve control model, and finally controls the control system parameters of the ventilator through an adaptive filtering method to achieve the user's breathing pressure is consistent with the target pressure. The present invention fully considers the influencing factors of the breathing gap and integrates it into the control system, and then through precise control and rapid response, it has a better control effect than the traditional control method. The present invention incorporates an adaptive filtering method, and its parameters can be adjusted in real time according to the user's breathing pattern to provide the best breathing support, without knowing the user's specific breathing data in advance, but can be dynamically adjusted according to the user's real-time breathing data, and can quickly and efficiently provide users with breathing comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By referring to the following drawings, the exemplary embodiments of the present invention can be more completely understood. The drawings are used to provide a further understanding of the embodiments of the present application, and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present invention and do not constitute a limitation of the present invention. In the drawings, the same reference numerals generally represent the same parts or steps.

[0017] Figure 1 A flowchart of an adaptive control method for a ventilator provided in an embodiment of the present application; Figure 2 A schematic diagram of an adaptive control system for a ventilator provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application; Figure 4 A schematic diagram of a computer-readable storage medium provided for an embodiment of the present application. DETAILED DESCRIPTION

[0018] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0019] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0020] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0021] Example 1 Reference Figure 1 This embodiment discloses an adaptive control method for a ventilator, comprising the following steps: S1: Collect the user's current breathing data, which includes breathing flow, breathing pressure, breathing frequency and breathing pattern; Before controlling, it is necessary to collect the user's breathing data at the current moment for use in subsequent breathing control. The current moment is just a reference. Specifically, during the user's breathing process, it is necessary to collect the data within a breathing cycle in real time and dynamically.

[0022] S2: constructing a breathing gap model of the user based on the breathing data; Specifically, there are breathing gaps between the user's exhalation and inhalation stages, and the adaptive ventilator controls or eliminates the breathing gaps through the collected breathing data.

[0023] When detecting the user's breathing, set a threshold for the breathing flow rate. , when the user's breathing flow is detected and last for a certain period of time After that, it is determined that the breathing gap has been entered. It can be determined based on the user's breathing cycle, which is typically a few hundred milliseconds.

[0024] In this embodiment, the breathing gap model is expressed by the following formula: (5); in, For users in time The breathing compensation force, , , and The time Respiratory flow, respiratory pressure, respiratory rate and respiratory pattern under normal conditions; , , and They are respectively the respiratory flow weight, respiratory pressure weight, respiratory frequency weight and respiratory mode weight. In this embodiment, the weight coefficient can be set according to the following principles: (1) Initial setting: according to physiological principles and clinical trials, the initial value of the weight coefficient is set; (2) Data collection: collect specific data of users under different respiratory modes to set; (3) Use the collected data to train the model, and adjust the weight coefficient through the optimization algorithm (which will be explained in detail in the following method steps), so as to adjust the parameters of the system and achieve the optimization purpose; (4) Verification and adjustment: Test the model performance on actual users, and further adjust the weight coefficient according to the feedback results.

[0025] In this embodiment, in order to accurately describe the relationship between the control input and the respiratory output, the respiratory process is approximated as a linear model, and its transfer function can be expressed by the following formula: (2); in, The control system gain of the adaptive ventilator will affect the output amplitude of the entire system and should be adjusted appropriately. The value can ensure that the system output is within a safe and effective range. T is a time constant, which affects the response speed of the system. Reducing T can speed up the response, but may cause system instability. is the control input, is the output pressure.

[0026] S3: constructing a PID algorithm control model of a ventilator based on the respiratory data; In this embodiment, in order to compensate for the breathing gap during the user's breathing process, the breathing process is controlled by a PID algorithm, and its transfer function can be expressed as the following formula: (3); in, , and They are proportional gain, integral gain and differential gain of the PID algorithm control model. Specifically, the selection method of the three parameters is as follows: (1) :Increase It can improve the response speed of the system, but too high a speed may cause system overshoot and oscillation. It is necessary to select a suitable ; (2) :Integral control helps to eliminate the steady-state error of the system, but too high a value may cause the system to respond slowly or even become unstable. It is necessary to select a suitable ; (3) : Derivative control can predict the changing trend of the error and help reduce overshoot, but too high a value may make the system sensitive to noise.

[0027] S4: constructing a proportional valve control model of a ventilator based on the respiratory data; In this embodiment, the electromagnetic rotary proportional valve is used to adjust the gas flow of the ventilator, thereby adjusting the assistance provided by the ventilator. In order to simplify the calculation process, it is constructed as a first-order system, and its transfer function is expressed by the following formula: (4); in, is the gas flow through the proportional valve, It is the valve control signal; is the time constant of the proportional valve, which is usually related to the dynamic characteristics of the valve. The opening and closing speed of the valve can be optimized, thereby affecting the auxiliary effect of the ventilator.

[0028] S5: Based on the breathing gap model, the PID algorithm control model and the proportional valve control model, the transfer function of the control system of the ventilator is obtained; Specifically, the transfer function of the entire ventilator control system is obtained by integrating the breathing gap model, algorithm control model and valve control model. for: (1); Substituting into formula (2)-(4), we get It can be expressed as: ; The transfer function describes the proportional valve control signal To output pressure The relationship between the breathing gap, PID control algorithm and proportional valve control strategy includes the influencing factors. , , , , T and The value of can optimize the performance of the control system to better match the user's breathing needs, thereby achieving the purpose of self-adaptation.

[0029] S6: Control the control system parameters of the ventilator by an adaptive filtering method to ensure that the user's breathing pressure is consistent with the target pressure; In this embodiment, the adaptive filtering method is a recursive least squares method (RLS) or a gradient descent method.

[0030] Specifically, step S6 includes the following steps: S61: Obtaining an error signal based on the user's respiratory flow and the target flow; Specifically, the error signal It is expressed by the following formula: ; S62: constructing a cost function based on the error function; Specifically, the cost function It is expressed by the following formula: (6); in, For time The target flow rate, is the start time of a breathing cycle, The end time of a breathing cycle.

[0031] S63: Adopting an adaptive filtering method to continuously optimize the cost function until a preset target is met.

[0032] Specifically, the gain coefficient is adjusted using the recursive least squares (RLS) method or the gradient descent method. (In actual operation, adjust , and ) so that the cost function converges to a minimum value or reaches the maximum number of iterations of the adaptive filtering method.

[0033] Specifically, RLS is an online algorithm that updates the filter weights by minimizing the sum of squared errors. In this embodiment, RLS can be used to adjust the switching point of breathing to optimize breathing. The principle of RLS is as follows: (1) Set the initial weight vector and the covariance matrix ,generally , is a small positive number, is the identity matrix; (2) Receiving input signal (such as respiratory flow or pressure difference) and reference noise (such as the set respiratory flow threshold); (3) Calculate the gain vector , For the forgetting factor; (4) Calculation error ; (5) Update the weight vector ; (6) Update the covariance matrix ; (7) Repeat the above steps (2) to (6) until the Converges to a minimum value.

[0034] Specifically, the gradient descent algorithm is an iterative optimization algorithm used to minimize the cost function. In this embodiment, the gradient descent algorithm can be used to adjust the gain coefficient , in order to minimize the error between the actual airflow signal and the expected airflow signal, the update process using the gradient descent algorithm is as follows: (1) Select the initialization gain coefficient ; (2) Calculate the cost function about The partial derivative of ; (3) Update gain coefficient , is the learning rate; (4) Repeat steps (2) to (3) until Converge to the minimum value or reach the maximum number of iterations.

[0035] Through the above process, the adaptive filtering method can adjust its parameters in real time according to the user's breathing pattern to provide the best breathing support. The present invention does not need to know the specific breathing data of the user in advance, but can make dynamic adjustments according to the real-time breathing data of the user, and can quickly and efficiently provide breathing comfort for the user.

[0036] Example 2 Reference Figure 2 This embodiment discloses an adaptive control system 20 for a ventilator, comprising: A breathing data collection unit 201 is used to collect the user's current breathing data, wherein the breathing data includes breathing flow, breathing pressure, breathing frequency and breathing pattern; A first model building unit 202, configured to build a breathing gap model of the user based on the breathing data; A second model building unit 203 is used to build a PID algorithm control model of the ventilator based on the respiratory data; A third model building unit 204 is used to build a proportional valve control model of the ventilator based on the respiratory data; The transfer function acquisition unit 205 is used to obtain the transfer function of the control system of the ventilator based on the breathing gap model, the PID algorithm control model and the proportional valve control model; A system control unit 206, used to control the control system parameters of the ventilator by an adaptive filtering method, so as to achieve consistency between the user's breathing pressure and the target pressure; Among them, the transfer function of the ventilator control system is expressed by the following formula: (1); in, is the transfer function of the breathing gap model, is the transfer function of the PID algorithm control model, is the transfer function of the proportional valve control model, , and They are expressed by the following formulas: (2); (3); (4); in, is the control system gain of the adaptive ventilator, T is the time constant, is the control input, is the output pressure, , and They are the proportional gain, integral gain and differential gain of the PID algorithm control model respectively. is the gas flow through the proportional valve, is the valve control signal, is the time constant of the proportional valve.

[0037] The system provided in the embodiment of the present application can implement the above method, and the system can be implemented by software, hardware or a combination of software and hardware. For example, the system may include integrated or separate functional modules or units to perform the corresponding steps in the above methods. In some implementations of the embodiment of the present application, the system provided in the embodiment of the present application is based on the same inventive concept as the method provided in the previous embodiment of the present application, and has the same beneficial effect. No further description is given here.

[0038] Example 3 An embodiment of the present application also provides an electronic device corresponding to the method provided in the aforementioned embodiment. The electronic device may be an electronic device used for a server, such as a server, including an independent server and a distributed server cluster, etc., to execute the above method; the electronic device may also be an electronic device used for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the above method.

[0039] Please refer to Figure 3, which shows a schematic diagram of an electronic device provided by some embodiments of the present application. Figure 3 As shown, the electronic device 30 includes: a processor 300, a memory 301, a bus 302 and a communication interface 303, and the processor 300, the communication interface 303 and the memory 301 are connected via the bus 302; the memory 301 stores a computer program that can be run on the processor 300, and the processor 300 executes the aforementioned method of the present application when running the computer program.

[0040] The memory 301 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 303 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0041] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store programs, and the processor 300 executes the programs after receiving execution instructions. The method disclosed in any implementation of the above-mentioned embodiment of the present application may be applied to the processor 300, or implemented by the processor 300.

[0042] The processor 300 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 300. The above processor 300 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 301, and the processor 300 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.

[0043] The electronic device provided in the embodiment of the present application and the method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, operated or implemented by them.

[0044] Example 4 The present application also provides a computer-readable medium corresponding to the method provided in the above embodiment. Figure 4 The computer-readable storage medium shown is a CD 40 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the aforementioned method is executed.

[0045] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0046] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0047] It should be noted that the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0048] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0049] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0050] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0051] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0052] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application, and they should all be included in the scope of the claims and specification of the present application.

Claims

1. An adaptive control method for a ventilator, characterized in that: The steps include: S1: Collect the user's current breathing data, which includes breathing flow, breathing pressure, breathing frequency and breathing pattern; S2: constructing a breathing gap model of the user based on the breathing data; S3: constructing a PID algorithm control model of a ventilator based on the respiratory data; S4: constructing a proportional valve control model of a ventilator based on the respiratory data; S5: obtaining a transfer function of a control system of a ventilator based on the breathing gap model, the PID algorithm control model and the proportional valve control model; S6: Control the control system parameters of the ventilator by an adaptive filtering method to ensure that the user's breathing pressure is consistent with the target pressure; Among them, the transfer function of the ventilator control system is expressed by the following formula: （1）； in, is the transfer function of the breathing gap model, is the transfer function of the PID algorithm control model, is the transfer function of the proportional valve control model, , and They are expressed by the following formulas: （2）； （3）； （4）； in, is the control system gain of the adaptive ventilator, T is the time constant, is the control input, is the output pressure, , and They are the proportional gain, integral gain and differential gain of the PID algorithm control model respectively. is the gas flow through the proportional valve, is the valve control signal, is the time constant of the proportional valve.

2. The method according to claim 1, characterized in that The breathing gap model is expressed by the following formula: （5）； in, For users in time The breathing compensation force, , , and The time Respiratory flow, respiratory pressure, respiratory rate and respiratory pattern under normal conditions; , , and They are respiratory flow weight, respiratory pressure weight, respiratory frequency weight and respiratory pattern weight.

3. The method according to claim 2, characterized in that The adaptive filtering method is a recursive least squares method or a gradient descent method.

4. The method according to claim 3, characterized in that Step S6 includes: S61: Obtaining an error signal based on the user's respiratory flow and the target flow; S62: constructing a cost function based on the error signal; S63: Adopting an adaptive filtering method to continuously optimize the cost function until a preset target is met.

5. The method according to claim 4, characterized in that The preset goal is that the cost function converges to a minimum value or reaches a maximum number of iterations of the adaptive filtering method.

6. The method according to claim 5, characterized in that The cost function is expressed by the following formula: （6）； in, is the cost function, For time The target flow rate, is the start time of a breathing cycle, The end time of a breathing cycle.

7. An adaptive control system for a ventilator, characterized in that: include: A breathing data collection unit, used to collect the user's current breathing data, the breathing data including breathing flow, breathing pressure, breathing frequency and breathing pattern; A first model building unit, configured to build a breathing gap model of the user based on the breathing data; A second model building unit, used to build a PID algorithm control model of the ventilator based on the respiratory data; A third model building unit, used for building a proportional valve control model of the ventilator based on the breathing data; A transfer function acquisition unit, used to obtain a transfer function of a control system of a ventilator based on the breathing gap model, the PID algorithm control model and the proportional valve control model; A system control unit, used to control the control system parameters of the ventilator by an adaptive filtering method so as to keep the user's breathing pressure consistent with the target pressure; Among them, the transfer function of the ventilator control system is expressed by the following formula: （1）； in, is the transfer function of the breathing gap model, is the transfer function of the PID algorithm control model, is the transfer function of the proportional valve control model, , and They are expressed by the following formulas: （2）； （3）； （4）； in, is the control system gain of the adaptive ventilator, T is the time constant, is the control input, is the output pressure, , and They are the proportional gain, integral gain and differential gain of the PID algorithm control model respectively. is the gas flow through the proportional valve, is the valve control signal, is the time constant of the proportional valve.

8. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 6.

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