A respiratory motion simulation system, simulation method, and simulation phantom

Abstract

The present invention belongs to the technical field of human respiratory motion simulation, and particularly relates to a respiratory motion simulation system, a simulation method, and a simulation phantom. A respiratory motion simulation system includes a control module and a stepper motor; the control module is used to control the steering, speed, and rotation angle of the stepper motor, and the stepper motor drives the part connected thereto to simulate human respiratory motion by changing the steering, speed, and rotation angle; the control module includes a port communication unit, a parameter adjustment unit, a motion control unit, and an expert library; the expert library takes the respiratory motion parameters of n sampling personnel with different characteristics as samples, and after classification processing, fits the relationship curve between different characteristics and PID adjustment parameters. While achieving high-precision closed-loop control, it can perform targeted fitting adjustment according to the characteristic information of the personnel to be simulated, build a more realistic and diverse simulation environment, and lower the threshold for conducting respiratory motion simulation tests.

Description

Technical Field

[0001] The present invention belongs to the technical field of human respiratory motion simulation, and particularly relates to a respiratory motion simulation system, a simulation method, and a simulation phantom. Background Art

[0002] During the process of tumor radiotherapy, CT and MRI are often used to perform tomographic scans on the lesion to show the relative positions of the lesion and each reference point in the coordinate system. The treatment planning system processes the images scanned by CT and MRI, reconstructs the three-dimensional morphology of the lesion and its surrounding tissues, calculates the number of target points, target coordinates, irradiation time, and the collimator numbers used for each target point required for radiotherapy using a gamma knife, etc. The electrical control system sequentially sends each target point to the focal point for quantitative irradiation, so as to produce focal necrosis or functional changes to achieve the purpose of treating tumors.

[0003] The CT and MRI positioning images used for treatment planning are all static images. However, during the actual treatment process, the patient is always in a breathing state, and the tumors in the lungs, liver, and mediastinum always move back and forth with the respiratory movement. That is, the actual irradiation position of the patient's tumor and the distribution of the received irradiation dose will be affected by the respiratory movement. To observe the changes in the position and volume of the tumor during respiratory movement and accurately evaluate the influence of respiratory movement on the tumor position and dose, the Canadian QUASAR programmable respiratory motion phantom or MotionSim of the American SUN NUCLEAR Corporation is commonly used to simulate human respiratory movement. However, these instruments for simulating human respiratory movement adopt an open-loop control system, with insufficient motion accuracy. After the system is debugged, it cannot be adjusted specifically according to the respiratory characteristics of the human body, and the fitting degree is not high. In addition, professional personnel are required to debug the instrument, and the usage threshold is high. Moreover, it cannot intuitively display the motion curve of human respiratory movement, nor can it perform convenient human-computer interaction, which is not conducive to the conduct of medical experiments.

[0004] The Chinese invention patent with the publication number CN114849083A discloses a closed-loop controlled human respiratory movement simulation system, which improves the motion accuracy and can display the simulated respiratory movement curve, facilitating observation. However, the simulated human respiratory movement model is relatively simple, and it can only simulate the respiratory movement of people with standard body weight, and cannot reflect the diversity of respiratory movements of people with various different characteristics. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies of the above-mentioned prior art and provide a respiratory motion simulation system that can be adjusted specifically according to the characteristic information of the person to be simulated, and build a more realistic and diverse simulation environment.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions:

[0007] A breathing motion simulation system, comprising a control module and a stepping motor; the control module is used to control the steering, speed and rotation angle of the stepping motor, and the stepping motor drives the connected part to simulate human breathing motion by changing the steering, speed and rotation angle;

[0008] The control module includes a port communication unit, a parameter adjustment unit, a motion control unit and an expert library;

[0009] The port communication unit is used to obtain the breathing motion parameters of sampling personnel with n different characteristics as samples, and a total of n samples are obtained. Different characteristics refer to gender, height, weight, and age. The breathing motion parameters include breathing frequency, the longitudinal undulation amplitude of the chest at different moments within a breathing cycle, and the longitudinal undulation speed of the chest at different moments within a breathing cycle. Each sample data is also different characteristics and the corresponding breathing motion parameters; the port communication unit is connected to the expert library, and the port communication unit transmits the obtained n sample data to the expert library;

[0010] The expert library is used to arrange these n sample data, that is, based on the breathing motion simulation of a person with a standard weight, according to the n sample data, n groups of PID adjustment parameters are obtained, an orthogonal experiment is designed, the range analysis is carried out on the experimental results, and through the interpolation calculation of the analysis results, the relationship curve between different characteristics and PID adjustment parameters is fitted. The PID adjustment parameters include the proportional adjustment parameter K p 、the integral adjustment parameter K i and the differential adjustment parameter K d ;

[0011] The port communication unit is also used to obtain the characteristic information of the person to be simulated. The characteristic information of the person to be simulated includes gender, input height a, input weight b, and input age c; the port communication unit transmits the characteristic information of the person to be simulated to the expert library; the expert library substitutes the characteristic information of the person to be simulated into each relationship curve between different characteristics and PID adjustment parameters for calculation, and the calculated PID adjustment parameters Kp(a), Ki(b) and Kd(c) are the most suitable PID adjustment parameters for the person to be simulated;

[0012] The expert library is connected to the parameter adjustment unit, and is used to transfer the most suitable PID adjustment parameters of the person to be simulated to the parameter adjustment unit. The parameter adjustment unit calculates and simulates the theoretical breathing motion curve of the person to be simulated according to the most suitable PID adjustment parameters of the person to be simulated and the breathing motion of a person with a standard weight;

[0013] The port communication unit is also connected to the motion control unit, and is used to send a start-stop signal to the motion control unit;

[0014] The motion control unit is connected to the stepper motor and is used to control the operation of the stepper motor through pulses;

[0015] The stepper motor is also connected to the parameter adjustment unit. The stepper motor transmits the actual operating parameters of the motor, such as real-time steering, speed, and rotation angle, to the parameter adjustment unit. After calculating and comparing the actual operating parameters of the motor with the theoretical breathing motion curve, the parameter adjustment unit obtains real-time correction information and sends it to the motion control unit. The motion control unit controls the stepper motor to operate under real-time correction pulses.

[0016] Preferably, the expert database is respectively set with a first sub-database and a second sub-database according to different genders of men and women. The expert database divides the received n sample data into the first sub-database and the second sub-database according to gender, and each sub-database respectively conducts sorting processing on the samples falling into it.

[0017] Preferably, based on the breathing motion simulation of standard weight people and n sample data, the first sub-database or the second sub-database reproduces the breathing motions of different characteristic personnel represented by each sample through motor simulation. During the reproduction of the breathing motions of different characteristic personnel represented by each sample, through the PID parameter tuning of the motor, the PID adjustment parameters corresponding to each sample are obtained until n groups of PID adjustment parameters corresponding to n sample data are obtained. The group of PID adjustment parameters corresponding to the nth sample data is denoted as (K pn , K in , K dn ).

[0018] Preferably, three variable factors, namely age, weight, and height, are selected. Each factor takes m level intervals respectively to generate an orthogonal test table of three factors and m levels. The PID adjustment parameters corresponding to n sample data are filled into the orthogonal test table as test results, ensuring that at least one sample is included in the level intervals of age, weight, and height. Range analysis is performed based on the orthogonal test data.

[0019] Preferably, the height in the n sample data and the corresponding proportional adjustment parameter K p are extracted as discrete sample points for interpolation calculation to fit the relationship curve between height and the proportional adjustment parameter K p :

[0020]

[0021] a is the independent variable height, that is, the input height a, unit: centimeter, accurate to 0.01 centimeter; Kp(a) is the dependent variable proportional adjustment parameter, that is, the output proportional adjustment parameter, accurate to 0.01; is the Lagrange interpolation calculation formula, a i and a j are all the heights at different sample points, 0 ≤ i ≤ n, 0 ≤ j ≤ n, i ≠ j; when the difference between the input height and the height of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the K corresponding to this sample point p value as Kp(y); when the difference between the input height and the height of a certain sample point is a non - positive negative difference, and the absolute value of this negative difference is the minimum among all negative differences, take the K corresponding to this sample point p value as Kp(y - 1);

[0022] Extract the weights in the n sample data and the corresponding integral adjustment parameter K i as discrete sample points, perform interpolation calculations, and fit the relationship curve between the weight and the integral adjustment parameter K i :

[0023]

[0024] b is the independent variable weight, that is, the input weight b, unit: kilogram, accurate to 0.01 kilogram; Ki(b) is the dependent variable integral adjustment parameter, that is, the output integral adjustment parameter, accurate to 0.1; is the Lagrange interpolation calculation formula, b i and b j are all the weights at different sample points, 0 ≤ i ≤ n, 0 ≤ j ≤ n, i ≠ j; when the difference between the input weight and the weight of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the K corresponding to this sample point i value as Ki(y); when the difference between the input weight and the weight of a certain sample point is a non - positive negative difference, and the absolute value of this negative difference is the minimum among all negative differences, take the K corresponding to this sample point i value as Ki(y - 1);

[0025] Extract the ages in the n sample data and the corresponding integral adjustment parameter K d as discrete sample points, perform interpolation calculations, and fit the relationship curve between the age and the differential adjustment parameter K d :

[0026]

[0027] c is the independent variable age, that is, the input age c, unit: year old, accurate to 1 year old; Kd(c) is the dependent variable differential adjustment parameter, that is, the output differential adjustment parameter, accurate to 0.1; is the Lagrange interpolation calculation formula, ci and c j are ages at different sample points, where 0 ≤ i ≤ n, 0 ≤ j ≤ n, and i ≠ j; when the difference between the input age and the age of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, the K value corresponding to this sample point is taken d as Kd(y); when the difference between the input age and the age of a certain sample point is a non - positive negative difference, and the absolute value of this negative difference is the minimum among all absolute values of negative differences, the K value corresponding to this sample point is taken d as Kd(y - 1).

[0028] Preferably, the motion control unit includes a control board and a driver. The control board receives the start - stop signal transmitted by the port communication unit and the real - time correction information sent by the parameter adjustment unit. The control board performs real - time correction on the pulse signal sent by the driver, and controls the driver to input the corrected correction pulse to the stepper motor.

[0029] Preferably, the control module further includes a waveform display unit, and the waveform display unit is respectively connected to the port communication unit and the parameter adjustment unit;

[0030] The waveform display unit includes four parts. The first part is the basic setting part, which is used to input and display the characteristic information and parameters required by the port communication unit. The second part is the motion parameter part, which is used to receive various parameter information in the parameter adjustment unit, display the most suitable PID adjustment parameters calculated in the expert library, the actual operation parameters of the stepper motor, and adjust the operation parameters of the stepper motor. The third part is the motion waveform part, which displays the motion waveform by receiving the parameter information in the parameter adjustment unit. The fourth part is the start - stop control part. The tester issues the start - stop command of the stepper motor through this part, and transmits the start - stop signal to the motion control unit through the port communication unit to realize the control of the start - stop of the stepper motor.

[0031] Preferably, the control module includes a host computer and a slave computer. The port communication unit, parameter adjustment unit, waveform display unit, and expert library in the control module are all implemented through the host computer. The host computer sends instructions to the slave computer through the RS232 communication interface; the slave computer is the control board, and the slave computer parses the instructions into pulse signals and sends them to the driver, and the driver amplifies the signals for the operation of the stepper motor.

[0032] The present invention also provides a simulation method for a respiratory motion simulation system, including the following steps:

[0033] S1. The port communication unit acquires the respiratory movement parameters of sampling personnel with n different characteristics, that is, a total of n samples are obtained. Different characteristics refer to gender, height, weight, and age. The respiratory movement parameters include respiratory rate, the longitudinal fluctuation amplitude of the chest at different moments within a respiratory cycle, and the longitudinal fluctuation speed of the chest at different moments within a respiratory cycle. Each sample data is also different characteristics and the corresponding respiratory movement parameters. The port communication unit transmits the n sample data to the expert database.

[0034] S2. The expert database performs sorting processing on the n sample data, that is, according to the n sample data and the respiratory movement simulation of a person with standard weight, n groups of PID adjustment parameters are obtained. Design an orthogonal experiment and perform range analysis on the experimental results. Through interpolation calculation of the analysis results, a relationship curve between different characteristics and PID adjustment parameters is fitted.

[0035] S3. The port communication unit acquires the characteristic information of the person to be simulated, including gender, input height, input weight, and input age. The motion control unit acquires a start signal and controls the stepping motor to start running.

[0036] S4. The expert database substitutes the characteristic information of the person to be simulated into each relationship curve between different characteristics and PID adjustment parameters for calculation. The calculated PID adjustment parameters Kp(a), Ki(b), and Kd(c) are the most suitable PID adjustment parameters for the person to be simulated.

[0037] S5. The parameter adjustment unit calculates and simulates the theoretical respiratory movement curve of the person to be simulated according to the most suitable PID adjustment parameters of the person to be simulated received and the respiratory movement of a person with standard weight.

[0038] S6. The parameter adjustment unit acquires the actual operation parameters of the stepping motor, including real-time rotation direction, speed, and rotation angle. The parameter adjustment unit compares the theoretical respiratory movement curve with the actual operation parameters of the motor and outputs real-time correction information according to the comparison error.

[0039] S7. The motion control unit receives the real-time correction information, and the control board corrects the pulse signal sent by the driver in real time. The driver inputs the corrected real-time correction pulse signal to the stepping motor.

[0040] S8. Repeat S6 - S7 until the error between the theoretical respiratory movement curve and the actual operation parameters of the motor is less than the error setting value, that is, it is determined that the stepping motor runs stably, and the subsequent medical experiment is officially carried out.

[0041] S9. After the characteristic information of the person to be simulated changes, repeat S3 - S8 to start the respiratory movement simulation under the new characteristic information.

[0042] Preferably, S2 further includes the following steps:

[0043] S21. Divide n samples into the first sub-library or the second sub-library according to different genders of male and female;

[0044] S22. In each sub-library, based on each sample data and the breathing movement of a person with standard weight, through motor simulation, reproduce the breathing movements of different characteristic persons represented by each sample. During the process of reproducing the breathing movements of different characteristic persons represented by each sample, through the PID parameter tuning of the motor, obtain the PID adjustment parameters corresponding to each sample, that is, the proportional adjustment parameter K p , the integral adjustment parameter K i , and the differential adjustment parameter K d , until obtaining n groups of PID adjustment parameters corresponding to n sample data, and the group of PID adjustment parameters corresponding to the nth sample data is denoted as (K pn , K in , K dn );

[0045] S23. Design an orthogonal experiment and conduct a range analysis on the experimental results;

[0046] S24. Through interpolation calculation, fit the relationship curve between different characteristics and PID adjustment parameters.

[0047] Preferably, S23 further includes the following steps:

[0048] S231. Select age, weight, and height as three variable factors, and then set the level intervals of each variable factor to generate an orthogonal experiment table;

[0049] S232. According to the corresponding intervals in the orthogonal experiment table, fill the PID adjustment parameters of the samples included in each interval into the orthogonal experiment table as experimental results, ensuring that at least one sample is included in the level interval of each age, the level interval of each weight, and the level interval of each height, totaling n samples;

[0050] S233. Conduct a range analysis on the orthogonal experiment table to obtain a correlation conclusion: the proportional adjustment parameter K p mainly depends on the height of the sample person, the integral adjustment parameter K i mainly depends on the weight of the sample person, and the differential adjustment parameter K d mainly depends on the age of the sample person.

[0051] Preferably, S24 further includes the following steps:

[0052] S241. Extract the height and the corresponding proportional adjustment parameter K p in n samples as discrete sample points, and through interpolation calculation, fit the height and the proportional adjustment parameter K pThe relationship curve between them, and the calculation formula is:

[0053]

[0054] a is the independent variable height, unit: centimeter, that is, the input height, accurate to 0.01 centimeter; Kp(a) is the dependent variable proportional adjustment parameter, that is, the output proportional adjustment parameter, accurate to 0.01; is the Lagrange interpolation calculation formula, a i and a j are all heights at different sample points, 0 ≤ i ≤ n, 0 ≤ j ≤ n, i ≠ j; when the difference between the input height and the height of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the K p value corresponding to this sample point as Kp(y); when the difference between the input height and the height of a certain sample point is a non - positive negative difference, and the absolute value of this negative difference is the minimum among all negative differences, take the K p value corresponding to this sample point as Kp(y - 1);

[0055] S242, extract the weights and the corresponding integral adjustment parameters K i from the n samples, and use them as discrete sample points. Through interpolation calculation, fit the relationship curve between the weight and the integral adjustment parameter K i The calculation formula is as follows:

[0056]

[0057] b is the independent variable weight, unit: kilogram, that is, the input weight, accurate to 0.01 kilogram; Ki(b) is the dependent variable integral adjustment parameter, that is, the output integral adjustment parameter, accurate to 0.1; is the Lagrange interpolation calculation formula, b i and b j are all weights at different sample points, 0 ≤ i ≤ n, 0 ≤ j ≤ n, i ≠ j; when the difference between the input weight and the weight of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the K i value corresponding to this sample point as Ki(y); when the difference between the input weight and the weight of a certain sample point is a non - positive negative difference, and the absolute value of this negative difference is the minimum among all negative differences, take the K i value corresponding to this sample point as Ki(y - 1);

[0058] S243, extract the ages and the corresponding differential adjustment parameters K d from the n samples, and use them as discrete sample points. Through interpolation calculation, fit the relationship curve between the age and the differential adjustment parameter K dThe relationship curve between them has the following calculation formula:

[0059]

[0060] c is the independent variable age, unit: years old, that is, the input age, accurate to 1 year old; Kd(c) is the dependent variable differential adjustment parameter, that is, the output differential adjustment parameter, accurate to 0.1; is the Lagrange interpolation calculation formula, c i and c j are both ages at different sample points, 0 ≤ i ≤ n, 0 ≤ j ≤ n, i ≠ j; when the difference between the input age and the age of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the K d value corresponding to this sample point as Kd(y); when the difference between the input age and the age of a certain sample point is a non - positive negative difference, and the absolute value of this negative difference is the minimum among all absolute values of negative differences, take the K d value corresponding to this sample point as Kd(y - 1).

[0061] The present invention also provides a simulation phantom using a respiratory motion simulation system. The simulation phantom includes the control module and the stepping motor, and a motion platform connected to the stepping motor. It also includes a simulation torso disposed outside the stepping motor and the motion platform. The stepping motor drives the motion platform by performing reciprocating motion and changing the steering, speed, and rotation angle, so that the simulation torso simulates the human respiratory motion.

[0062] The beneficial effects of the present invention are as follows:

[0063] (1) A respiratory motion simulation system of the present invention takes the respiratory motion parameters of sampling personnel as samples to establish an expert database. The expert database conducts sorting processing on the sample data falling into it, that is, based on each sample data and on the basis of simulating the respiratory motion of a person with a standard weight, through motor simulation, the respiratory motions of different characteristic personnel represented by each sample are reproduced. During the process of reproducing the respiratory motions of different characteristic personnel represented by each sample, through the PID parameter tuning of the motor, the PID adjustment parameters corresponding to each sample are obtained; an orthogonal experiment is designed, and the range analysis is performed on the experimental results; through the interpolation calculation of the analysis results, the relationship curve between different characteristics and the PID adjustment parameters is fitted; so that a respiratory motion simulation system of the present invention can, while performing high - precision closed - loop control, perform targeted fitting adjustment according to the characteristic information of the personnel to be simulated, that is, simulate the respiratory motion of non - standard - weight humans and build a more realistic and diverse simulation environment.

[0064] (2) During the sorting process in the expert database of the present invention, an orthogonal experiment is designed. Through the range analysis of the results of the orthogonal experiment, the influences of three variable factors, namely age, weight, and height, on the PID adjustment parameters are obtained. Even when the sample size is small, the range analysis of the orthogonal experiment results can comprehensively reflect the results of the full-scale experiment, ensuring the accuracy and representativeness in the data analysis process. Moreover, relying on the orthogonality of the orthogonal table, the orthogonal experiment can select representative points that are evenly distributed among a large number of samples from the full-scale experiment, minimizing the number of experiments required to obtain samples as much as possible, saving manpower, and improving the experimental efficiency. Additionally, since each sample is affected by multiple factors, using the orthogonal experiment facilitates observing and analyzing the influence of the level changes of each factor on the PID adjustment parameters.

[0065] (3) Based on the result analysis of the samples in the orthogonal experiment by the expert database of the present invention, the conclusion is obtained that the proportional adjustment parameter K p mainly depends on the height of the sample personnel, the integral adjustment parameter K i mainly depends on the weight of the sample personnel, and the derivative adjustment parameter K d mainly depends on the age of the sample personnel. Then, through interpolation calculation, a relationship curve between the personnel characteristics and the PID adjustment parameters is fitted. The sorting processing method that combines interpolation calculation with the orthogonal experiment can, in the case of only a small number of samples, ensure the accuracy of the fitted relationship curve between the personnel characteristics and the PID adjustment parameters as much as possible. And as the required sample size increases, the accuracy of the fitted relationship curve between the personnel characteristics and the PID adjustment parameters can be further improved.

[0066] (4) The expert database of the present invention substitutes the characteristic information of the simulated personnel into the relationship curves between different characteristics and the PID adjustment parameters for calculation. The calculated PID adjustment parameters Kp(a), Ki(b), and Kd(c) are the most suitable PID adjustment parameters for the to-be-simulated personnel. Adjusting and performing closed-loop control on the breathing movement of a standard-weight person according to the most suitable PID adjustment parameters for the to-be-simulated personnel, the simulated theoretical breathing movement curve of the to-be-simulated personnel is closer to the actual breathing movement of the personnel with this characteristic information.

[0067] (5) A breathing movement simulation system of the present invention also has a waveform display unit for human-computer interaction, which not only assists in completing the interactive work of inputting expert database samples, but also can display various characteristic information, parameters, and breathing waveforms during the breathing movement simulation process of this system. Moreover, through this waveform display unit, manual intervention on the parameters and motor start-stop actions of the entire system can be performed, with convenient operation and high human-computer interaction.

[0068] (6) The present invention reduces the threshold for conducting respiratory motion simulation tests, enabling such tests to be carried out without the need for professional personnel to conduct full-course debugging based on the motion of the motor. Instead, only the test personnel need to input the characteristic information of the person to be simulated during the experiment, and the expert library will automatically calculate the most suitable PID adjustment parameters for that person to be simulated. Then, the parameter adjustment unit will, according to the most suitable PID adjustment parameters for that person to be simulated and the actual operating parameters of the stepping motor, perform closed-loop control on the stepping motor through the motion control unit, causing the motor to drive the connected part to accurately simulate the required theoretical respiratory motion within a very short time, shortening the preparation time in medical experiments, improving the efficiency of medical experiments, and facilitating the conduct of medical experiments. Description of the Drawings

[0069] Figure 1 It is a schematic structural diagram of a respiratory motion simulation system;

[0070] Figure 2 is Figure 1 a schematic diagram of signal transmission between the expert library and its peripheral units in

[0071] Figure 3 the relationship curve between the fitted height and the proportional adjustment parameter K p ;

[0072] Figure 4 the relationship curve between the fitted weight and the integral adjustment parameter K i ;

[0073] Figure 5 the relationship curve between the fitted age and the integral adjustment parameter K d ;

[0074] Figure 6 It is a flowchart of the simulation method of a respiratory motion simulation system;

[0075] Figure 7 It is a schematic structural diagram of a simulation phantom using a respiratory motion simulation system.

[0076] The actual corresponding relationship between each label and the component name of the present invention is as follows:

[0077] 1. Control module; 11. Port communication unit; 12. Waveform display unit; 13. Parameter adjustment unit;

[0078] 14. Motion control unit; 141. Control board; 142. Driver; 15. Expert library; 151. First sub-library; 152. Second sub-library;

[0079] 2. Stepping motor; 3. Motion platform. Detailed Embodiment

[0080] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The solutions obtained by those of ordinary skill in the art through equivalent replacement of the technical features of the technical solutions of the present invention and conventional reasoning fall within the protection scope of the present invention.

[0081] As Figure 1 shown, a respiratory motion simulation system includes a connected control module 1 and a stepper motor 2; the control module 1 is used to control the steering, speed, and rotation angle of the stepper motor 2. The stepper motor 2 can perform a reciprocating motion by changing the steering, speed, and rotation angle, driving the connected part to simulate human respiratory motion.

[0082] By on-site observation, the respiratory motion parameters of the sampling personnel are recorded as samples, and a total of n samples are obtained. Sampling personnel with different characteristics are selected, and different characteristics refer to gender, height, weight, and age; the respiratory motion parameters include respiratory rate, the longitudinal undulation amplitude of the chest cavity at different times within a respiratory cycle, and the longitudinal undulation speed of the chest cavity at different times within a respiratory cycle; each sample data is also different characteristics and the corresponding respiratory motion parameters. The height in the sample data is accurate to 0.01 cm, the weight is accurate to 0.01 kg, and the age is accurate to 1 year old.

[0083] The control module 1 includes a port communication unit 11, a waveform display unit 12, a parameter adjustment unit 13, a motion control unit 14, and an expert library 15.

[0084] The port communication unit 11 is used to obtain n sample data, and the port communication unit 11 is connected to the expert library 15 to transmit the n sample data to the expert library 15.

[0085] As Figure 2 shown, in the expert library 15, a first sub-library 151 and a second sub-library 152 are respectively set according to different genders; the expert library 15 classifies the received n sample data into the first sub-library 151 or the second sub-library 152 according to gender. As Figure 2 shown, the information transmission of each sample data is represented by a dotted line with an arrow.

[0086] Each sub-library respectively performs sorting processing on the sample data falling into it, that is: according to the n sample data, n groups of PID adjustment parameters are obtained; an orthogonal experiment is designed, and the range analysis is performed on the experimental results; through the interpolation calculation of the analysis results, the relationship curve between different characteristics and PID adjustment parameters is fitted. Specifically as follows:

[0087] 1. According to the n sample data, n groups of PID adjustment parameters are obtained

[0088] Based on the simulation of the breathing movements of standard - weight people for each sample data, through motor simulation, the breathing movements of different characteristic people represented by each sample are reproduced. The simulation of the breathing movements of standard - weight people and the reproduction of breathing movements through motor simulation based on sample data are both prior arts, which have been recorded in the patent with the publication number CN114849083A, and will not be elaborated here.

[0089] The standard weights corresponding to different genders, ages, and heights of humans can be calculated through the standard weight calculation formula published by the World Health Organization:

[0090] Standard weight for men=(height in cm - 80)×70%, height unit: centimeter, weight unit: kilogram;

[0091] Standard weight for women=(height in cm - 70)×60%, height unit: centimeter, weight unit: kilogram;

[0092] During the process of reproducing the breathing movements of different characteristic people represented by each sample, through the PID parameter tuning of the motor, the PID adjustment parameters corresponding to each sample are obtained, that is, the proportional adjustment parameter K p 、the integral adjustment parameter K i and the derivative adjustment parameter K d , until n groups of PID adjustment parameters corresponding to n sample data are obtained; the proportional adjustment parameter K p is accurate to 0.01, the integral adjustment parameter K i is accurate to 0.1, and the derivative adjustment parameter K d is accurate to 0.1; the group of PID adjustment parameters corresponding to the nth sample data is denoted as (K pn , K in , K dn ). The method of PID parameter tuning is a prior art and will not be elaborated here.

[0093] 2. Design an orthogonal experiment and conduct a range analysis on the experimental results

[0094] In this embodiment, three variable factors, namely age, weight, and height, are selected, and each factor takes three level intervals respectively to generate an orthogonal experiment table of three factors and three levels:

[0095] The level interval of age is taken as [20, 40), [40, 60), [60, 80), unit: year;

[0096] The level interval of weight is taken as [40, 60), [60, 80), [80, 100), unit: kilogram;

[0097] The horizontal intervals of height are [130, 150), [150, 170), [170, 190), unit: centimeter;

[0098] The test results are the PID adjustment parameters for each group.

[0099] The samples transmitted to the expert database 15 are the samples collected for each factor and each level interval in advance, that is, it is ensured that at least one sample data is included in the level intervals of each age, weight, and height. In the generated orthogonal experiment table of three factors and three levels, the PID adjustment parameters corresponding to the level intervals of each factor can be filled into the orthogonal experiment table as the test results. The orthogonal experiment table of three factors and three levels in this embodiment is shown as follows:

[0100] Column where it is located 1 2 3 Factor Height Weight Age Test result Test 1 [130，150) [40，60) [20，40) <![CDATA[(K p1 , K i1 , K d1 )]]> Test 2 [150，170) [60，80) [40，60) <![CDATA[(K p2 , K i2 , K d2 )]]> Test 3 [170，190) [80，100) [60，80) <![CDATA[(K p3 , K i3 , K d3 )]]> Test 4 [130，150) [40，60) [40，60) <![CDATA[(K p4 ,K i4 ,K d4 )]]> Test 5 [150，170) [60，80) [60，80) <![CDATA[(K p5 ,K i5 ,K d5 )]]> Test 6 [170，190) [80，100) [20，40) <![CDATA[(K p6 ,K i6 ,K d6 )]]> Test 7 [130，150) [40，60) [60，80) <![CDATA[(K p7 ,K i7 ,K d7 )]]> Test 8 [150，170) [60，80) [20，40) <![CDATA[(K p8 , K i8 , K d8 )]]> Test 9 [170，190) [80，100) [40，60) <![CDATA[(K p9 ,K i9 ,K d9 )]]>

[0101] If the samples collected in advance fail to ensure that at least one sample is included in each factor level interval, it is necessary to supplement and collect the sample data corresponding to the height, weight, and age intervals according to the generated orthogonal experiment table, and obtain the PID adjustment parameters corresponding to these sample data through the above method.

[0102] The orthogonal experiment table can be generated using software such as Minitab, which belongs to the prior art and will not be elaborated here.

[0103] The variable factors, the number of variable factors, and the level intervals taken by each variable factor in this embodiment cannot be used as a limitation to the present invention; for example, the present invention can also select "the degree of physical fitness" as the fourth variable factor, and also take "the degree of physical fitness" as one of the different characteristics during sampling, and each factor can also be divided into more than three level intervals.

[0104] By using the orthogonal experiment, it is possible to select representative points that are evenly distributed in a large number of samples from the comprehensive experiment based on the orthogonality of the orthogonal table for the experiment to obtain samples. These obtained samples can more comprehensively reflect the results of the comprehensive experiment. While ensuring the accuracy and representativeness of the data, the number of experiments for obtaining samples is reduced as much as possible, saving manpower and improving the experimental efficiency; and each sample is affected by multiple factors. By using the orthogonal experiment, it is convenient to observe and analyze the influence of the level changes of each factor on the PID adjustment parameters.

[0105] In the actual orthogonal experiment table of the present invention, the level intervals taken by each factor far exceed 3 level intervals, and the total number of experiments also far exceeds 9 times, that is, the actual number of orthogonal experiments is also huge; according to the range analysis of the actual orthogonal experiment data, it is obtained that the proportional adjustment parameter K p mainly depends on the height of the sample personnel, the integral adjustment parameter K i mainly depends on the weight of the sample personnel, and the derivative adjustment parameter Kd It mainly depends on the age of the sample personnel. The range analysis of orthogonal experiments is a prior art and will not be elaborated here.

[0106] 3. Through the interpolation calculation of the analysis results, the relationship curve between different features and PID adjustment parameters is fitted.

[0107] Because the proportional adjustment parameter K p mainly depends on the height of the sample personnel, the height in the n sample data and the proportional adjustment parameter K p are extracted as discrete sample points. Taking the height (unit: centimeter) as the independent variable a, accurate to 0.01 centimeter; the proportional adjustment parameter K p is the dependent variable Kp(a), accurate to 0.01; through the interpolation calculation of the discrete sample points, the relationship curve between the height and the proportional adjustment parameter K p is fitted. The interpolation calculation formula between the height and the proportional adjustment parameter K p is as follows:

[0108]

[0109] a is the independent variable height, that is, the input height a; Kp(a) is the dependent variable proportional adjustment parameter, that is, the output proportional adjustment parameter; is the Lagrange interpolation calculation formula, a i and a j are both heights at different sample points, 0 ≤ i ≤ n, 0 ≤ j ≤ n, i ≠ j; when the difference between the input height and the height of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the K p value corresponding to this sample point as Kp(y); when the difference between the input height and the height of a certain sample point is a non - positive negative difference, and this negative difference is the minimum among the absolute values of all negative differences, take the K p value corresponding to this sample point as Kp(y - 1).

[0110] For example, if the input height is a = 160.00 centimeters, assuming there are four sample points in total, and the heights corresponding to each sample point are a 1 = 145.00 centimeters, a 2 = 157.50 centimeters, a 3 = 162.50 centimeters, a 4 = 168.00 centimeters. Then the difference between a and a 1 is a positive difference of 15.00 centimeters, the difference between a and a 2 is a positive difference of 2.50 centimeters, the difference between a and a 3 is a negative difference of 2.50 centimeters, and the difference between a and a 4The difference between them is a negative difference of 8.00 cm.

[0111] Obviously, only when a 2 = 157.50 cm, the difference between the input height and the height of this sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences. That is, when taking a 2 = 157.50 cm, the corresponding K p value is Kp(y); only when a 3 = 162.50 cm, the difference between the input height and the height of a certain sample point is a non - positive negative difference, and this negative difference is the minimum among the absolute values of all negative differences. That is, when taking a 3 = 162.50 cm, the corresponding K p value is Kp(y - 1).

[0112] As Figure 3 shown, the dotted line in the figure is the curve of the relationship between K p and height fitted by interpolation calculation. It can be seen that the discrete sample points in the figure basically all fall on the curve of the relationship between K p and height fitted by interpolation calculation; for the convenience of observation, Figure 3 the relationship between K p and height fitted by a straight line is also drawn with a solid line; from Figure 3 it can be seen that the fitting accuracy of the curve of the relationship between K p and height fitted by interpolation calculation is significantly higher than that of the straight - line fitting.

[0113] Similarly, because the integral regulation parameter K i mainly depends on the weight of the sample personnel, the weights in the n samples and the corresponding integral regulation parameters K i are extracted as discrete sample points. Taking the weight (unit: kg) as the independent variable b, accurate to 0.01 kg; the integral regulation parameter K i is the dependent variable Ki(b), accurate to 0.1; the relationship curve between the weight and the integral regulation parameter K i is fitted by interpolation calculation of the discrete sample points. The interpolation calculation formula between the weight and the integral regulation parameter K i is as follows:

[0114]

[0115] b is the independent variable weight, that is, the input weight b; Ki(b) is the dependent variable integral regulation parameter, that is, the output integral regulation parameter; is the Lagrange interpolation calculation formula, b i and b jThey are all the weights at different sample points, where 0 ≤ i ≤ n, 0 ≤ j ≤ n, and i ≠ j; when the difference between the input weight and the weight of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the K corresponding to this sample point i value as Ki(y); when the difference between the input weight and the weight of a certain sample point is a non - positive negative difference, and this negative difference is the minimum among the absolute values of all negative differences, take the K i value as Ki(y - 1).

[0116] As Figure 4 shown, the dotted line in the figure is the relationship curve of K i fitted by interpolation calculation with respect to weight. It can be seen that the discrete sample points in the figure basically all fall on the relationship curve of K i fitted by interpolation calculation with respect to weight; for the convenience of observation, Figure 4 the relationship of K i fitted by a straight line is also drawn with a solid line; it can be seen from Figure 4 that the fitting accuracy of the relationship curve of K i fitted by interpolation calculation with respect to weight is significantly higher than that of the straight - line fitting.

[0117] Similarly, because the differential adjustment parameter K d mainly depends on the age of the sample personnel, extract the ages and the corresponding differential adjustment parameters K d from the n samples as discrete sample points. Take the age (unit: years old) as the independent variable c, accurate to 1 year old; the differential adjustment parameter K d as the dependent variable Kd(c), accurate to 0.1; fit the relationship curve between age and the differential adjustment parameter K d through interpolation calculation of the discrete sample points. The interpolation calculation formula between age and the differential adjustment parameter K d is as follows:

[0118]

[0119] c is the independent variable age, that is, the input age c; Kd(c) is the dependent variable differential adjustment parameter, that is, the output differential adjustment parameter; is the Lagrange interpolation calculation formula, c i and c j are all the ages at different sample points, where 0 ≤ i ≤ n, 0 ≤ j ≤ n, and i ≠ j; when the difference between the input age and the age of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the K dThe value is Kd(y); when the difference between the input age and the age of a certain sample point is a non-positive negative difference, and this negative difference is the minimum among the absolute values of all negative differences, take the K corresponding to this sample point. d The value is Kd(y - 1).

[0120] As Figure 5 shown, the dashed line in the figure is the curve of the relationship between K and age fitted by interpolation calculation. d It can be seen that the discrete sample points in the figure basically all fall on the curve of the relationship between K and age fitted by interpolation calculation. d For easy observation, Figure 5 the solid line is also used to draw the relationship between K and age fitted by linear fitting in d ; It can be seen from Figure 5 that the fitting accuracy of the curve of the relationship between K and age fitted by interpolation calculation is significantly higher than that of linear fitting. d

[0121] As Figure 1 - Figure 2 shown, the port communication unit 11 is also used to obtain the characteristic information of the person to be simulated. The characteristic information of the person to be simulated includes gender, input height a, input weight b, and input age c; the port communication unit 11 transmits the characteristic information of the person to be simulated to the expert database 15. According to the different genders in the characteristic information of the person to be simulated, the expert database 15 sends the characteristic information of the person to be simulated into the first sub-database 151 or the second sub-database 152. In each sub-database, the input height a, input weight b, and input age c in the characteristic information of the person to be simulated are respectively substituted into the relationship curves of different characteristics and PID adjustment parameters for calculation, and the calculated PID adjustment parameters Kp(a), Ki(b), and Kd(c) are the most suitable PID adjustment parameters for this person to be simulated.

[0122] The port communication unit 11 is also connected to the motion control unit 14 for sending start / stop signals to the motion control unit 14.

[0123] The expert database 15 is connected to the parameter adjustment unit 13, and the expert database 15 transfers the most suitable PID adjustment parameters for the person to be simulated calculated according to the characteristic information of the person to be simulated to the parameter adjustment unit 13.

[0124] As Figure 1As shown, the parameter adjustment unit 13 is also respectively connected to the stepping motor 2 and the motion control unit 14; the motion control unit 14 is connected to the stepping motor 2. After receiving the start signal transmitted by the port communication unit 11, the motion control unit 14 will send a start signal to start the stepping motor 2. Specifically, the motion control unit 14 includes a control board 141 and a driver 142. After receiving the start signal transmitted by the port communication unit 11, the control board 141 controls the driver 142 to input a pulse signal to the stepping motor 2, controlling the stepping motor 2 to operate and simulate human breathing motion.

[0125] During the operation of the stepping motor 2, the actual operation parameters of the motor such as the real-time rotation direction, speed, and rotation angle are transmitted into the parameter adjustment unit 13; the parameter adjustment unit 13 calculates and simulates the theoretical breathing motion curve of the person to be simulated according to the PID adjustment parameters most suitable for the person to be simulated and the breathing motion of a person with a standard weight; after comparing the theoretical breathing motion curve with the actual operation parameters of the motor, the parameter adjustment unit 13 sends real-time correction information to the control board 141 in the motion control unit 14; the control board 141 makes real-time corrections to the pulse signal sent by the driver 142 according to the received real-time correction information, controlling the driver 142 to input the corrected correction pulse to the stepping motor 2, so that the stepping motor 2 operates under the real-time correction pulse.

[0126] The waveform display unit 12 is respectively connected to the port communication unit 11 and the parameter adjustment unit 13. The waveform display unit 12 serves as a human-machine interface and is divided into four parts. The first part is the basic setting part, which is used to input and display the characteristic information and parameters required by the port communication unit 11; the second part is the motion parameter part, which is used to receive various parameter information in the parameter adjustment unit 13, and can display the most suitable PID adjustment parameters calculated in the expert library 15 and the actual operation parameters of the stepping motor 2, and can adjust the operation parameters of the stepping motor 2; the third part is the motion waveform part, which displays the motion waveform by receiving the parameter information in the parameter adjustment unit 13; the fourth part is the start-stop control part. The tester issues the start-stop instruction of the stepping motor 2 through this part, and transmits the start-stop signal to the motion control unit 14 through the port communication unit 11 to realize the control of the start and stop of the stepping motor 2.

[0127] The control module 1 includes a motion control software, i.e., the upper computer, and a control board 141, i.e., the lower computer, in terms of hardware. The upper computer sends instructions to the lower computer through the RS232 communication interface, and the lower computer analyzes the instructions into pulse signals and sends them to the driver 142. The driver 142 amplifies the signals for the operation of the stepping motor 2.

[0128] A respiratory motion simulation system of the present invention establishes an expert database 15. After classifying the characteristics that affect respiratory motion, such as body weight, height, gender, and age, and the corresponding PID adjustment parameters for these characteristics, an orthogonal experiment is designed, the experimental results are analyzed, and through interpolation calculation of the analysis results, a relationship curve between different characteristics of a person and PID adjustment parameters is fitted. While enabling the respiratory motion simulation system of the present invention to perform high-precision closed-loop control, it can perform targeted fitting adjustment according to the characteristic information of the person to be simulated, that is, simulate the respiratory motion of humans with non-standard body weights, and build a more realistic and diverse simulation environment. Moreover, the present invention reduces the threshold for conducting respiratory motion simulation experiments, enabling the respiratory motion simulation experiment to be automatically calculated by the expert database 15 to obtain the most suitable PID adjustment parameters without the need for professional personnel to perform full debugging according to the motion conditions of the motor. Only the experimental personnel need to input the characteristic information of the person to be simulated during the experiment, and the parameter adjustment unit 13 will perform closed-loop control on the stepping motor 2 according to the most suitable PID adjustment parameters and the actual operating parameters of the stepping motor 2 to achieve adaptive simulation of respiratory motion, facilitating the conduct of medical experiments. The sample processing method in the expert database 15 can ensure the accuracy of the relationship curve between the fitted person characteristics and PID adjustment parameters as much as possible with only a small number of samples. As the required number of samples increases, the accuracy of the relationship curve between the fitted person characteristics and PID adjustment parameters can be further improved.

[0129] The simulation method of a respiratory motion simulation system of the present invention is as Figure 6 shown and includes the following steps:

[0130] S1. The port communication unit 11 acquires the respiratory motion parameters of n sampling persons with different characteristics, that is, a total of n samples are obtained. Different characteristics refer to gender, height, body weight, and age. The respiratory motion parameters include respiratory frequency, the longitudinal fluctuation amplitude of the chest cavity at different moments within a respiratory cycle, and the longitudinal fluctuation speed of the chest cavity at different moments within a respiratory cycle. Each sample data is also different characteristics and the corresponding respiratory motion parameters for these characteristics. The height is accurate to 0.01 cm, the body weight is accurate to 0.01 kg, the age is accurate to 1 year old, the respiratory frequency is accurate to 1 time, the longitudinal fluctuation amplitude is accurate to 0.1 cm, and the longitudinal fluctuation speed is accurate to 0.1 cm / s. The port communication unit 11 transmits the n sample data to the expert database 15.

[0131] S2. The expert database 15 classifies the n sample data, that is, according to the n sample data, n sets of PID adjustment parameters are obtained. An orthogonal experiment is designed, and range analysis is performed on the experimental results. Through interpolation calculation of the analysis results, a relationship curve between different characteristics and PID adjustment parameters is fitted. S2 also includes the following steps:

[0132] S21, divide n samples into the first sub-library 151 or the second sub-library 152 according to different genders;

[0133] S22, in each sub-library, based on each sample data and the breathing movement of a standard-weight person, through motor simulation, reproduce the breathing movements of different characteristic persons represented by each sample. During the process of reproducing the breathing movements of different characteristic persons represented by each sample, through the PID parameter tuning of the motor, obtain the PID adjustment parameters corresponding to each sample, that is, the proportional adjustment parameter K p , integral adjustment parameter K i and derivative adjustment parameter K d , until obtaining n groups of PID adjustment parameters corresponding to n sample data. The group of PID adjustment parameters corresponding to the nth sample data is denoted as (K pn , K in , K dn );

[0134] S23, design an orthogonal experiment and conduct a range analysis on the experimental results; S23 also includes the following steps:

[0135] S231, select age, weight, and height as three variable factors, then set the level intervals of each variable factor to generate an orthogonal experiment table;

[0136] S232, according to the corresponding intervals in the orthogonal experiment table, fill the PID adjustment parameters of the samples included in each interval into the orthogonal experiment table as experimental results, ensuring that at least one sample is included in the level interval of each age, the level interval of each weight, and the level interval of each height, for a total of n samples;

[0137] S233, conduct a range analysis on the orthogonal experiment table to obtain a correlation conclusion: the proportional adjustment parameter K p mainly depends on the height of the sample person, the integral adjustment parameter K i mainly depends on the weight of the sample person, and the derivative adjustment parameter K d mainly depends on the age of the sample person;

[0138] S24, through interpolation calculation, fit the relationship curve between different characteristics and PID adjustment parameters; S24 also includes the following steps:

[0139] S241, extract the height and the corresponding proportional adjustment parameter K p from the n samples as discrete sample points, and through interpolation calculation, fit the relationship curve between height and proportional adjustment parameter K p , and the calculation formula is:

[0140]

[0141] Let \(a\) be the independent variable height (unit: centimeter), that is, the input height, accurate to 0.01 centimeter; \(K_p(a)\) is the dependent variable proportional adjustment parameter, that is, the output proportional adjustment parameter, accurate to 0.01; is the Lagrange interpolation calculation formula, \(a\) i and \(a\) j are all heights at different sample points, \(0\leq i\leq n\), \(0\leq j\leq n\), \(i\neq j\); when the difference between the input height and the height of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the \(K\) corresponding to this sample point p value as \(K_p(y)\); when the difference between the input height and the height of a certain sample point is a non - positive negative difference, and this negative difference is the minimum among the absolute values of all negative differences, take the \(K\) corresponding to this sample point p value as \(K_p(y - 1)\);

[0142] S242, extract the weights and the corresponding integral adjustment parameters \(K\) in \(n\) samples i as discrete sample points, and fit the relationship curve between the weight and the integral adjustment parameter \(K\) through interpolation calculation. The calculation formula is as follows: i Let \(b\) be the independent variable weight (unit: kilogram), that is, the input weight, accurate to 0.01 kilogram; \(K_i(b)\) is the dependent variable integral adjustment parameter, that is, the output integral adjustment parameter, accurate to 0.1;

[0143]

[0144] Let \(b\) be the independent variable weight (unit: kilogram), that is, the input weight, accurate to 0.01 kilogram; \(K_i(b)\) is the dependent variable integral adjustment parameter, that is, the output integral adjustment parameter, accurate to 0.1; is the Lagrange interpolation calculation formula, \(b\) i and \(b\) j are all weights at different sample points, \(0\leq i\leq n\), \(0\leq j\leq n\), \(i\neq j\); when the difference between the input weight and the weight of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the \(K\) corresponding to this sample point i value as \(K_i(y)\); when the difference between the input weight and the weight of a certain sample point is a non - positive negative difference, and this negative difference is the minimum among the absolute values of all negative differences, take the \(K\) corresponding to this sample point i value as \(K_i(y - 1)\);

[0145] S243, extract the ages and the corresponding differential adjustment parameters \(K\) in \(n\) samples d as discrete sample points, and fit the relationship curve between the age and the differential adjustment parameter \(K\) through interpolation calculation. The calculation formula is as follows: d Let \(c\) be the independent variable age (unit: year), that is, the input age, accurate to 0.01 year; \(K_d(c)\) is the dependent variable differential adjustment parameter, that is, the output differential adjustment parameter, accurate to 0.01;

[0146]

[0147] Let \(c\) be the independent variable of age (unit: years old), that is, the input age, accurate to 1 year old; \(K_d(c)\) is the dependent variable of the differential adjustment parameter, that is, the output differential adjustment parameter, accurate to 0.1. is the Lagrange interpolation calculation formula, \(c\) i and \(c\) j are all ages at different sample points, \(0\leq i\leq n\), \(0\leq j\leq n\), \(i\neq j\); when the difference between the input age and the age of a certain sample point is a non - negative positive difference, and this positive difference is the minimum of all positive differences, take the \(K\) corresponding to this sample point d value as \(K_d(y)\); when the difference between the input age and the age of a certain sample point is a non - positive negative difference, and this negative difference is the minimum of the absolute values of all negative differences, take the \(K\) corresponding to this sample point d value as \(K_d(y - 1)\);

[0148] S3. The port communication unit 11 obtains the characteristic information of the person to be simulated, including gender, input height, input weight, and input age; the motion control unit 14 obtains the start signal and controls the stepping motor 2 to start running.

[0149] S4. The expert library 15 substitutes the characteristic information of the person to be simulated into the relationship curves between different characteristics and PID adjustment parameters for calculation. The calculated PID adjustment parameters \(K_p(a)\), \(K_i(b)\), and \(K_d(c)\) are the most suitable PID adjustment parameters for the person to be simulated.

[0150] S5. The parameter adjustment unit 13 calculates and simulates the theoretical respiratory motion curve of the person to be simulated according to the most suitable PID adjustment parameters of the person to be simulated received and the respiratory motion of a standard - weight person.

[0151] S6. The parameter adjustment unit 13 obtains the actual operation parameters of the stepping motor 2, including real - time steering, speed, and rotation angle; after comparing the theoretical respiratory motion curve with the actual operation parameters of the motor, the parameter adjustment unit 13 outputs real - time correction information according to the comparison error.

[0152] S7. The motion control unit 14 receives the real - time correction information, and the control board 141 corrects the pulse signal sent by the driver 142 in real - time. The driver 142 inputs the corrected real - time correction pulse signal to the stepping motor 2.

[0153] S8. Repeat S6 - S7 until the error between the theoretical respiratory motion curve and the actual operation parameters of the motor is less than the error set value, that is, it is determined that the stepping motor 2 runs stably, and the subsequent medical experiment is officially carried out; the error set value includes an angular error of ±0.05° and a rotational speed error of ±5 revolutions per minute.

[0154] After the characteristic information of the person to be simulated changes, repeat S3 to S8 to start the breathing motion simulation under the new characteristic information.

[0155] A simulation phantom using a breathing motion simulation system according to the present invention, in addition to the connected control module 1 and stepping motor 2, further includes a motion platform 3 and a simulation torso disposed outside the motion platform 3 and the stepping motor 2. The stepping motor 2 drives the connected motion platform 3 through reciprocating motion of changing the steering, speed, and rotation angle to make the simulation torso simulate the human breathing motion. The parts of the control module 1 and the stepping motor 2 will not be described in detail. The motion platform 3 further includes a worm, a slide table, a zero adjuster, a limiter, etc. The operation of the stepping motor 2 drives the lead screw to rotate, and the slide table moves along with the lead screw. The limiter and the zero adjuster are used to limit the movement of the slide table.

[0156] The technologies, shapes, and structures not described in detail in the present invention are all well-known technologies.

Claims

1. A respiratory motion simulation system, characterized in that: it includes a control module (1) and a stepper motor (2); the control module (1) is used to control the steering, speed, and rotation angle of the stepper motor (2), and the stepper motor (2) drives the connected part to simulate human respiratory motion by changing the steering, speed, and rotation angle; the control module (1) includes a port communication unit (11), a parameter adjustment unit (13), a motion control unit (14), and an expert database (15); the port communication unit (11) is used to obtain the respiratory motion parameters of sampling personnel with n different characteristics as samples, a total of n samples are obtained, different characteristics refer to gender, height, weight, age, and the respiratory motion parameters include respiratory frequency, the longitudinal fluctuation amplitude of the chest cavity at different times within a respiratory cycle, and the longitudinal fluctuation speed of the chest cavity at different times within a respiratory cycle. Each sample data is also different characteristics and the corresponding respiratory motion parameters; the port communication unit (11) is connected to the expert database (15), and the port communication unit (11) transmits the obtained n sample data to the expert database (15); The expert database (15) is used to classify and process these n sample data, that is, based on the respiratory motion simulation of a standard-weight person, n sets of PID adjustment parameters are obtained according to the n sample data, an orthogonal experiment is designed, the range analysis is carried out on the experimental results, and through the interpolation calculation of the analysis results, the relationship curve between different features and the PID adjustment parameters is fitted. The PID adjustment parameters include the proportional adjustment parameter K p , the integral adjustment parameter K i and the differential adjustment parameter K d ; the port communication unit (11) is also used to obtain the characteristic information of the person to be simulated. The characteristic information of the person to be simulated includes gender, input height a, input weight b, and input age c; the port communication unit (11) transmits the characteristic information of the person to be simulated to the expert database (15); the expert database (15) substitutes the characteristic information of the person to be simulated into each relationship curve between different characteristics and PID adjustment parameters for calculation, and the calculated PID adjustment parameters Kp(a), Ki(b), and Kd(c) are the most suitable PID adjustment parameters for the person to be simulated; the expert database (15) is connected to the parameter adjustment unit (13), and is used to transfer the most suitable PID adjustment parameters of the person to be simulated to the parameter adjustment unit (13). The parameter adjustment unit (13) calculates and simulates the theoretical respiratory motion curve of the person to be simulated according to the most suitable PID adjustment parameters of the person to be simulated and the respiratory motion of a person with standard weight; the port communication unit (11) is also connected to the motion control unit (14), and is used to send a start-stop signal to the motion control unit (14); the motion control unit (14) is connected to the stepper motor (2), and is used to control the operation of the stepper motor (2) through pulses; the stepper motor (2) is also connected to the parameter adjustment unit (13), and the stepper motor (2) transmits the actual operation parameters of the motor, such as real-time steering, speed, and rotation angle, to the parameter adjustment unit (13); after calculating and comparing the actual operation parameters of the motor and the theoretical respiratory motion curve, the parameter adjustment unit (13) obtains real-time correction information and sends it to the motion control unit (14), and the motion control unit (14) controls the stepper motor (2) to operate under real-time correction pulses.

2. The respiratory motion simulation system according to claim 1, characterized in that: The expert database (15) is respectively provided with a first sub-database (151) and a second sub-database (152) according to different genders. The expert database (15) classifies the received n sample data into the first sub-database (151) and the second sub-database (152) according to gender, and each sub-database respectively performs sorting processing on the samples falling into it.

3. The respiratory motion simulation system according to claim 2, characterized in that: The first sub-library (151) or the second sub-library (152) reproduces the breathing movements of different characteristic persons represented by each sample through motor simulation based on the breathing movement simulation of a standard-weight person and n sample data; during the reproduction of the breathing movements of different characteristic persons represented by each sample, the PID parameters of the motor are tuned to obtain the PID adjustment parameters corresponding to each sample until n sets of PID adjustment parameters corresponding to n sample data are obtained, and the set of PID adjustment parameters corresponding to the nth sample data is denoted as (K pn , K in , K dn ).

4. The respiratory motion simulation system according to claim 3, characterized in that: Three variable factors of age, weight, and height are selected, and each factor takes m level intervals respectively to generate an orthogonal test table of three factors and m levels. The PID adjustment parameters corresponding to the n sample data are filled into the orthogonal test table as test results, ensuring that at least one sample is included in the level interval of each age, the level interval of weight, and the level interval of height, and range analysis is performed according to the orthogonal test data.

5. The respiratory motion simulation system according to claim 4, characterized in that: Extract the height and the corresponding ratio adjustment parameter K from the n sample data as discrete sample points for interpolation calculation, and fit the relationship curve between the height and the ratio adjustment parameter K: p Extract them and use them as discrete sample points for interpolation calculation to fit the relationship curve between the height and the ratio adjustment parameter K p between: Let \(a\) be the independent variable height, that is, the input height \(a\), unit: centimeter, accurate to 0.01 centimeter; \(K_p(a)\) is the dependent variable proportional adjustment parameter, that is, the output proportional adjustment parameter, accurate to 0.01; is the Lagrange interpolation calculation formula, \(a\) i and \(a\) j are all heights at different sample points, \(0\leq i\leq n\), \(0\leq j\leq n\), \(i\neq j\); When the difference between the input height and the height of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the K corresponding to this sample point p value as Kp(y); when the difference between the input height and the height of a certain sample point is a non - positive negative difference, and this negative difference is the minimum among the absolute values of all negative differences, take the K p value as Kp(y - 1); Extract the body weight and the corresponding integral adjustment parameter K from the n sample data as discrete sample points, perform interpolation calculations, and fit the relationship curve between the body weight and the integral adjustment parameter K: i Extract the body weight and the corresponding integral adjustment parameter K from the n sample data as discrete sample points, perform interpolation calculations, and fit the relationship curve between the body weight and the integral adjustment parameter K i between: Let \(b\) be the independent variable of body weight, that is, the input body weight \(b\), unit: kilogram, accurate to \(0.01\) kilogram; \(K_i(b)\) is the dependent variable integral regulation parameter, that is, the output integral regulation parameter, accurate to \(0.1\). is the Lagrange interpolation calculation formula, \(b\) i and \(b\) j are the body weights at different sample points, \(0\leq i\leq n\), \(0\leq j\leq n\), \(i\neq j\). When the difference between the input weight and the weight of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the K corresponding to this sample point i value as Ki(y); when the difference between the input weight and the weight of a certain sample point is a non - positive negative difference, and this negative difference is the minimum among the absolute values of all negative differences, take the K i value as Ki(y - 1); Extract the age and the corresponding integral adjustment parameter K in the n sample data, and use them as discrete sample points for interpolation calculation to fit the relationship curve between age and differential adjustment parameter K: d Extract them, and use them as discrete sample points for interpolation calculation to fit the relationship curve between age and differential adjustment parameter K d : Let \(c\) be the independent variable age, that is, the input age \(c\), unit: years old, accurate to 1 year old; \(K_d(c)\) is the dependent variable differential adjustment parameter, that is, the output differential adjustment parameter, accurate to 0.1; is the Lagrange interpolation calculation formula, \(c\) i and \(c\) j are ages at different sample points, \(0\leq i\leq n\), \(0\leq j\leq n\), \(i\neq j\); When the difference between the input age and the age of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the K corresponding to this sample point d The value is Kd(y); when the difference between the input age and the age of a certain sample point is a non - positive negative difference, and this negative difference is the minimum among the absolute values of all negative differences, take the K d The value is Kd(y - 1).

6. The respiratory motion simulation system according to claim 1, characterized in that: The motion control unit (14) includes a control board (141) and a driver (142). The control board (141) receives the start-stop signal transmitted by the port communication unit (11) and the real-time correction information sent by the parameter adjustment unit (13). The control board (141) performs real-time correction on the pulse signal sent by the driver (142) and controls the driver (142) to input the corrected correction pulse to the stepping motor (2).

7. The respiratory motion simulation system according to any one of claims 1 to 6, characterized in that: The control module (1) further includes a waveform display unit (12), and the waveform display unit (12) is respectively connected to the port communication unit (11) and the parameter adjustment unit (13); The waveform display unit (12) includes four parts. The first part is the basic setting part, which is used to input and display the characteristic information and parameters required by the port communication unit (11); the second part is the motion parameter part, which is used to receive various parameter information in the parameter adjustment unit (13), display the most suitable PID adjustment parameters calculated in the expert database (15), the actual operation parameters of the stepping motor (2), and adjust the operation parameters of the stepping motor (2); the third part is the motion waveform part, which displays the motion waveform by receiving the parameter information in the parameter adjustment unit (13); the fourth part is the start-stop control part. The tester issues the start-stop instruction of the stepping motor (2) through this part, and transmits the start-stop signal to the motion control unit (14) through the port communication unit (11) to realize the start-stop control of the stepping motor (2).

8. The respiratory motion simulation system according to claim 7, characterized in that: The control module (1) includes a host computer and a slave computer. The port communication unit (11), parameter adjustment unit (13), waveform display unit (12) and expert library (15) in the control module (1) are all implemented by the host computer. The host computer sends instructions to the slave computer through the RS232 communication interface. The slave computer is a control board (141), which parses the instructions into pulse signals and sends them to the driver (142). The driver (142) amplifies the signals and uses them for the operation of the stepping motor (2).

9. A simulation method for a respiratory motion simulation system according to any one of claims 1 to 6, characterized in that, it includes the following steps: S1. The port communication unit (11) obtains the respiratory motion parameters of n sampling persons with different characteristics, that is, a total of n samples are obtained. Different characteristics refer to gender, height, weight, and age. The respiratory motion parameters include respiratory rate, the longitudinal fluctuation amplitude of the chest at different moments within a respiratory cycle, and the longitudinal fluctuation speed of the chest at different moments within a respiratory cycle. Each sample data is also different characteristics and the corresponding respiratory motion parameters. The port communication unit (11) transmits the n sample data to the expert library (15). S2. The expert library (15) performs classification processing on the n sample data, that is, according to the respiratory motion simulation of the n sample data and a person with a standard weight, n sets of PID adjustment parameters are obtained. An orthogonal experiment is designed, and the range analysis is performed on the experimental results. Through the interpolation calculation of the analysis results, a relationship curve between different characteristics and PID adjustment parameters is fitted. S3. The port communication unit (11) obtains the characteristic information of the person to be simulated, including gender, input height, input weight, and input age. The motion control unit (14) obtains a start signal and controls the stepping motor (2) to start running. S4. The expert library (15) substitutes the characteristic information of the person to be simulated into each relationship curve between different characteristics and PID adjustment parameters for calculation. The calculated PID adjustment parameters Kp(a), Ki(b), and Kd(c) are the most suitable PID adjustment parameters for the person to be simulated. S5. The parameter adjustment unit (13) calculates and simulates the theoretical respiratory motion curve of the person to be simulated according to the most suitable PID adjustment parameters of the person to be simulated and the respiratory motion of a person with a standard weight. S6. The parameter adjustment unit (13) obtains the actual operation parameters of the stepping motor (2), including real-time rotation direction, speed, and rotation angle. The parameter adjustment unit (13) compares the theoretical respiratory motion curve with the actual operation parameters of the motor, and outputs real-time correction information according to the comparison error. S7. The motion control unit (14) receives the real-time correction information, and the control board (141) performs real-time correction on the pulse signal sent by the driver (142). The driver (142) inputs the corrected real-time correction pulse signal to the stepping motor (2). S8. Repeat S6 to S7 until the error between the theoretical respiratory motion curve and the actual operation parameters of the motor is less than the error setting value, that is, it is determined that the stepping motor (2) runs stably, and the subsequent medical experiments are officially carried out. S9. After the characteristic information of the person to be simulated changes, repeat S3 to S8 to start the breathing motion simulation under the new characteristic information.

10. The simulation method according to claim 9, wherein, S2 further includes the following steps: S21. According to different genders of male and female, divide n samples into the first sub-library (151) or the second sub-library (152); S22. In each sub-library, based on each sample data and the breathing movement of a standard-weight person, through motor simulation, reproduce the breathing movements of different characteristic persons represented by each sample. During the process of reproducing the breathing movements of different characteristic persons represented by each sample, through the PID parameter tuning of the motor, obtain the PID adjustment parameters corresponding to each sample, that is, the proportional adjustment parameter K p , the integral adjustment parameter K i and the differential adjustment parameter K d , until obtaining n groups of PID adjustment parameters corresponding to n sample data. The group of PID adjustment parameters corresponding to the nth sample data is denoted as (K pn , K in , K dn ); S23. Design an orthogonal experiment and perform range analysis on the experimental results; S24. Through interpolation calculation, fit the relationship curve between different characteristics and PID adjustment parameters.

11. The simulation method according to claim 10, wherein, S23 further includes the following steps: S231. Select age, weight, and height as three variable factors, then set the level intervals of each variable factor to generate an orthogonal experiment table; S232. According to the corresponding intervals in the orthogonal experiment table, fill the PID adjustment parameters of the samples included in each interval into the orthogonal experiment table, ensuring that at least one sample is included in the level interval of each age, the level interval of each weight, and the level interval of each height, totaling n samples; S233, perform a range analysis on the orthogonal experiment table to obtain the correlation conclusion: the proportional adjustment parameter K p mainly depends on the height of the sample personnel, and the integral adjustment parameter K i mainly depends on the weight of the sample personnel, and the differential adjustment parameter K d mainly depends on the age of the sample personnel.

12. The simulation method according to claim 11, wherein, S24 further includes the following steps: S241, extract the height and the corresponding ratio adjustment parameter K in the n samples as discrete sample points, and fit the relationship curve between the height and the ratio adjustment parameter K through interpolation calculation. The calculation formula is as follows: p Extract them as discrete sample points, and fit the relationship curve between height and ratio adjustment parameter K through interpolation calculation p The relationship curve between them is as follows: Let \(a\) be the independent variable height, with the unit of centimeter, that is, the input height, accurate to 0.01 centimeter; \(K_p(a)\) is the dependent variable proportional adjustment parameter, that is, the output proportional adjustment parameter, accurate to 0.01; is the Lagrange interpolation calculation formula, \(a\) i and \(a\) j are all heights at different sample points, \(0\leq i\leq n\), \(0\leq j\leq n\), \(i\neq j\); when the difference between the input height and the height of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the \(K\) p value corresponding to this sample point as \(K_p(y)\); when the difference between the input height and the height of a certain sample point is a non - positive negative difference, and the absolute value of this negative difference is the minimum among all absolute values of negative differences, take the \(K\) p value corresponding to this sample point as \(K_p(y - 1)\); S242, extract the body weight and the corresponding integral adjustment parameter K in the n samples as discrete sample points, and fit the relationship curve between the body weight and the integral adjustment parameter K through interpolation calculation. The calculation formula is as follows: i Extract them as discrete sample points, and fit the relationship curve between body weight and integral adjustment parameter K through interpolation calculation. i The relationship curve between them is as follows: Let \(b\) be the independent variable body weight, unit: kilogram, that is, the input body weight, accurate to 0.01 kilogram; \(K_i(b)\) is the dependent variable integral regulation parameter, that is, the output integral regulation parameter, accurate to 0.1; is the Lagrange interpolation calculation formula, \(b\) i and \(b\) j are the body weights at different sample points, \(0\leq i\leq n\), \(0\leq j\leq n\), \(i\neq j\); when the difference between the input body weight and the body weight at a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the \(K\) i value corresponding to this sample point as \(K_i(y)\); when the difference between the input body weight and the body weight at a certain sample point is a non - positive negative difference, and the absolute value of this negative difference is the minimum among all absolute values of negative differences, take the \(K\) i value corresponding to this sample point as \(K_i(y - 1)\); S243, extract the age and the corresponding differential adjustment parameter K in the n samples as discrete sample points, and fit the relationship curve between the age and the differential adjustment parameter K through interpolation calculation. The calculation formula is as follows: d Extract them as discrete sample points, and fit the relationship curve between age and differential adjustment parameter K through interpolation calculation d The relationship curve between them is as follows: Let \(c\) be the independent variable age, in years, that is, the input age, accurate to 1 year; \(K_d(c)\) is the dependent variable differential adjustment parameter, that is, the output differential adjustment parameter, accurate to 0.

1. is the Lagrange interpolation calculation formula, \(c\) i and \(c\) j are ages at different sample points, \(0\leq i\leq n\), \(0\leq j\leq n\), \(i\neq j\); when the difference between the input age and the age of a certain sample point is a non - negative positive difference, and this positive difference is the minimum among all positive differences, take the \(K\) d value corresponding to this sample point as \(K_d(y)\); when the difference between the input age and the age of a certain sample point is a non - positive negative difference, and the absolute value of this negative difference is the minimum among all absolute values of negative differences, take the \(K\) d value corresponding to this sample point as \(K_d(y - 1)\).

13. A simulation phantom using the breathing motion simulation system according to any one of claims 1 to 6, wherein: The simulation phantom includes the control module (1) and the stepping motor (2), and a motion platform (3) connected to the stepping motor (2). It also includes a simulation torso disposed outside the stepping motor (2) and the motion platform (3). The stepping motor (2) drives the motion platform (3) by performing reciprocating motion and changing the steering, speed, and rotation angle, so that the simulation torso simulates the human breathing motion.

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