Simulated human ventilatory capacity data calibration method and device, computing equipment and readable storage medium

Abstract

The invention provides a simulation human ventilation capacity data calibration method and device, computing equipment and a readable storage medium, and the method comprises the steps: carrying out the calibration processing of simulation human ventilation capacity for many times, and obtaining a plurality of calibration results; determining a data fitting model according to the plurality of calibration results; the data fitting model is input into a data calculation unit, so that the gas volume data output by the data calculation unit is equal to the gas introduction amount of the dummy, and calibration of the ventilation amount data of the dummy is completed; wherein the calibration result comprises the corresponding relation between the volume data of the gas introduced into the simulation person and the chest fluctuation data of the simulation person. According to the method, the accuracy of data displayed by the anthropomorphic dummy is ensured by calibrating the ventilation data of the anthropomorphic dummy, the problem that the ventilation data displayed by the anthropomorphic dummy is inaccurate due to the difference between different anthropomorphic dummy is avoided, and the improvement of the cardio-pulmonary resuscitation technology of a trainee is facilitated.

Description

Technical Field

[0001] The present application relates to the field of medical device technology, and in particular to a method, device, computing equipment and readable storage medium for calibrating simulated human ventilation data. Background Art

[0002] The simulator is a teaching aid used for CPR training in medical institutions. It can simulate the reaction of a real human body during cardiopulmonary resuscitation (CPR). This simulator is widely used in the fields of medical care, first aid, nursing, etc., with the aim of improving the first aid capabilities of medical personnel and the public, so that they can quickly and accurately perform cardiopulmonary resuscitation in emergency situations such as cardiac arrest, thereby saving the patient's life. Cardiopulmonary resuscitation includes steps such as chest compression, ventilation and defibrillation. When the patient is ventilated, the rescuer observes the rise and fall of the patient's chest to determine the ventilation status. The simulator in the prior art is provided with a lung bag at the chest position. When the simulator is ventilated, gas enters the lung bag, and the lung bag is propped up, driving the simulator's chest to rise and fall.

[0003] In the prior art, the amplitude of the chest rise and fall of the simulated person can also be measured to convert the ventilation volume for the simulated person to determine whether the ventilation volume is qualified.

[0004] However, after different simulators are assembled, due to differences between different simulators, when the same amount of gas is passed into the lung bags of different simulators, the amplitude of the chest rise and fall of the simulators is different, that is, the same set of algorithms cannot be used to convert the amplitude of chest rise and fall to ventilation volume. Therefore, the ventilation data of each simulator needs to be calibrated. Summary of the invention

[0005] The present application proposes a method, apparatus, computing device and readable storage medium for calibrating ventilation data of a simulated person, which are used to calibrate the ventilation of each simulated person.

[0006] In a first aspect, the present application provides a method for calibrating simulated human ventilation data, comprising: Perform multiple calibration processes on the simulated person's ventilation volume to obtain multiple calibration results; determining a data fitting model according to a plurality of calibration results; Inputting the data fitting model into the data calculation unit so that the gas volume data output by the data calculation unit is equal to the gas intake of the simulated person, thereby completing the calibration of the simulated person's ventilation data; The calibration results include: the corresponding relationship between the volume data of the gas introduced into the simulated person and the chest rise and fall data of the simulated person; The data calculation unit is used to calculate the gas intake of the simulated person according to the chest cavity rise and fall data.

[0007] In a feasible implementation, the calibration process includes: A fixed amount of gas is introduced into the lung bag of the simulator; Obtain chest rise and fall data of a simulated person; Determine the correspondence between chest rise and fall data and quantitative gas.

[0008] In a feasible implementation, the ventilation volume of the simulated person is calibrated multiple times to obtain multiple calibration results, including: Different volumes of gas are introduced into the simulated person in turn, and corresponding chest rise and fall data are obtained respectively, and the corresponding relationship between the chest rise and fall data and the gas volume is determined.

[0009] In a feasible implementation, during each calibration process, at least two equal volumes of gas are introduced into the lung bag in sequence, and corresponding chest cavity rise and fall data are obtained respectively; Calculate the average of all chest rise and fall data, and configure the average as the final chest rise and fall data; Determine the corresponding relationship between the final chest rise and fall data and the gas volume.

[0010] In a feasible implementation, the ventilation volume of the simulated person is calibrated at least three times, including: A first volume of gas is introduced into the lung bag, first chest cavity rise and fall data of the simulated person is read, a corresponding relationship between the first chest cavity rise and fall data and the first volume is determined, and a first calibration result is obtained; A second volume of gas is introduced into the lung bag, second chest cavity rise and fall data of the simulated person is read, a corresponding relationship between the second chest cavity rise and fall data and the second volume is determined, and a second calibration result is obtained; A third volume of gas is introduced into the lung bag, third chest cavity rise and fall data of the simulated person is read, a corresponding relationship between the third chest cavity rise and fall data and the third volume is determined, and a third calibration result is obtained; A data fitting model is determined according to the first calibration result, the second calibration result and the third calibration result.

[0011] In a feasible implementation, a grating ruler and / or a displacement sensor is used to collect chest rise and fall data.

[0012] In a second aspect, the present application provides a calibration device for simulating human ventilation, which uses the simulated human ventilation data calibration method of the first aspect, including: A ventilation component is connected to the lung bag of the simulated person and is used to introduce a certain amount of gas into the lung bag multiple times; A calibration processing module is used to obtain chest rise and fall data of a simulated person, and respectively determine the corresponding relationship between the chest rise and fall data and the quantitative gas to obtain multiple calibration results; The information processing module is electrically connected to the calibration processing module and is used to determine a data fitting model according to a plurality of calibration results. The data calculation module is electrically connected to the information processing module. The information processing module inputs the data fitting model into the data calculation module. The data calculation module completes the simulated human ventilation volume calibration according to the data fitting model.

[0013] In a third aspect, the present application provides a computing device, including: A storage component, used for storing a program for calibrating a simulated human ventilation data; A processing component, used to execute the program of the simulated human ventilation data calibration method to implement the steps of the simulated human ventilation data calibration method of the first aspect; The communication bus is used to realize the connection and communication between the storage component and the processing component.

[0014] In a fourth aspect, the present application provides a readable storage medium having an executable program stored thereon, and when the executable program is executed by a processor, the steps of the simulated human ventilation data calibration method of the first aspect are implemented.

[0015] The beneficial effects of the present invention are as follows: The present application provides a simulated human ventilation data calibration method, device, computing device and readable storage medium, including: The ventilation volume of the simulator is calibrated multiple times to obtain multiple calibration results; a data fitting model is determined based on the multiple calibration results; the data fitting model is input into the data calculation unit so that the gas volume data output by the data calculation unit is equal to the gas intake of the simulator, and the calibration of the ventilation volume data of the simulator is completed; wherein the calibration results include: the correspondence between the volume data of the gas taken into the simulator and the chest rise and fall data of the simulator; the data calculation unit is used to calculate the gas intake of the simulator based on the chest rise and fall data to calibrate the ventilation volume of each simulator. By calibrating the ventilation data of the simulator, the accuracy of the data displayed by the simulator is ensured, and the problem of inaccurate ventilation data displayed by the simulator due to differences between different simulators is avoided, which is conducive to the improvement of the cardiopulmonary resuscitation technology of the trainees. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative work: Figure 1 A flow chart of a method for calibrating simulated human ventilation data provided in an embodiment of the present application; Figure 2A schematic diagram of a calibration device for simulating human ventilation provided in one embodiment of the present application.

[0017] Description of reference numerals: 100 - calibration processing module; 200 - information processing module; 300 - data calculation module. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0019] In the description of the embodiments of the present application, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, for example, two, three, etc., unless otherwise clearly and specifically defined.

[0020] In this application, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0021] In order to ensure the accuracy of the data displayed by the simulator and avoid the occurrence of inaccurate ventilation data displayed by the simulator due to differences between different simulators, the embodiment of the present application provides a method, device, computing device and readable storage medium for calibrating the ventilation data of a simulator. The scheme provided by the embodiment of the present application will be described in detail below in conjunction with the drawings in the specification.

[0022] Figure 1 A flowchart of a method for calibrating simulated human ventilation data provided in one embodiment of the present application.

[0023] Reference Figure 1As shown, in the first aspect, the present application provides a method for calibrating simulated human ventilation data, which can be implemented by a computer. The computer can be a computing device configured with a processor, and the processor can be a general-purpose processor, a digital signal processor (DSP), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0024] Specifically, the method comprises the following steps: S100: performing multiple calibration processes on the simulated human ventilation volume to obtain multiple calibration results.

[0025] The calibration results include: the corresponding relationship between the volume data of the gas introduced into the simulator and the chest rise and fall data of the simulator. For example, when calibrating a simulator, 500 ml of gas is injected into the lung bag of the simulator using the ventilation component, and the chest surface of the simulator rises by 1 cm. At this time, the corresponding relationship between 500 ml and 1 cm is recorded.

[0026] Exemplarily, the steps of the calibration process specifically include: The first step is to use the ventilation assembly to introduce a certain amount of gas into the lung bag of the simulated person; The second step is to collect and obtain the chest rise and fall data of the simulated person using the measuring component; The third step is to determine the corresponding relationship between the chest rise and fall data and the quantitative gas.

[0027] Exemplarily, a grating ruler and / or a displacement sensor may be used to collect chest rise and fall data.

[0028] In some examples, in order to obtain a more accurate data fitting model, when the simulator is calibrated multiple times, different volumes of gas may be introduced into the simulator in sequence, and the corresponding chest rise and fall data may be obtained respectively, and the corresponding relationship between the chest rise and fall data and the gas volume may be determined. For example, the simulator is calibrated six times, and 550ml, 500ml, 450ml, 400ml, 350ml and 300ml of gas are introduced into the simulator respectively, and the corresponding chest rise and fall data may be determined respectively, and the corresponding relationship between the chest rise and fall data and the gas volume may be determined.

[0029] In some examples, when calibrating a simulated person, the same volume of gas is repeatedly introduced into the lung bag of the same simulated person to obtain multiple chest rise and fall data, and the repeated chest rise and fall data are taken as the final chest rise and fall data, and the corresponding relationship between the chest rise and fall data and the gas volume is determined.

[0030] For example, 500 ml of gas is introduced into the same simulated human lung bag for five times, and the chest rise and fall data measured by the grating ruler are 0.7 cm, 1 cm, 1 cm, 1.2 cm, and 1 cm respectively. Then 1 cm is taken as the chest rise and fall data of the simulated person after 500 ml of gas is introduced, and the corresponding relationship between 500 ml and 1 cm is determined.

[0031] In some other examples, in order to improve the accuracy of the collected data, at least two equal volumes of gas are introduced into the lung bag in sequence during each calibration process, and corresponding chest rise and fall data are obtained respectively. Then, an average value is calculated for the at least two chest rise and fall data, and the average value is configured as the final chest rise and fall data; finally, the corresponding relationship between the final chest rise and fall data and the gas volume is determined.

[0032] For example, 500 ml of gas is introduced into the same simulated human lung bag for five times, and the chest rise and fall data measured by the grating ruler are 0.7 cm, 1 cm, 1 cm, 1.2 cm, and 1 cm respectively. The average value of the five data, 0.98, is taken as the chest rise and fall data of the simulated person after 500 ml of gas is introduced, and the corresponding relationship between 0.98 ml and 1 cm is determined.

[0033] For example, when calibrating the ventilation volume of a simulated person, the ventilation volume of the simulated person is calibrated three times, including: During the first calibration process, the first volume of 500 ml of gas is introduced into the lung bag for 5 consecutive times, the first chest rise and fall data of the simulated person is read, and then the average value of the 5 chest rise and fall data (1 cm) is calculated, the corresponding relationship between the first chest rise and fall data (1 cm) and the first volume (500 ml) is determined, and the first calibration result is obtained.

[0034] During the first calibration process, a second volume of 450 ml of gas was introduced into the lung bag for five consecutive times, the second chest rise and fall data of the simulated person was read, and then the average value of the five chest rise and fall data, 0.7 cm, was calculated to obtain the correspondence between the second chest rise and fall data 0.7 cm and the second volume 450 ml, and the second calibration result was obtained.

[0035] During the third calibration process, a third volume of 400 ml of gas is introduced into the lung bag for five consecutive times, the third chest rise and fall data of the simulated person is read, and then the average value of the five chest rise and fall data, 0.5 cm, is calculated to obtain the correspondence between the third chest rise and fall data 0.5 cm and the third volume 400 ml, and the third calibration result is obtained.

[0036] A data fitting model is determined according to the first calibration result, the second calibration result and the third calibration result.

[0037] It can be understood that the more times the calibration process is performed, the more accurate the result of the data fitting model obtained. The more times the calibration process is performed can be determined according to actual needs.

[0038] In addition, it should be noted that the above test data are only used to illustrate the specific scheme of this application and are not actual test results.

[0039] S200: Determine a data fitting model according to the calibration result.

[0040] Specifically, the calibration result obtained in step S100 is fitted into an approximately linear graph to determine a data fitting model.

[0041] Exemplarily, data analysis software can be used for data analysis and fitting. Specifically, all calibration results can be marked in a coordinate system, and then data fitting can be performed according to a linear regression model or a nonlinear regression model to determine an approximately linear graph. It can be understood that linear regression is a method in statistics for establishing a linear relationship model between one or more independent variables and a dependent variable.

[0042] For example, to find the relationship between A and B based on three sets of A and B values, especially when they are positively correlated, a simple linear regression analysis can be used. Linear regression is a statistical method used to estimate the best fit line between two variables. The basic steps are explained in detail below: Step 1: Collect data: Assume that there are three sets of data points (A, B), for example (x1, y1), (x2, y2), (x3, y3).

[0043] Step 2: Draw a scatter plot: First, plot these points in a coordinate system, with the horizontal axis representing x and the vertical axis representing y.

[0044] Step 3: Calculate the slope and intercept: Calculate the slope (m) and the y-intercept (b). The formula is:

[0045] Here, m represents the slope, b represents the intercept, N represents the number of data points (3 in this case), and ∑ represents the sum.

[0046] Step 4: Construct a linear equation: Using the slope m and intercept b obtained above, the linear equation can be written as: y=mx+b Step 5: Test the model: Using this linear equation, you can predict the value of B given the value of A and check the difference between the predicted value and the actual observed value. If the difference is small, it means that your model reflects the relationship between A and B well.

[0047] Step 6: Evaluate correlation: You can also calculate the Pearson correlation coefficient (r), which measures the degree of linear association between two variables. r ranges from -1 to 1, with values ​​close to 1 indicating a strong positive correlation.

[0048] Understandably, three data points may not be enough to build a very reliable model, as a small sample size may lead to overfitting or may not reflect the overall trend well. More data points usually improve the accuracy and reliability of the model. In other examples, more complex statistical software or tools can be used to perform analysis and determine the fitting curve, such as Excel, R, Python and other data analysis tools.

[0049] S300: Inputting the data fitting model into the data calculation unit, so that the gas volume data output by the data calculation unit is equal to the gas intake of the simulated person, thereby completing the calibration of the simulated person's ventilation data.

[0050] In this step, the data fitting model obtained in step S200 is input into the data calculation unit for matching calculation, the calculation algorithm is determined, and the calculation algorithm is recorded in the data calculation unit, so that the gas volume data output by the data calculation unit is equal to the gas intake of the simulated person, so as to finally complete the calibration of the simulated person's ventilation data.

[0051] It can be understood that by calibrating the ventilation data of the simulator, the correspondence between the ventilation volume of the simulator's lung bag and the actual displayed data can be determined, thereby ensuring the accuracy of the simulator's displayed data and avoiding the occurrence of inaccurate ventilation data displayed by the simulator due to differences between different simulators, which is beneficial to improving the cardiopulmonary resuscitation skills of the trainees.

[0052] Figure 2 A schematic diagram of a calibration device for simulating human ventilation provided in one embodiment of the present application.

[0053] Reference Figure 2 As shown, in the second aspect, the present application provides a calibration device for simulated human ventilation, which uses the simulated human ventilation data calibration method of the first aspect, including a ventilation component, a calibration processing module 100, an information processing module 200 and a data calculation module 300. Among them, the ventilation component is connected to the lung bag of the simulated person, and is used to pass quantitative gas into the lung bag multiple times. The calibration processing module 100 is used to obtain the chest rise and fall data of the simulated person, and respectively determine the corresponding relationship between the chest rise and fall data and the quantitative gas, and obtain multiple calibration results.

[0054] The information processing module 200 is electrically connected to the calibration processing module 100, and is used to determine the data fitting model according to the multiple calibration results. The data calculation module 300 is electrically connected to the information processing module 200, and the information processing module 200 inputs the data fitting model to the data calculation module 300, and the data calculation module 300 completes the simulated human ventilation calibration according to the data fitting model.

[0055] In a third aspect, the present application provides a computing device, including a storage component, a processing component, and a communication bus. The storage component is used to store a program of a simulated human ventilation data calibration method; the processing component is used to execute the program of the simulated human ventilation data calibration method to implement the steps of the simulated human ventilation data calibration method of the first aspect; and the communication bus is used to realize the connection and communication between the storage component and the processing component.

[0056] The processing unit may be a general-purpose processor, a digital signal processor (DSP), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a central processing unit (CPU), a microprocessor (MPU) or any other conventional processor.

[0057] In some embodiments, the computing device may further include: an input device, an output device, and an external communication interface, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown in the figure). In this embodiment, the input device may be a network connector, an analog-to-digital converter, etc., and the output device may be a display, a speaker, etc., for outputting data.

[0058] In some embodiments, the input device may also include, for example, a keyboard, a mouse, a microphone, etc. The output device may output various information to the outside, for example, in addition to the above-mentioned display and speaker, it may also be a printer, a projector, and a communication network and its connected remote output device, etc. The external communication interface may be wired, such as a standard serial port (RS232), a general-purpose interface bus (GPIB) interface, an Ethernet interface, a universal serial bus (USB) interface, or wireless, such as wireless network communication technology (WiFi), Bluetooth, etc.

[0059] The description of the computing device embodiment above is similar to the description of the method embodiment above, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the computing device of this embodiment, please refer to the description of the method embodiment in this application for understanding.

[0060] In a fourth aspect, the present application provides a readable storage medium having an executable program stored thereon, and when the executable program is executed by a processor, the steps of the simulated human ventilation data calibration method of the first aspect are implemented.

[0061] Exemplarily, the computer-readable storage medium may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The computer-readable storage medium is a tangible device that can hold and store instructions used by an instruction execution device. The readable storage medium may include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: portable computer disk, hard disk, random access memory (RAM, Random Access Memory), read-only memory (ROM, Read Only Memory), flash memory (Flash Memory), portable compact disk read-only memory (CD-ROM, Compact Disc Read-Only Memory), digital versatile disk (DVD, Digital Versatile Disc), memory stick, floppy disk, mechanical encoding device, such as a punch card or a protruding structure in a groove on which instructions are stored, and any suitable combination of the above.

[0062] The RAM includes: static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).

[0063] The ROM includes: Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), and Electrically Erasable Programmable Read-Only Memory (EEPROM).

[0064] The computer-readable storage medium used herein is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., light pulses through a fiber optic cable), or an electrical signal transmitted through wires.

[0065] The description of the above computer-readable storage medium embodiment is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment. For technical details not disclosed in the computer-readable storage medium of this embodiment, please refer to the description of the method embodiment in this application for understanding.

[0066] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features disclosed herein. The above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.

Claims

1. A method for calibrating simulated human ventilation data, characterized in that: include: Perform multiple calibration processes on the simulated person's ventilation volume to obtain multiple calibration results; Determining a data fitting model according to the plurality of calibration results; Inputting the data fitting model into a data calculation unit so that the gas volume data output by the data calculation unit is equal to the gas intake of the simulated person, thereby completing the calibration of the ventilation data of the simulated person; Wherein, the calibration result includes: the corresponding relationship between the volume data of the gas introduced into the simulated person and the chest rise and fall data of the simulated person; The data calculation unit is used to calculate the gas intake of the simulated person according to the chest cavity fluctuation data.

2. The method for calibrating simulated human ventilation data according to claim 1, characterized in that: The calibration process comprises: Introducing a fixed amount of gas into the lung bag of the manikin; Acquiring chest rise and fall data of the simulated person; The corresponding relationship between the chest cavity fluctuation data and the quantitative gas is determined.

3. The method for calibrating simulated human ventilation data according to claim 2, characterized in that: The simulated human ventilation volume is calibrated multiple times to obtain multiple calibration results, including: Different volumes of gas are introduced into the simulated person in sequence, and the corresponding chest cavity fluctuation data are respectively obtained, and the corresponding relationship between the chest cavity fluctuation data and the gas volume is determined.

4. The method for calibrating simulated human ventilation data according to claim 2, characterized in that: During each calibration process, at least two equal volumes of gas are introduced into the lung bag in sequence, and corresponding chest cavity fluctuation data are obtained respectively; Calculating an average value of all the chest cavity rise and fall data, wherein the average value is configured as final chest cavity rise and fall data; Determine the corresponding relationship between the final chest cavity rise and fall data and the gas volume.

5. The method for calibrating simulated human ventilation data according to claim 1, characterized in that: The calibration process is performed at least three times on the simulated human ventilation, comprising: A first volume of gas is introduced into the lung bag, first chest cavity rise and fall data of the simulated person is read, a corresponding relationship between the first chest cavity rise and fall data and the first volume is determined, and a first calibration result is obtained; Introducing a second volume of gas into the lung bag, reading second chest cavity rise and fall data of the simulated person, determining a corresponding relationship between the second chest cavity rise and fall data and the second volume, and obtaining a second calibration result; A third volume of gas is introduced into the lung bag, third chest cavity rise and fall data of the simulated person is read, a corresponding relationship between the third chest cavity rise and fall data and the third volume is determined, and a third calibration result is obtained; A data fitting model is determined according to the first calibration result, the second calibration result and the third calibration result.

6. The method for calibrating simulated human ventilation data according to any one of claims 1 to 5, characterized in that: The chest rise and fall data is collected using a grating ruler and / or a displacement sensor.

7. A simulated human ventilation data calibration device, characterized in that: The method for calibrating simulated human ventilation data according to any one of claims 1 to 6 comprises: A ventilation component, connected to the lung bag of the simulated person, and used for introducing a fixed amount of gas into the lung bag multiple times; A calibration processing module (100) is used to obtain chest cavity rise and fall data of the simulated person, and respectively determine the corresponding relationship between the chest cavity rise and fall data and the quantitative gas, so as to obtain a plurality of calibration results; The information processing module (200) is electrically connected to the calibration processing module (100) and is used to determine a data fitting model according to the plurality of calibration results. A data calculation module (300) is electrically connected to the information processing module (200), the information processing module (200) inputs the data fitting model into the data calculation module (300), and the data calculation module (300) completes the simulated human ventilation calibration according to the data fitting model.

8. A computing device, characterized in that include: A storage component, used for storing a program for calibrating a simulated human ventilation data; A processing component, used to execute a program of a simulated human ventilation data calibration method to implement the steps of the simulated human ventilation data calibration method as described in any one of claims 1 to 6; A communication bus is used to realize the connection and communication between the storage component and the processing component.

9. A readable storage medium, characterized in that: The readable storage medium stores an executable program, and when the executable program is executed by a processor, the steps of the simulated human ventilation data calibration method according to any one of claims 1 to 6 are implemented.

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