Flight anti-overload HP action evaluation method, equipment and program product

Abstract

The invention relates to the field of intelligent medical treatment, in particular to a flight anti-overload HP action evaluation method, equipment and a program product. Comprising the following steps: acquiring detection values of EIT data, plantar pressure data and myoelectricity data of a to-be-detected person; the EIT data comprises one or more of the following integral ventilation parameters: an inspiration quantity value, an expiration uniformity value and an expiration flow rate value; the myoelectricity data comprises one or more of a gastrocnemius muscle ratio, a rectus femoris ratio and a rectus abdominis ratio; hP action evaluation is carried out based on the detection values of the EIT data, the plantar pressure data and the myoelectricity data, the standard or substandard evaluation result is obtained, and the method has good application value.

Description

Technical Field

[0001] The present application relates to the field of intelligent medical treatment, and specifically to a flight anti-overload HP action evaluation method, device, program product and computer-readable storage medium. Background Art

[0002] In new training modes such as continuous free air combat and system confrontation, pilots are often in a high-G and high-growth environment, with long load action time and repeated high load action. The incidence of G-induced loss of consciousness (G-LOC) in the air increases significantly, posing a serious threat to flight safety. Correct and effective anti-G straining maneuvers (AGSM) are an important measure to prevent G-LOC. Commonly used AGSMs include LI, M-1 and HP actions. However, L-1 and M-1 actions require strong skills and are difficult to control, making it difficult for pilots to master them. Therefore, many pilots have inaccurate anti-G actions and are accustomed to their inherent wrong actions. In addition, the combination of extended coverage G-suit (ECGS) and positive pressure breathing for +G (PBG) in advanced and efficient anti-G systems has low efficiency and consumes a lot of physical energy, which can easily lead to pilot fatigue. Therefore, some researchers have proposed HP actions, which have been widely used among air force fighter pilots and have significant anti-G effects. AGSM uses changes in eye-level systolic blood pressure as the gold standard for evaluation. However, the current non-invasive, real-time collection of systolic blood pressure mainly relies on infrared plethysmography. The blood vessels in the head are all located deep in the brain, and it is difficult for the equipment to penetrate deep into the brain to accurately find the blood vessels and collect eye-level systolic blood pressure. Therefore, a technology is needed to replace eye-level systolic blood pressure to evaluate the effectiveness of AGSM. Summary of the invention

[0003] In view of the above problems, the present invention provides a method for evaluating flight anti-overload HP action, which specifically includes:

[0004] Obtain the test values ​​of the EIT data, plantar pressure data, and electromyography data of the test subject;

[0005] The EIT data includes one or more of the following overall ventilation parameters: inspiratory volume value, expiratory uniformity value, expiratory flow rate value; the electromyographic data includes one or more of the following: gastrocnemius ratio, rectus femoris ratio, rectus abdominis ratio;

[0006] HP action evaluation is performed based on the detection values ​​of the EIT data, plantar pressure data, and electromyography data to obtain an evaluation result of whether it meets the standard or not.

[0007] The plantar pressure includes one or more of the following regional pressures: thumb area, first metatarsal area, lateral arch area, and posterior heel area;

[0008] Optionally, the plantar pressure area also includes one or more of the following: the second and third metatarsal areas, the fourth and fifth metatarsal areas, the medial heel area, and the lateral heel area;

[0009] Optionally, the gastrocnemius ratio is a ratio of an electromyographic signal value of the gastrocnemius muscle when the tester performs HP action to an electromyographic signal value of the gastrocnemius muscle when the tester breathes quietly;

[0010] Optionally, the rectus femoris ratio is a ratio of an electromyographic signal value of the rectus femoris when the tester performs HP action to an electromyographic signal value of the rectus femoris when the tester breathes quietly;

[0011] Optionally, the rectus abdominis ratio is a ratio of an electromyographic signal value of the rectus abdominis when the tester performs HP action to an electromyographic signal value of the rectus abdominis when the tester breathes quietly;

[0012] Optionally, the evaluation is performed by comparing the detection value of the EIT data with a preset threshold, the detection value of the plantar pressure data with a preset threshold, and the detection value of the electromyography data with a preset threshold. When the detection values ​​of the EIT data, the plantar pressure data, and the electromyography data are greater than the preset threshold, it is determined to be up to standard; when any one of the detection values ​​of the EIT data, the plantar pressure data, and the electromyography data is less than the preset threshold, it is determined to be not up to standard;

[0013] Optionally, the non-compliance includes one or more of the following: EIT does not meet the standard, plantar pressure does not meet the standard, electromyographic data does not meet the standard, EIT and plantar pressure do not meet the standard, EIT and electromyographic data do not meet the standard, plantar pressure and electromyographic data do not meet the standard, EIT, plantar pressure and electromyographic data do not meet the standard;

[0014] Optionally, the method further includes HP action optimization. When the evaluation result is not up to standard, targeted HP action optimization is performed for the not up to standard result.

[0015] The EIT data is replaced by: inspiratory volume ratio, expiratory uniformity ratio, and expiratory flow rate ratio; the inspiratory volume ratio is the ratio of the inspiratory volume of the tester when performing the HP action to the inspiratory volume during quiet breathing; the expiratory uniformity ratio is the ratio of the expiratory uniformity of the tester when performing the HP action to the expiratory uniformity during quiet breathing; the expiratory flow rate ratio is the ratio of the expiratory flow rate of the tester when performing the HP action to the expiratory flow rate during quiet breathing;

[0016] Optionally, the EIT data further includes one or more of the following: inspiratory volume ratio, expiratory uniformity ratio, expiratory flow rate ratio;

[0017] The EIT data also includes one or more of the following local ventilation parameters: left and right lung ventilation, ventilation center value;

[0018] Optionally, the local ventilation parameters of the EIT data are replaced by: left-right lung ventilation ratio, ventilation center ratio; the left-right lung ventilation ratio is the ratio of the left-right lung ventilation of the test subject when performing HP action to the left-right lung ventilation during quiet breathing; the ventilation center ratio is the ratio of the ventilation center of the test subject when performing HP action to the ventilation center of the test subject when quiet breathing;

[0019] Optionally, the EIT data also includes one or more of the following: left-right lung ventilation ratio, ventilation center ratio;

[0020] Optionally, the EIT data is judged to be up to standard when the inspiratory volume is greater than a preset threshold, the exhalation uniformity is better, the exhalation flow rate is greater than a preset threshold, the ventilation center is biased toward the ventral side, and the left and right lung ventilation is biased toward the left side.

[0021] The method further includes a frontal lobe level oxygenated hemoglobin value, based on the EIT data, the plantar pressure data, the detection value of the electromyographic data, and the frontal lobe level oxygenated hemoglobin value, performing an HP action assessment to obtain an assessment result;

[0022] Optionally, the evaluation is performed by comparing the detection value of the EIT data with a preset threshold, the detection value of the plantar pressure data with a preset threshold, the detection value of the electromyographic data with a preset threshold, and the oxygenated hemoglobin value at the frontal lobe level with a preset threshold. When the detection value of the EIT data, the plantar pressure data, the detection value of the electromyographic data, and the oxygenated hemoglobin value at the frontal lobe level are all greater than the preset threshold, it is determined that the standard is met; when any one of the detection value of the EIT data, the plantar pressure data, the electromyographic data, and the oxygenated hemoglobin value at the frontal lobe level is less than the preset threshold, it is determined that the standard is not met;

[0023] Optionally, the non-compliance includes one or more of the following: EIT does not meet the standard, plantar pressure does not meet the standard, electromyographic data does not meet the standard, frontal level oxygenated hemoglobin value does not meet the standard, EIT and plantar pressure do not meet the standard, EIT and electromyographic data do not meet the standard, plantar pressure and electromyographic data do not meet the standard, EIT and frontal level oxygenated hemoglobin value does not meet the standard, plantar pressure and frontal level oxygenated hemoglobin value does not meet the standard, electromyographic data and frontal level oxygenated hemoglobin value do not meet the standard, EIT, plantar pressure and electromyographic data do not meet the standard, EIT, frontal level oxygenated hemoglobin value and electromyographic data do not meet the standard, EIT, plantar pressure and frontal level oxygenated hemoglobin value does not meet the standard, frontal level oxygenated hemoglobin value, plantar pressure and electromyographic data do not meet the standard, frontal level oxygenated hemoglobin value, EIT, plantar pressure and electromyographic data do not meet the standard.

[0024] The evaluation is replaced by: performing HP action evaluation through an evaluation model to obtain an evaluation result; the evaluation model is obtained by training the correlation between the detection values ​​of EIT data, plantar pressure data, electromyography data and the high value of eye-level systolic blood pressure;

[0025] Optionally, the training process of the evaluation model is:

[0026] Obtaining EIT data, plantar pressure data, electromyographic data detection values, and eye-level systolic blood pressure high values ​​when the subject performs HP action;

[0027] A linear regression model is constructed based on the detection values ​​of the EIT data, the plantar pressure data, the electromyography data and the high value of the eye-level systolic blood pressure to obtain an evaluation model;

[0028] Optionally, in the linear regression model, the high value of eye level systolic pressure is the dependent variable, and the detection values ​​of EIT data, plantar pressure data, and electromyography data are independent variables;

[0029] Optionally, the training process of the evaluation model is replaced by:

[0030] Obtaining EIT data, plantar pressure data, electromyographic data detection values, and eye-level systolic blood pressure high values ​​when the subject performs HP action;

[0031] Calculating the relationship between the detection values ​​of the EIT data, the plantar pressure data, and the electromyography data and the high value of the eye-level systolic pressure to obtain a mapping relationship;

[0032] The mapping relationship is input into a neural network model for training to obtain an evaluation model.

[0033] The evaluation model is replaced by: the evaluation result is obtained by performing HP action evaluation through the first evaluation model; the first evaluation model is obtained by training the correlation between EIT data, plantar pressure data, detection values ​​of electromyography data, frontal lobe level oxygenated hemoglobin value and eye level systolic high value;

[0034] Optionally, the training process of the first evaluation model is:

[0035] Obtaining the EIT data, plantar pressure data, electromyographic data detection values, frontal lobe level oxygenated hemoglobin value and eye level systolic blood pressure high value when the subject performs HP action;

[0036] A linear regression model is constructed based on the EIT data, the plantar pressure data, the detection value of the electromyography data, the oxygenated hemoglobin value at the frontal lobe level and the high value of the systolic blood pressure at the eye level to obtain a first evaluation model;

[0037] Optionally, in the regression model of the first evaluation model, the high value of eye-level systolic pressure is a dependent variable, and the detection values ​​of EIT data, plantar pressure data, electromyography data, and frontal lobe level oxygenated hemoglobin value are independent variables;

[0038] Optionally, the training process of the first evaluation model is replaced by:

[0039] Obtaining the EIT data, plantar pressure data, electromyographic data detection values, frontal lobe level oxygenated hemoglobin value, and eye level systolic high blood pressure value when the subject performs HP action;

[0040] Calculating the relationship between the EIT data, the plantar pressure data, the detection value of the electromyography data, the oxygenated hemoglobin value at the frontal lobe level and the high value of the systolic pressure at the eye level to obtain a mapping relationship;

[0041] The mapping relationship is input into a neural network model for training to obtain a first evaluation model.

[0042] Optionally, the evaluation model is replaced by: performing HP action evaluation through a second evaluation model to obtain an evaluation result; the second evaluation model is obtained by training the correlation between the detection values ​​of EIT data, plantar pressure data, electromyography data and the oxygenated hemoglobin value at the frontal lobe level;

[0043] Optionally, the training process of the second evaluation model is:

[0044] Obtaining EIT data, plantar pressure data, electromyographic data detection values, and frontal lobe oxygenated hemoglobin values ​​when the subject performs HP action;

[0045] A linear regression model is constructed based on the detection values ​​of the EIT data, the plantar pressure data, the electromyography data and the oxygenated hemoglobin value at the frontal lobe level to obtain a second evaluation model.

[0046] Optionally, in the regression model of the second evaluation model, the oxygenated hemoglobin value at the frontal lobe level is the dependent variable, and the detection values ​​of EIT data, plantar pressure data, and electromyography data are independent variables.

[0047] The plantar pressure parameter is obtained by a plantar pressure device, which includes an insole, N pressure sensors, a signal transmission circuit, and an external circuit module; the N pressure sensors are fixed on the insole and connected to form a closed loop through the signal transmission circuit, the signal transmission circuit is connected to the external circuit module, and the external circuit module is used to process signal data, and N is a natural number greater than or equal to 4;

[0048] When the pressure sensor is subjected to pressure, a pressure signal is generated, and the pressure signal is transmitted to the external circuit module through the signal transmission circuit, and the external circuit module receives the pressure signal to obtain the plantar pressure data;

[0049] Optionally, the number of the pressure sensors is 4-8;

[0050] Optionally, the number of the pressure sensors is 4;

[0051] Optionally, when the number of the pressure sensors is 4, the pressure sensors are respectively fixed on the thumb area, the first metatarsal area, the outer side area of ​​the arch, and the rear side area of ​​the heel;

[0052] Optionally, when the number of the pressure sensors is 8, the pressure sensors are respectively fixed on the thumb area, the first metatarsal area, the lateral arch area, the posterior heel area, the second and third metatarsal areas, the fourth and fifth metatarsal areas, the medial heel area, and the lateral heel area.

[0053] The object of the present invention is to provide a computer program product, which includes a computer program or instructions, and the computer program or instructions are executed by a processor to implement the above-mentioned flight anti-overload HP action evaluation method.

[0054] The object of the present invention is to provide a computer device, which includes a memory, a processor and a computer program or instructions stored in the memory, wherein the computer program or instructions are executed by the processor to implement the above-mentioned flight anti-overload HP action evaluation method.

[0055] The object of the present invention is to provide a computer-readable storage medium having a computer program or instructions stored thereon, wherein the computer program or instructions are executed by a processor to implement the above-mentioned flight anti-overload HP action evaluation method.

[0056] Advantages of the present invention:

[0057] 1. HP action evaluation is performed through multiple parameters, including EIT parameter data, plantar parameter data, and electromyography parameter data. This method conducts a comprehensive test on the test subject when performing HP action, forming a complete test process and test system, which is helpful for the reliability and accuracy of HP action detection.

[0058] 2. Regarding plantar parameters, experiments have shown that pressure measurement of the thumb area, first metatarsal area, lateral arch area, and posterior heel area among plantar parameters can better detect the standard degree of the test subject when performing HP movements compared with other areas, which helps to improve the detection accuracy of HP movements.

[0059] 3 The present invention also proposes the frontal lobe level oxygenated hemoglobin value, which is used together with the EIT parameter data, the plantar parameter data, and the electromyographic parameter data to detect HP movements, thereby adding a new dimension and improving the reliability of HP detection; secondly, targeted HP movement optimization and improvement are carried out based on the evaluation results. The EIT parameter is used to determine whether the breathing during the HP movement is up to standard, the plantar parameter data and the electromyographic parameter data are used to determine whether the plantar and lower limb movements are up to standard respectively, and the frontal lobe level oxygenated hemoglobin value is used to determine whether the change in deoxyhemoglobin concentration is normal to meet the requirements for HP movements, which is helpful for HP movement training.

[0060] 4. A regression model is constructed based on the frontal lobe level oxygenated hemoglobin value, EIT parameter data, plantar parameter data, and electromyography parameter data, and the constructed regression equation is used to conduct real-time and accurate evaluation of the training effect of pilots' AGSM. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0062] Figure 1 A schematic flow chart of a method for evaluating flight anti-overload HP action provided by an embodiment of the present invention;

[0063] Figure 2 A schematic diagram of an evaluation system for flight anti-overload HP action provided by an embodiment of the present invention;

[0064] Figure 3 A schematic diagram of an evaluation device for flight anti-overload HP action provided by an embodiment of the present invention;

[0065] Figure 4 A schematic diagram of the attachment positions of surface electromyography electrodes and the positions of insoles for measuring plantar pressure provided in an embodiment of the present invention;

[0066] Figure 5 The average value of eye-level systolic pressure during quiet breathing and HP action of the subject provided in the embodiment of the present invention;

[0067] Figure 6 EIT impedance-time curves provided in the embodiments of the present invention, A: impedance-time curves of one subject during calm breathing and HP action; B: impedance-time curves of two subjects during HP action. a: P < 0.001, compared with the impedance value during calm breathing; b: P < 0.001, compared with the impedance value of subject 1; AU: arbitrary unit;

[0068] Figure 7Inspiratory volume during calm breathing and HP maneuver (A) and inspiratory volume ratio of different groups (B) provided by the embodiment of the present invention; A: IV dot-line graphs during calm breathing and HP maneuver P1-P12 represent 12 subjects respectively, 85.5321 and 233.4905 represent the IV mean values ​​of 12 subjects during calm breathing and HP maneuver respectively; B: IVHP / IVPJ box-line graph between different groups. a: P < 0.001, compared with the mean inspiratory volume during calm breathing; b: P < 0.01, compared with the d < 30 mmHg group; AU: arbitrary unit;

[0069] Figure 8 The uniformity of exhalation during quiet breathing and HP action of the subject provided in the embodiment of the present invention;

[0070] Fig. 9 The exhaled flow rate during quiet breathing and HP maneuvers of the subject provided in the embodiment of the present invention;

[0071] Fig.10 The uniformity of exhalation during quiet breathing and HP action of the subject provided in the embodiment of the present invention;

[0072] Fig.11 The breathing time during calm breathing and HP action of the subject provided by the embodiment of the present invention;

[0073] Fig.12 The central mean value of ventilation during quiet breathing and HP action of the subject provided in the embodiment of the present invention;

[0074] Fig.13 The mean value of the left and right lung ventilation ratio during the quiet breathing and HP maneuver of the subject provided in the embodiment of the present invention;

[0075] Fig.14 iEMG box plots of three muscles of the subjects at rest and during HP action provided by the embodiment of the present invention;

[0076] Fig.15 The box plot of the mean plantar pressure of the right foot of the subjects provided in the embodiment of the present invention, a: the difference between the 5-point relaxation state and the other points is statistically significant (p < 0.05); b: the difference between the 8-point relaxation state and the other points (except 5) is statistically significant (p < 0.001);

[0077] Fig.16 HbO2 box plots during calm and HP maneuvers provided by an embodiment of the present invention;

[0078] Fig.17 A regression equation fitting diagram provided by an embodiment of the present invention;

[0079] Fig.18Bland-Altman consistency test provided by the embodiment of the present invention;

[0080] Fig.19 The single factor linear regression scatter point and fitting equation diagram provided by the embodiment of the present invention. DETAILED DESCRIPTION

[0081] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0082] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The sequence numbers of the operations, such as S101, S102, etc., are only used to distinguish between different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0083] Figure 1 A schematic diagram of a method for evaluating flight anti-overload HP action provided by an embodiment of the present invention specifically includes:

[0084] S101: Acquire the detection values ​​of the EIT data, plantar pressure data, and electromyographic data of the subject to be tested;

[0085] The EIT data includes one or more of the following overall ventilation parameters: inspiratory volume value, expiratory uniformity value, expiratory flow rate value; the electromyographic data includes one or more of the following: gastrocnemius ratio, rectus femoris ratio, rectus abdominis ratio;

[0086] In one embodiment, the plantar pressure includes pressure in one or more of the following areas: the thumb area, the first metatarsal area, the lateral arch area, and the rear heel area.

[0087] In one embodiment, the plantar pressure area further includes one or more of the following: the second and third metatarsal areas, the fourth and fifth metatarsal areas, the medial heel area, and the lateral heel area.

[0088] In one embodiment, the gastrocnemius ratio is the ratio of the electromyographic signal value of the gastrocnemius muscle when the tester performs HP action to the electromyographic signal value of the gastrocnemius muscle when the tester breathes quietly;

[0089] Optionally, the rectus femoris ratio is a ratio of an electromyographic signal value of the rectus femoris when the tester performs HP action to an electromyographic signal value of the rectus femoris when the tester breathes quietly;

[0090] Optionally, the rectus abdominis ratio is a ratio of an electromyographic signal value of the rectus abdominis when the tester performs the HP action to an electromyographic signal value of the rectus abdominis when the tester breathes quietly.

[0091] In one embodiment, the EIT data is replaced by: inspiratory volume ratio, expiratory uniformity ratio, and expiratory flow rate ratio; the inspiratory volume ratio is the ratio of the inspiratory volume of the test subject when performing HP maneuvers to the inspiratory volume during calm breathing; the expiratory uniformity ratio is the ratio of the expiratory uniformity of the test subject when performing HP maneuvers to the expiratory uniformity during calm breathing; the expiratory flow rate ratio is the ratio of the expiratory flow rate of the test subject when performing HP maneuvers to the expiratory flow rate during calm breathing.

[0092] In one embodiment, the EIT data further includes one or more of the following: an inspiratory volume ratio, an expiratory uniformity ratio, and an expiratory flow rate ratio.

[0093] The EIT data also includes one or more of the following local ventilation parameters: left and right lung ventilation, ventilation center value;

[0094] In one embodiment, the local ventilation parameters of the EIT data are replaced by: left-right lung ventilation ratio, ventilation center ratio; the left-right lung ventilation ratio is the ratio of the left-right lung ventilation when the tester performs HP action to the left-right lung ventilation during quiet breathing; the ventilation center ratio is the ratio of the ventilation center when the tester performs HP action to the ventilation center during quiet breathing.

[0095] In one embodiment, the EIT data further includes one or more of the following: left-right lung ventilation ratio and ventilation center ratio.

[0096] In one embodiment, the plantar pressure parameter is obtained by a plantar pressure device, which includes an insole, N pressure sensors, a signal transmission circuit, and an external circuit module; the N pressure sensors are fixed on the insole and connected to form a closed loop through the signal transmission circuit, the signal transmission circuit is connected to the external circuit module, and the external circuit module is used to process signal data, and N is a natural number greater than or equal to 4;

[0097] When the pressure sensor is subjected to pressure, a pressure signal is generated, and the pressure signal is transmitted to the external circuit module through the signal transmission circuit, and the external circuit module receives the pressure signal to obtain the plantar pressure data;

[0098] In one embodiment, the number of the pressure sensors is 4-8.

[0099] In one embodiment, the number of the pressure sensors is 4.

[0100] In one embodiment, when the number of the pressure sensors is 4, the pressure sensors are respectively fixed on the thumb area, the first metatarsal area, the outer side area of ​​the arch, and the rear side area of ​​the heel.

[0101] In one embodiment, when the number of the pressure sensors is 8, the pressure sensors are respectively fixed on the thumb area, the first metatarsal area, the lateral arch area, the posterior heel area, the second and third metatarsal areas, the fourth and fifth metatarsal areas, the medial heel area, and the lateral heel area.

[0102] In a specific embodiment, the experiment was conducted in March 2023, and 8 healthy volunteers from the Air Force Medical University were selected as research subjects. They had no previous exposure to anti-gravity training and were beginners. The inclusion criteria were: no history of smoking, no respiratory diseases, no serious cardiovascular and nervous system diseases, no infectious diseases, and those with severe flat feet and lower limb trauma were excluding those with severe flat feet and lower limb trauma.

[0103] Before the formal experiment began, a professional instructor (proficient in AGSM) guided all subjects (i.e. beginners) to learn the essentials of the HP and L1 movements. The subjects practiced AGSM repeatedly until they fully learned it and passed the professional instructor's inspection. According to the chest size of the subjects, an EIT electrode belt (with 16 equidistant rubber electrodes) of the same length was selected. After disinfecting the EIT electrode belt and the body surface with alcohol cotton pads, physiological saline was applied to the electrode belt, and then the EIT electrode belt was placed horizontally between the 4th and 5th intercostal spaces of the subjects, and the collection box was connected to the electrode belt. The EIT electrode belt No. 1 / 16 / 8 / 9 was pasted on the skin surface with conductive glue to prevent disconnection. The lung EIT imager was used for real-time imaging of lung ventilation (the lung EIT imager used Bluetooth to transmit data, the acquisition device volume was 12cm×8cm×3cm, the number of electrodes was 16, the acquisition frame rate was 20Hz, the excitation current was 700μA, and the imaging speed was 20 frames / second). 3M disposable ECG electrodes were used for surface electromyography electrodes. After disinfecting the skin with alcohol, they were pasted on the highest part of the muscle belly parallel to the direction of the muscle fibers. The electrodes were about 2 cm apart, and the reference electrode was placed on the protruding part of the bony landmark (low muscle activity). In this study, the center of the patella was selected. The gastrocnemius, rectus femoris, and rectus abdominis were pasted at the following locations: Figure 4As shown in A. The plantar pressure measuring insole was placed in the subject's shoe, and the 50Kg range 8-point plantar pressure measuring insole was used. The subjects wore black physical training shoes, and the pedaling rudder was changed to pedaling the ground to cover all points of the multi-point plantar pressure insole. A near-infrared headband was worn on the frontal lobe of the head, and the near-infrared device headband was fixed to the forehead with a self-adhesive elastic bandage to prevent interference caused by exercise. The subjects sat in the anti-gravity physiological training instrument (GD-KHXLY-1.1 model), and were asked to breathe calmly and relax. The baseline data was collected for 2 minutes, and the systolic blood pressure was relatively stable for 20 seconds for analysis; the maximum pedaling force test was performed with the maximum force pedaling, followed by HP action and L1 action for 30 seconds each three times, during which multi-dimensional physiological parameters were measured, with an interval of at least 5 minutes to ensure that they returned to rest, and the largest change in the mean systolic blood pressure of 5 to 25 seconds was taken for analysis. Non-invasive continuous blood pressure is measured using eye-level arterial pressure. The AGSM load resistance is reflected by the increase in eye-level systolic pressure (d), which is calculated as follows: the mean eye-level systolic pressure (SBPHP) during 5 to 25 seconds of HP action minus the mean eye-level systolic pressure during 20 seconds of quiet breathing (SBPPJ) (i.e., d = SBPHP-SBPPJ), in mmHg. The specific method is: connect the hardware equipment of the non-invasive continuous blood pressure measurement instrument (cuff, finger cuff, height calibrator), perform physiological correction and reflux correction, and obtain the exact heart-level blood pressure of each subject, then place the cylindrical device (heart-level height) at the level of the outer canthus of the eye, and the blood pressure converted according to the fluid statics of the calibrator is the continuous eye-level blood pressure. In a specific embodiment, EIT data processing:

[0104] EIT data analysis was carried out based on Matlab R2022a (The MathWorks Inc, Natick, MA, USA), and the EIDORS 2.8 platform was used to complete the reconstruction of lung EIT images. The reconstruction method adopted the GREIT algorithm. A finite element model was constructed based on the real adult male CT image, and then the reconstruction matrix was calculated. The reconstructed EIT image consists of 32*32 pixels. The end of exhalation in a calm breathing state was selected as the reference frame to reconstruct the lung EIT image during the entire experiment. The key EIT parameters used in this study are described as follows. Inspiratory Volume (IV): The EIT image at the beginning of inspiration minus the EIT image at the beginning of exhalation reflects the change in inspiratory volume. The average of the inspiratory volume of more than 5 respiratory cycles was used as the final value, and the calculation formula is as follows:

[0105]

[0106] Among them, IV i is pixel i in the EIT image; N is the number of breaths included in the analysis, which is ≥5 in this study; ΔZ i,Ins,n and ΔZ i,Exp,nare the pixel values ​​of the original EIT image at the beginning of inspiration and expiration respectively. i <0, assign 0 to IV i .

[0107] Expiratory uniformity (EU): It is obtained by calculating the change in the exhaled volume per unit time during each exhalation period, reflecting the uniformity of exhalation during quiet breathing and anti-gravity maneuvers. The average of the exhaled uniformity of more than 5 breathing cycles is used as the final value, and the unit time is defined as 200ms. The calculation formula is as follows:

[0108]

[0109] in, is the global impedance change in the mth unit time in the nth respiratory cycle; N is the number of breaths included in the analysis, which is ≥5 in this study; and the unit time is defined as 200ms.

[0110] Expiratory speed (ES): It is calculated by calculating the ratio of the expiratory volume to the expiratory time during each exhalation period, reflecting the exhalation flow rate during quiet breathing and anti-gravity maneuvers. The average of the expiratory uniformity of more than 5 breathing cycles is used as the final value. The calculation formula is:

[0111]

[0112] in, is the change in tidal impedance during the nth respiratory cycle; N is the number of breaths included in the analysis, which was ≥5 in this study.

[0113] Center of ventilation (CoV): The change of CoV reflects the vertical ventricular and dorsal changes caused by AGSM.

[0114] The regional ventilation distribution change on the posterior and posterior coordinates (the weighted position of the relative impedance value) is calculated as follows:

[0115]

[0116] Among them, TV i is the impedance change of pixel i in the fEIT image, y i is the pixel height and pixel i scaled so that the bottom (dorsal) of the image is 100% and the top (ventral) is 0%. The average of AGSM breathing actions ≥ 5 breaths is calculated as the final value. Left-right lung ventilation ratio (RtoL): The change in RtoL reflects the change in regional ventilation distribution of the left and right lungs caused by AGSM. The calculation formula is as follows:

[0117]

[0118] Among them, ROI1-4 are the regional impedance changes of the specific customized region of interest (ROI), ROI1 and ROI3 are the regional impedance changes of the upper right area and the lower right area, ROI2 and ROI4 are the regional impedance changes of the upper left area and the lower left area; N is the number of breaths included in the analysis, which is ≥5 in this study.

[0119] In a specific embodiment, surface electromyography data processing;

[0120] Root mean square amplitude (RMS): RMS represents the strength of the electromyographic signal. Its size is related to the number of motor units involved in the activity and the degree of synchronization of the discharge frequency. In practical applications, it is often used to reflect the energy generated by electromyography. As fatigue deepens, RMS tends to rise. The calculation formula for the RMS value is:

[0121]

[0122] Among them, t is the time of EMG signal acquisition, and xt is the size of EMG signal.

[0123] Integrated electromyographic value (iEMG): The integrated electromyographic value (iEMG) is the sum of the areas under the curve per unit time after the electromyographic signal is rectified and filtered. It can reflect the strength changes of the electromyographic signal over time. iEMG is used to analyze the contraction characteristics of muscles per unit time, indicating the total discharge of the motor units involved in the muscle activity within a certain period of time. The calculation formula is as follows:

[0124]

[0125] Wherein, t1 is the time when the electromyographic signal collection starts, t2 is the time when the electromyographic signal collection ends, and x(t) is the size of the electromyographic signal. In a specific embodiment, near-infrared data processing: Previous research data show that the absorption rate of water in the blood to light in the near-infrared spectral window (i.e., the spectral window with a wavelength of 600-900nm) is the smallest, and the absorption rate of oxygenated hemoglobin (HbO2 or HbO) and deoxygenated hemoglobin (Hb or HbR) is relatively large. The concentration of oxygenated hemoglobin and deoxygenated hemoglobin is related to the Lambert-Beer law. The Lambert-Beer law is a formula that describes the relationship between incident light and outgoing light after light is incident on a non-scattering medium. The formula is as follows:

[0126]

[0127] The concentration change of HbO2 is calculated according to the Lambert-Beer law formula, and the calculation results are as follows:

[0128]

[0129] Among them, λ 1 represents the incident light intensity, λ 2 represents the received light intensity, DPF represents the differential path factor, HbO represents oxygenated hemoglobin, and HbR represents deoxygenated hemoglobin.

[0130] In one specific embodiment, SPSS27.0 (IBM, USA) was used to complete the statistical analysis. The Shapro-Wilk test was used to test the normality of the data. After confirming that the data were normally distributed, the measurement data were expressed as mean ± standard deviation (x ± s). The paired sample t test was used to compare the pulmonary ventilation parameters of quiet breathing and AGSM, and the independent sample t test was used to compare the plantar pressure parameters of the bivariate intra-group data (i.e., quiet breathing vs. HP action). The single-factor ANOVA test was used to compare the multivariate intra-group and inter-group data, i.e., quiet breathing, maximum pedal force, and surface electromyography parameters of AGSM. The LSD test was used for post hoc comparisons, and p < 0.05 was considered to be statistically significant.

[0131] In a specific embodiment, the eye level systolic pressure results are: the average eye level systolic pressure during quiet breathing is (95.17±8.51) mmHg, and the average eye level systolic pressure during HP action is (148.82±22.75) mmHg. The eye level systolic pressure during HP action is significantly increased, and the difference is statistically significant (P<0.001, Figure 5 ).

[0132] EIT results: EIT impedance-time curve comparison. The impedance-time curve of the subjects when performing HP maneuvers was significantly different from that of the subjects when they were breathing quietly (P<0.001, Figure 6 The impedance-time curves of different subjects when performing HP action were significantly different (P<0.001, Figure 6 B), EIT can reflect the changes in lung ventilation and realize real-time monitoring of lung ventilation images.

[0133] Comparison of IV during quiet breathing and HP maneuvers: Compared with quiet breathing, the inspiratory volume (IV) during HP maneuvers of beginners was significantly increased, and the difference was statistically significant (P < 0.001, Figure 7 The subjects were divided into three groups according to d>60, 30~60, and <30 mmHg. The inspiratory volume ratio (IVHP / IVPJ) was the largest in the d>60 mmHg group and the smallest in the d<30 mmHg group, and the difference was statistically significant (P<0.01, Figure 7 B).

[0134] Comparison of EU during quiet breathing and HP maneuvers: Compared with quiet breathing, the exhalation uniformity value (EU) of beginners during HP maneuvers was significantly reduced, and the exhalation was more uniform, with a statistically significant difference (P < 0.05, Figure 8 ).

[0135] Comparison of ES during quiet breathing and HP maneuvers: Compared with quiet breathing, the expiratory flow rate (ES) during HP maneuvers for beginners was significantly faster, and the difference was statistically significant (P < 0.001, Fig. 9 ).

[0136] Comparison of EU during quiet breathing and HP maneuvers: Compared with quiet breathing, the exhalation uniformity value (EU) of the subjects during HP maneuvers was significantly reduced, and the exhalation was more uniform, with a statistically significant difference (P < 0.05, Fig.10 ).

[0137] Comparison of breathing time during calm breathing and HP maneuvers: Compared with calm breathing, the inhalation and exhalation time during HP maneuvers for beginners were significantly shortened, and the differences were statistically significant (P < 0.001, P < 0.01, Fig.11 ), the exhalation time required by the HP action was 2.0s, but the exhalation time of beginners was shorter, and the difference was statistically significant (P<0.001, Fig.11 ).

[0138] Comparison of COV during quiet breathing and HP maneuvers: Compared with quiet breathing, the ventilation center (COV) of beginners during HP maneuvers was significantly smaller and tended to the ventral side, with a statistically significant difference (P < 0.001, Fig.12 ).

[0139] Comparison of RtoL during quiet breathing and HP maneuvers: Compared with quiet breathing, the ventilation ratio (RtoL) of the left and right lungs during HP maneuvers for beginners decreased, and the ventilation distribution was biased towards the left lung, with a statistically significant difference (P < 0.05, Fig.13 ).

[0140] sEMG Results Section:

[0141] The results of surface electromyography integrated electromyography (iEMG) (unit: μV·s) are as follows Fig.14As shown, in the resting state vs HP action: gastrocnemius 40.486±16.305 vs 1154.478±756.884 (p<0.001), rectus femoris 83.844±9.688 vs 1221.072±705.610 (p<0.001), rectus abdominis 114.248±35.701 vs 879.562±449.420 (p<0.001), the iEMG of the three muscles increased significantly during the HP action; the maximum force vs HP action: gastrocnemius 1211.694±779.679 vs 1154.478±756.884, rectus femoris 1266.028±695.562 vs 1221.072±705.610, rectus abdominis 813.311±445.013vs 879.562±449.420, the differences were not statistically significant (p>0.05), but compared with the maximum pedaling force state, the surface electromyography iEMG of the rectus abdominis was larger during HP action.

[0142] Multi-point plantar pressure results section:

[0143] The average multi-point plantar pressure (unit: Kgf) results are as follows Fig.15 As shown in the data, (1) compared with the other eight points in the calm state, the mean pressure values ​​of ⑤ the lateral area of ​​the arch and ⑧ the posterior area of ​​the heel were greater, and the difference was statistically significant (p < 0.05, p < 0.001); (2) in the calm state vs. HP action: the mean pressure values ​​of ① the thumb area, ② the first metatarsal area, ⑤ the lateral area of ​​the arch, and ⑧ the posterior area of ​​the heel during the HP action were significantly increased, and the difference was statistically significant (p < 0.01, p < 0.001). The mean pressure values ​​of the other four points: ③ the second and third metatarsal areas; ④ the fourth and fifth metatarsal areas; ⑥ the medial area of ​​the heel; and ⑦ the lateral area of ​​the heel were increased.

[0144] Near infrared results section: Fig.16 As shown in the figure, the mean value of oxygenated hemoglobin (HbO2) at the frontal lobe level in the calm state was 1.54, and the mean value during the HP action was 23.26, which was significantly higher than that in the calm state, and the difference was statistically significant (p < 0.001). It showed the same change trend as the systolic blood pressure at the eye level, which was in line with theoretical expectations.

[0145] In a specific embodiment, EIT can be used to monitor and evaluate pulmonary ventilation and respiratory muscle contraction during the HP maneuver breathing phase. From the perspective of EIT overall pulmonary ventilation, ① the EIT impedance-time curve of the subject during the HP maneuver is very different from that of quiet breathing. The impedance value during the HP maneuver is larger, reflecting that the pulmonary ventilation volume is larger, but for beginners, there are also obvious differences in the HP maneuver. During the HP maneuver, the pulmonary ventilation of test subject No. 1 is extremely unstable. First, the pulmonary impedance at the end of exhalation becomes smaller and smaller, then increases, and then decreases again, that is, the residual volume undergoes a change of decrease-increase-decrease, and the impedance change range at the end of inspiration is also extremely large, and the intrathoracic pressure established thereby fluctuates greatly; but during the HP maneuver, the pulmonary ventilation of test subject No. 2 is relatively stable, and the pulmonary impedance at the end of inspiration and exhalation changes very little, and the intrathoracic pressure established thereby is relatively stable, and the breathing action is more standard than that of test subject No. 1. Therefore, the EIT impedance-time curve can be used as an important reference for monitoring and guiding beginners to perform correct and effective breathing actions. ② Compared with calm breathing, the subjects' inspiratory volume (IV) during the HP maneuver was significantly greater (P < 0.001), which was consistent with the requirement of the HP maneuver for subjects to inhale a moderate amount of air quickly. The grouping results showed that the inspiratory volume ratio (IVHP / IVPJ) was the largest in the d > 60 mmHg group and the smallest in the d < 30 mmHg group, and the difference was statistically significant (P < 0.01). With the increase of the eye level systolic pressure difference, IVHP / IVPJ also increased, confirming the previous research that intrathoracic pressure increases with the increase of inspiratory volume, thereby increasing the eye level arterial pressure. At the same time, it shows that for beginners, the rapid inspiratory volume greatly affects the establishment of intrathoracic pressure. Therefore, in the AGSM training of beginners, the size of their rapid inspiratory volume (IV) should be focused on. ③Compared with calm breathing, the expiratory flow rate (ES) of the subjects during the HP maneuver was significantly faster (P < 0.001). This is related to the fact that the HP maneuver requires the subjects to slightly open their lips to form an expiratory throttle, pronounce the Chinese pinyin "P" (splash) and exhale forcefully. Forced exhalation leads to a faster expiratory flow rate than calm breathing. Therefore, the expiratory flow rate (ES) can be used to evaluate the exhalation phase of the HP maneuver. ④The inspiratory time of the HP maneuver was (0.77 ± 0.32) s, which was slightly slower than the standard requirement of 0.5 s, and the expiratory time was (1.59 ± 0.21) s < 2.0 s, which was faster than the standard requirement of 2.0 s. The difference was statistically significant (P < 0.001). Combined with the analysis of inspiratory volume (IV), for beginners, shallow and fast breathing is a more prominent problem in beginners' AGSM training.

[0146] From the perspective of EIT local lung ventilation, ① the ventilation center (COV) of the subjects during HP maneuvers was significantly smaller than that during calm breathing, and the ventilation center was more ventral (P < 0.001). The possible reasons are: during HP maneuvers, chest breathing is mainly performed. Due to forced breathing, the contraction degree of muscles such as the external intercostal muscles increases, driving the full expansion of the thorax. The spine is located in the center of the back of the thorax, and the two sides are wrapped by powerful erector spinae, latissimus dorsi, trapezius and other muscles, which limits its expansion. The lung tissue expands more ventrally, making the COV, an indicator reflecting the changes in the distribution of ventral and dorsal lung ventilation, smaller, and the ventilation center is more ventral. Therefore, the size of COV can be used as a sensitive indicator to reflect the forced contraction of chest respiratory muscles such as the external intercostal muscles. ②During HP maneuvers, the RtoL of the subjects was reduced compared with that of calm breathing, and the ventilation distribution changed from being biased towards the right lung during calm breathing to being biased towards the left lung (P < 0.05), that is, the ventilation of the right lung was slightly greater than that of the left lung during calm breathing, while the ventilation of the left lung was greater than that of the right lung during HP maneuvers. The reasons may be: the anatomical position of the heart is biased to the left, and the right lung is slightly larger than the left lung in normal people. The trachea is divided into left and right main bronchi. The left main bronchus is thin and long, runs obliquely, and enters the left lung through the left hilum. The right main bronchus is thick and short, runs steeply, and enters the left lung through the left hilum. The right hilum enters the right lung, so the right lung ventilation of a normal person is slightly greater than that of the left lung during quiet breathing. During the HP maneuver, forced breathing increases the up and down movement of the diaphragm (the main respiratory muscle). During inhalation, the diaphragm descends more, the heart moves downward, and the left lung expands more. During exhalation, the diaphragm lifts higher, the heart moves upward, compressing the left lung, making the residual volume of the left lung less than that of quiet breathing, causing the left lung ventilation to be greater than the right lung during the HP maneuver. Therefore, the size of RtoL can be used as a sensitive indicator to reflect the forceful contraction of the diaphragm and other respiratory muscles.

[0147] The results showed that: from the perspective of overall ventilation, EIT can image pulmonary ventilation in real time and reflect the size of pulmonary ventilation during HP maneuvers; compared with quiet breathing, the inspiratory volume (IV) during HP maneuvers was larger, the expiratory uniformity (EU) was better, and the expiratory flow rate (ES) was faster; from the perspective of local ventilation, compared with quiet breathing, the ventilation center (COV) during HP maneuvers was biased toward the ventral side, the RtoL was smaller, and the ventilation distribution was biased toward the left side, all of which were related to the forced contraction of respiratory muscles (external intercostal muscles, diaphragm, etc.). As a real-time pulmonary function imaging technology, EIT technology has the advantages of being non-invasive and radiation-free, portable equipment, and fast imaging speed, and has great application prospects in AGSM training and monitoring.

[0148] In a specific embodiment, multi-point plantar pressure-surface electromyography combined technology can be used for real-time and accurate monitoring and evaluation of muscle contraction during HP movements. 1. When the subject is in a calm state, the foot pressure is mainly concentrated in the ⑧ back of the heel and ⑤ the outer area of ​​the arch. When performing HP movements, the four areas with the most significant pressure changes are ① the thumb area, ② the first metatarsal area, ⑤ the outer area of ​​the arch, and ⑧ the back of the heel. Therefore, in HP movement training, pilots should emphasize the focus of these four areas. At the same time, the design of the focus points of these four areas should be fully considered in the design of the fighter's rudder, so as to provide sufficient support for fighter pilots to maximize their anti-load capacity; 2. During the HP movement, the iEMG of the lower limbs and abdominal muscles increased significantly (that is, the muscle strength increased significantly), but the increase in the iEMG of the rectus abdominis was The iEMG of the lower limb muscles during HP action is lower than that of the lower limb muscles, which means that the lower limb muscles are greater than the abdominal muscles in terms of muscle contribution. Since there is more abdominal fat than in the lower limbs, there is a certain degree of interference with the surface electromyographic signal, so this result needs further verification; 3. Compared with the maximum pedaling force state, the iEMG of the lower limb muscles during HP action is low, but the iEMG of the rectus abdominis increases, that is, the muscle strength of the rectus abdominis during maximum pedaling force is smaller than that of HP action. Therefore, the maximum pedaling force may not be used as the only evaluation indicator of the contraction of the lower limb and abdominal muscles. During training, there may be a situation where too much attention is paid to the size of the pedaling force but the use of the abdominal muscles is ignored. Therefore, among beginners, it may be more important to emphasize the contraction of the abdominal muscles.

[0149] In a specific embodiment, a near-infrared device can replace the eye-level systolic blood pressure device and be integrated into the lining of the flight helmet for real-time monitoring of AGSM. At present, the non-invasive real-time collection of systolic blood pressure mainly relies on infrared plethysmography, and the blood vessels in the head are all located deep in the brain. It is difficult for the device to penetrate into the brain to accurately find the blood vessels and collect the eye-level systolic blood pressure. Therefore, a technology is needed to replace the eye-level systolic blood pressure to evaluate the AGSM effect. The oxygenated hemoglobin (HbO2) calculated by the near-infrared spectral signal used in this experiment increases significantly during AGSM, and the near-infrared device collects near-infrared spectral signals, using the light intensity emitted and received to reflect the changes in the concentration of oxygenated hemoglobin and deoxygenated hemoglobin in the frontal lobe. It can be integrated into the lining of the pilot's helmet and may become an alternative indicator of eye-level systolic blood pressure for the evaluation of AGSM anti-gravity effect.

[0150] In a specific embodiment, a study on the construction of a real-time and accurate monitoring and evaluation method for AGSM was conducted in June 2024. 66 students from a university who were proficient in AGSM were selected as research subjects. They had previously undergone systematic learning and training in AGSM and were assessed as qualified and excellent by professional instructors. Their general information was: age (21.3±1.06) years old; height (176.6±4.52) cm; body mass (74.0±10.13) kg. The inclusion and exclusion criteria and experimental methods were the same as above. The key indicators in the multidimensional physiological parameter experiment during AGSM were verified and modeled. Similar to beginners, the trainees' inspiratory volume (IV), expiratory flow rate (ES), left and right lung ventilation ratio (RtoL), and ventilation center (COV) were significantly different during calm and AGSM (p<0.001, p<0.01), so they can be used as key indicators for monitoring and evaluation of the AGSM respiratory action link for modeling analysis. Compared with the calm state, the surface electromyography (iEMG) of the gastrocnemius, rectus femoris, and rectus abdominis muscles increased significantly during AGSM, with statistically significant differences (all p < 0.001). Therefore, they can be used as key indicators for monitoring and evaluating the degree of muscle contraction in AGSM for modeling and analysis. Compared with the calm state, the mean plantar pressure at 8 points increased significantly during AGSM, with statistically significant differences (p < 0.001, p < 0.01). Therefore, they can be used as key indicators for monitoring and evaluating the degree of lower limb force in AGSM for modeling and analysis.

[0151] The scatter plots of the horizontal systolic blood pressure difference (d-SBP) and IV ratio (IVHP / IVPJ), ES ratio (ESHP / ESPJ), COV ratio (COVHP / COVPJ), RtoL ratio (RtoLHP / RtoLPJ), gastrocnemius, rectus femoris, rectus abdominis iEMG ratio (iEMGHP / iEMGPJ), and multi-point plantar pressure differences at eight points (d-1 to d8) showed a roughly linear trend distribution. The multivariate linear regression equation was constructed with d-SBP as the y value (dependent variable), and 15 variables including IV ratio (x1), ES ratio (x2), COV ratio (x3), RtoL ratio (x4), peroneal-iEMG ratio (x5), femoral-iEMG ratio (x6), abdominal-iEMG ratio (x7), d-1 (x8), d-2 (x9), d-3 (x10), d-4 (x11), d-5 (x12), d-6 (x13), d-7 (x14), and d-8 (x15) as x values ​​(independent variables): d-SBP = 24.23981 + 0.17675x1 - 1.0 0423x2-7.66907x3+13.69225x4+0.21246x5+0.21986x6+0.42098x70.12854x8+0.123775x9+0.26601x10+0.25644x11-2.07761-4x12+1.48067x13-0.55104x14+0.79442x15The model goodness of fit test results show that the determination coefficient (adjusted R2) is 0.864, which proves that it fits the actual situation well. The DW test value is 1.601 (between 1 and 4), and the independence is in line with the regression model hypothesis test results. F=28.478, P<0.001, the regression equation is statistically significant. Considering that the independent variable indicators are numerous and complicated, combined with the standardized coefficient β value (i.e., contribution degree), significance, collinearity and expert opinions, the multi-point plantar pressure (1, 2, 5, 6, 7) difference independent variable was eliminated and the model was optimized. The optimized multi-factor linear regression equation is as follows:

[0152] d-SBP=14.5707+0.71223x1-0.74873x2+9.58953x3+8.97892x4+0.1628x5+0.11345x6+0.43536x7-0.5264x8+0.20822x9+0.59163x10. The goodness-of-fit test results of the optimization model showed that the coefficient of determination (adjusted R2) was 0.850, proving that it fits the actual situation well. The DW test value was 1.636 (between 1 and 4), and independence was met. The regression model hypothesis test results showed that F=37.702, P<0.01, and the regression equation was also statistically significant, such as Fig.17As shown in the figure, the fitting scatter points of the two regression model equations are roughly evenly distributed on both sides of y=x, and the fit is good. Fig.18 As shown in the figure, the Bland-Altman test was used to test the consistency of the true d-SBP and the fitted linear regression equation d-SBP. Only one difference was outside the 95% CI, and the differences of the optimized model were all within the 95% CI. Both were highly consistent.

[0153] In a specific embodiment, the correlation analysis and regression equation construction of the elevated systolic blood pressure (d-SBP) at the eye level and the elevated oxyhemoglobin (d-HbO2) are as follows: Fig.19 As shown in the figure, from the scatter plot of the increase in eye-level systolic blood pressure (d-SBP, dependent variable) and the increase in oxygenated hemoglobin (d-HbO2, x value, independent variable), it can be found that the two are roughly distributed in a linear trend. Pearson correlation analysis was performed, r = 0.95677, P < 0.001, indicating that the two are highly closely related. A single-factor linear regression equation was constructed, and the model fit goodness test results showed that the determination coefficient (adjusted R2) was 0.914, proving that it was highly fitted to the actual situation. The regression model was analyzed for variance, F = 692.594, P < 0.0001, and the regression equation was statistically significant, and the model was successfully built. When pilots perform anti-gravity maneuvers, accompanied by an increase in local cerebral blood flow and oxygen metabolic rate, oxygenated hemoglobin (HbO2) will increase and deoxygenated hemoglobin (HHb) will decrease. Although previous studies have examined the relationship between cerebral blood flow velocity, cerebral cortical activity, and changes in oxygen and hemoglobin in an overweight environment, little research has been done on the relationship between oxyhemoglobin and arterial pressure at the eye level. This study was conducted based on this background, and the results of the experiment showed that there was a very close relationship between oxyhemoglobin at the frontal lobe level and the change in systolic blood pressure at the eye level (the gold standard for evaluating the anti-gravity effect of AGSM), and there was a high positive correlation.

[0154] In a specific embodiment, the above two regression equations are combined to construct a regression equation with oxyhemoglobin as the dependent variable (i.e., YHbO2), IV ratio (x1), ES ratio (x2), COV ratio (x3), RtoL ratio (x4), peroneal-iEMG ratio (x5), femoral-iEMG ratio (x6), abdominal-iEMG ratio (x7), d-3 (x8), d-4 (x9), d-8 (x10) and other 10 key indicators as independent variables. S102: HP action evaluation is performed based on the detection values ​​of the EIT data, plantar pressure data, and electromyographic data to obtain an evaluation result of whether it meets the standard or not.

[0155] In one embodiment, the evaluation is performed by comparing the EIT data detection value with a preset threshold, the plantar pressure data detection value with a preset threshold, and the electromyography data detection value with a preset threshold. When the detection values ​​of the EIT data, the plantar pressure data, and the electromyography data are greater than the preset threshold, it is determined that the standard is met; when any one of the detection values ​​of the EIT data, the plantar pressure data, and the electromyography data is less than the preset threshold, it is determined that the standard is not met.

[0156] In one embodiment, the non-compliance includes one or more of the following: EIT does not meet the standard, plantar pressure does not meet the standard, electromyographic data does not meet the standard, EIT and plantar pressure do not meet the standard, EIT and electromyographic data do not meet the standard, plantar pressure and electromyographic data do not meet the standard, EIT, plantar pressure and electromyographic data do not meet the standard.

[0157] In one embodiment, the method further includes HP action optimization. When the evaluation result is not up to standard, targeted HP action optimization is performed for the not up to standard result.

[0158] In one embodiment, when the gastrocnemius ratio, rectus femoris ratio, and rectus abdominis ratio in the electromyographic data are greater than a preset threshold, it is determined to be up to standard.

[0159] In one embodiment, when the pressure value of any one or several areas in the plantar pressure data is greater than a preset threshold, it is determined to be up to standard, and the areas are the thumb area, the first metatarsal area, the lateral arch area, the posterior heel area, the second and third metatarsal areas, the fourth and fifth metatarsal areas, the medial heel area, and the lateral heel area.

[0160] In one embodiment, the EIT data is judged to be up to standard when the inspiratory volume is greater than a preset threshold, the exhalation uniformity is better, the exhalation flow rate is greater than a preset threshold, the ventilation center is biased toward the ventral side, and the left and right lung ventilation is biased toward the left side.

[0161] In one embodiment, the method further includes a frontal lobe level oxygenated hemoglobin value, and HP motion assessment is performed based on the EIT data, plantar pressure data, detection values ​​of electromyography data, and the frontal lobe level oxygenated hemoglobin value to obtain an assessment result.

[0162] In one embodiment, the evaluation is performed by comparing the EIT data detection value with a preset threshold, the plantar pressure data detection value with a preset threshold, the electromyography data detection value with a preset threshold, and the frontal lobe level oxygenated hemoglobin value with a preset threshold. When the EIT data, the plantar pressure data, the electromyography data detection value, and the frontal lobe level oxygenated hemoglobin value are all greater than the preset threshold, it is determined that the standard is met; when any one of the EIT data, the plantar pressure data, the electromyography data detection value, and the frontal lobe level oxygenated hemoglobin value is less than the preset threshold, it is determined that the standard is not met.

[0163] In one embodiment, the non-compliance includes one or more of the following: EIT does not meet the standard, plantar pressure does not meet the standard, electromyographic data does not meet the standard, frontal level oxygenated hemoglobin value does not meet the standard, EIT and plantar pressure do not meet the standard, EIT and electromyographic data do not meet the standard, plantar pressure and electromyographic data do not meet the standard, EIT and frontal level oxygenated hemoglobin value does not meet the standard, plantar pressure and frontal level oxygenated hemoglobin value does not meet the standard, electromyographic data and frontal level oxygenated hemoglobin value do not meet the standard, EIT, plantar pressure and electromyographic data do not meet the standard, EIT, frontal level oxygenated hemoglobin value and electromyographic data do not meet the standard, EIT, plantar pressure and frontal level oxygenated hemoglobin value does not meet the standard, frontal level oxygenated hemoglobin value, plantar pressure and electromyographic data do not meet the standard, frontal level oxygenated hemoglobin value, EIT, plantar pressure and electromyographic data do not meet the standard.

[0164] In one embodiment, the evaluation is replaced by: performing HP action evaluation through an evaluation model to obtain an evaluation result; the evaluation model is obtained by training the correlation between the detection values ​​of EIT data, plantar pressure data, electromyography data and the high value of eye level systolic blood pressure.

[0165] In one embodiment, the training process of the evaluation model is:

[0166] The EIT data, plantar pressure data, electromyographic data detection values, and eye-level systolic high pressure values ​​of the subject are obtained when the subject performs HP action; a linear regression model is constructed based on the EIT data, plantar pressure data, electromyographic data detection values ​​and eye-level systolic high pressure values ​​to obtain an evaluation model.

[0167] In one embodiment, in the linear regression model, the high value of eye level systolic blood pressure is the dependent variable, and the detection values ​​of EIT data, plantar pressure data, and electromyography data are independent variables.

[0168] In one embodiment, the training process of the evaluation model is replaced by:

[0169] The EIT data, plantar pressure data, electromyographic data detection values, and eye-level systolic high values ​​of the subject when performing HP action are obtained; the relationship between the EIT data, plantar pressure data, electromyographic data detection values ​​and eye-level systolic high values ​​is calculated to obtain a mapping relationship; the mapping relationship is input into a neural network model for training to obtain an evaluation model.

[0170] In one embodiment, the evaluation model is replaced by: performing HP action evaluation through a first evaluation model to obtain an evaluation result; the first evaluation model is obtained by training through the correlation between EIT data, plantar pressure data, detection values ​​of electromyography data, frontal lobe level oxygenated hemoglobin value and eye level systolic blood pressure high value.

[0171] In one embodiment, the training process of the first evaluation model is:

[0172] Obtaining the EIT data, plantar pressure data, electromyographic data detection values, frontal lobe level oxygenated hemoglobin value and eye level systolic blood pressure high value when the subject performs HP action;

[0173] A linear regression model is constructed based on the EIT data, the plantar pressure data, the detection values ​​of the electromyography data, the oxygenated hemoglobin value at the frontal lobe level and the high value of the systolic blood pressure at the eye level to obtain a first evaluation model.

[0174] In one embodiment, in the regression model of the first evaluation model, the high value of eye-level systolic blood pressure is the dependent variable, and the detection values ​​of EIT data, plantar pressure data, electromyography data, and frontal lobe level oxygenated hemoglobin value are independent variables.

[0175] In one embodiment, the training process of the first evaluation model is replaced by:

[0176] Obtaining the EIT data, plantar pressure data, electromyographic data detection values, frontal lobe level oxygenated hemoglobin value, and eye level systolic high blood pressure value when the subject performs HP action;

[0177] Calculating the relationship between the EIT data, the plantar pressure data, the detection value of the electromyography data, the oxygenated hemoglobin value at the frontal lobe level and the high value of the systolic pressure at the eye level to obtain a mapping relationship;

[0178] The mapping relationship is input into a neural network model for training to obtain a first evaluation model.

[0179] In one embodiment, the evaluation model is replaced by: performing HP action evaluation through a second evaluation model to obtain an evaluation result; the second evaluation model is obtained by training the correlation between the detection values ​​of EIT data, plantar pressure data, electromyography data and the oxygenated hemoglobin values ​​at the frontal lobe level.

[0180] In one embodiment, the training process of the second evaluation model is:

[0181] Obtaining EIT data, plantar pressure data, electromyographic data detection values, and frontal lobe oxygenated hemoglobin values ​​when the subject performs HP action;

[0182] A linear regression model is constructed based on the detection values ​​of the EIT data, the plantar pressure data, the electromyography data and the oxygenated hemoglobin value at the frontal lobe level to obtain a second evaluation model.

[0183] The disclosed embodiments of the present invention also provide a computer program product or system, including a computer program, which, when executed by a processor, implements the above-mentioned flight anti-overload HP action evaluation method steps.

[0184] Figure 2 The schematic diagram of the flight anti-overload HP action evaluation system provided by the embodiment of the present invention specifically includes:

[0185] Acquisition unit: acquires the detection values ​​of the EIT data, plantar pressure data, and electromyographic data of the subject to be tested;

[0186] The EIT data includes one or more of the following overall ventilation parameters: inspiratory volume value, expiratory uniformity value, expiratory flow rate value; the electromyographic data includes one or more of the following: gastrocnemius ratio, rectus femoris ratio, rectus abdominis ratio;

[0187] Evaluation unit: Performing HP action evaluation based on the detection values ​​of the EIT data, plantar pressure data, and electromyography data to obtain an evaluation result of whether it meets the standard or not.

[0188] Figure 3 The schematic diagram of the flight anti-overload HP action evaluation device provided by the embodiment of the present invention specifically includes:

[0189] A memory and a processor; the memory is used to store program instructions; the processor is used to call program instructions, and when the program instructions are executed, any one of the above-mentioned flight anti-overload HP action evaluation methods is implemented.

[0190] The disclosed embodiment of the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it is any one of the above-mentioned flight anti-overload HP action evaluation methods.

[0191] The verification results of this verification embodiment show that assigning inherent weights to indications can improve the performance of the method relative to the default setting. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units. A person of ordinary skill in the art may understand that all or part of the steps in the various methods of the above-mentioned embodiments may be completed by instructing the relevant hardware through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0192] A person skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be implemented by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned medium storage can be a read-only memory, a disk or an optical disk, etc.

[0193] The above is a detailed introduction to a computer device provided by the present invention. For a person skilled in the art, according to the concept of the embodiments of the present invention, there may be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for evaluating flight anti-overload HP action, characterized in that: include: Obtain the test values ​​of the EIT data, plantar pressure data, and electromyography data of the test subject; The EIT data includes one or more of the following overall ventilation parameters: inspiratory volume value, expiratory uniformity value, expiratory flow rate value; the electromyographic data includes one or more of the following: gastrocnemius ratio, rectus femoris ratio, rectus abdominis ratio; HP action evaluation is performed based on the detection values ​​of the EIT data, plantar pressure data, and electromyography data to obtain an evaluation result of whether it meets the standard or not.

2. The flight anti-overload HP action evaluation method according to claim 1, characterized in that: The plantar pressure includes one or more of the following regional pressures: thumb area, first metatarsal area, lateral arch area, and posterior heel area; Optionally, the plantar pressure area also includes one or more of the following: the second and third metatarsal areas, the fourth and fifth metatarsal areas, the medial heel area, and the lateral heel area; Optionally, the gastrocnemius ratio is a ratio of an electromyographic signal value of the gastrocnemius muscle when the tester performs HP action to an electromyographic signal value of the gastrocnemius muscle when the tester breathes quietly; Optionally, the rectus femoris ratio is a ratio of an electromyographic signal value of the rectus femoris when the tester performs HP action to an electromyographic signal value of the rectus femoris when the tester breathes quietly; Optionally, the rectus abdominis ratio is a ratio of an electromyographic signal value of the rectus abdominis when the tester performs HP action to an electromyographic signal value of the rectus abdominis when the tester breathes quietly; Optionally, the evaluation is performed by comparing the detection value of the EIT data with a preset threshold, the detection value of the plantar pressure data with a preset threshold, and the detection value of the electromyography data with a preset threshold. When the detection values ​​of the EIT data, the plantar pressure data, and the electromyography data are greater than the preset threshold, it is determined to be up to standard; when any one of the detection values ​​of the EIT data, the plantar pressure data, and the electromyography data is less than the preset threshold, it is determined to be not up to standard; Optionally, the non-compliance includes one or more of the following: EIT does not meet the standard, plantar pressure does not meet the standard, electromyographic data does not meet the standard, EIT and plantar pressure do not meet the standard, EIT and electromyographic data do not meet the standard, plantar pressure and electromyographic data do not meet the standard, EIT, plantar pressure and electromyographic data do not meet the standard; Optionally, the method further includes HP action optimization. When the evaluation result is not up to standard, targeted HP action optimization is performed for the not up to standard result.

3. The flight anti-overload HP action evaluation method according to claim 1, characterized in that: The overall ventilation parameters of the EIT data are replaced by: inspiratory volume ratio, expiratory uniformity ratio, and expiratory flow rate ratio; the inspiratory volume ratio is the ratio of the inspiratory volume of the test subject when performing HP action to the inspiratory volume during quiet breathing; the expiratory uniformity ratio is the ratio of the expiratory uniformity of the test subject when performing HP action to the expiratory uniformity during quiet breathing; the expiratory flow rate ratio is the ratio of the expiratory flow rate of the test subject when performing HP action to the expiratory flow rate during quiet breathing; Optionally, the EIT data further includes one or more of the following overall ventilation parameters: inspiratory volume ratio, expiratory uniformity ratio, expiratory flow rate ratio; The EIT data also includes one or more of the following local ventilation parameters: left and right lung ventilation, ventilation center value; optionally, the local ventilation parameters of the EIT data are replaced by: left and right lung ventilation ratio, ventilation center ratio; the left and right lung ventilation ratio is the ratio of the left and right lung ventilation of the tester when performing HP action to the left and right lung ventilation during quiet breathing; the ventilation center ratio is the ratio of the ventilation center of the tester when performing HP action to the ventilation center of the tester when quiet breathing; Optionally, the EIT data also includes one or more of the following local ventilation parameters: left-right lung ventilation ratio, ventilation center ratio; Optionally, the EIT data is judged to be up to standard when the inspiratory volume is greater than a preset threshold, the exhalation uniformity is better, the exhalation flow rate is greater than a preset threshold, the ventilation center is biased toward the ventral side, and the left and right lung ventilation is biased toward the left side.

4. The flight anti-overload HP action evaluation method according to claim 1, characterized in that: The method further includes a frontal lobe level oxygenated hemoglobin value, based on the EIT data, the plantar pressure data, the detection value of the electromyographic data, and the frontal lobe level oxygenated hemoglobin value, performing an HP action assessment to obtain an assessment result; Optionally, the evaluation is performed by comparing the detection value of the EIT data with a preset threshold, the detection value of the plantar pressure data with a preset threshold, the detection value of the electromyographic data with a preset threshold, and the oxygenated hemoglobin value at the frontal lobe level with a preset threshold. When the detection value of the EIT data, the plantar pressure data, the detection value of the electromyographic data, and the oxygenated hemoglobin value at the frontal lobe level are all greater than the preset threshold, it is determined that the standard is met; when any one of the detection value of the EIT data, the plantar pressure data, the electromyographic data, and the oxygenated hemoglobin value at the frontal lobe level is less than the preset threshold, it is determined that the standard is not met; Optionally, the non-compliance includes one or more of the following: EIT does not meet the standard, plantar pressure does not meet the standard, electromyographic data does not meet the standard, frontal level oxygenated hemoglobin value does not meet the standard, EIT and plantar pressure do not meet the standard, EIT and electromyographic data do not meet the standard, plantar pressure and electromyographic data do not meet the standard, EIT and frontal level oxygenated hemoglobin value does not meet the standard, plantar pressure and frontal level oxygenated hemoglobin value does not meet the standard, electromyographic data and frontal level oxygenated hemoglobin value do not meet the standard, EIT, plantar pressure and electromyographic data do not meet the standard, EIT, frontal level oxygenated hemoglobin value and electromyographic data do not meet the standard, EIT, plantar pressure and frontal level oxygenated hemoglobin value does not meet the standard, frontal level oxygenated hemoglobin value, plantar pressure and electromyographic data do not meet the standard, frontal level oxygenated hemoglobin value, EIT, plantar pressure and electromyographic data do not meet the standard.

5. The flight anti-overload HP action evaluation method according to claim 2, characterized in that: The evaluation is replaced by: performing HP action evaluation through an evaluation model to obtain an evaluation result; the evaluation model is obtained by training the correlation between the detection values ​​of EIT data, plantar pressure data, electromyography data and the high value of eye-level systolic blood pressure; Optionally, the training process of the evaluation model is: Obtaining EIT data, plantar pressure data, electromyographic data detection values, and eye-level systolic blood pressure high values ​​when the subject performs HP action; A linear regression model is constructed based on the detection values ​​of the EIT data, the plantar pressure data, the electromyography data and the high value of the eye-level systolic blood pressure to obtain an evaluation model; Optionally, in the linear regression model, the high value of eye level systolic pressure is the dependent variable, and the detection values ​​of EIT data, plantar pressure data, and electromyography data are independent variables; Optionally, the training process of the evaluation model is replaced by: Obtaining EIT data, plantar pressure data, electromyographic data detection values, and eye-level systolic blood pressure high values ​​when the subject performs HP action; Calculating the relationship between the detection values ​​of the EIT data, the plantar pressure data, and the electromyography data and the high value of the eye-level systolic pressure to obtain a mapping relationship; The mapping relationship is input into a neural network model for training to obtain an evaluation model.

6. The flight anti-overload HP action evaluation method according to claim 5, characterized in that: The evaluation model is replaced by: the evaluation result is obtained by performing HP action evaluation through the first evaluation model; the first evaluation model is obtained by training the correlation between EIT data, plantar pressure data, detection values ​​of electromyography data, frontal lobe level oxygenated hemoglobin value and eye level systolic high value; Optionally, the training process of the first evaluation model is: Obtaining the EIT data, plantar pressure data, electromyographic data detection values, frontal lobe level oxygenated hemoglobin value and eye level systolic blood pressure high value when the subject performs HP action; A linear regression model is constructed based on the EIT data, the plantar pressure data, the detection values ​​of the electromyographic data, the oxygenated hemoglobin value at the frontal lobe level and the high systolic blood pressure value at the eye level to obtain a first evaluation model; optionally, in the regression model of the first evaluation model, the high systolic blood pressure value at the eye level is a dependent variable, and the EIT data, the plantar pressure data, the detection values ​​of the electromyographic data, and the oxygenated hemoglobin value at the frontal lobe level are independent variables; Optionally, the training process of the first evaluation model is replaced by: Obtaining the EIT data, plantar pressure data, electromyographic data detection values, frontal lobe level oxygenated hemoglobin value, and eye level systolic high blood pressure value when the subject performs HP action; Calculating the relationship between the EIT data, the plantar pressure data, the detection value of the electromyography data, the oxygenated hemoglobin value at the frontal lobe level and the high value of the systolic pressure at the eye level to obtain a mapping relationship; Inputting the mapping relationship into a neural network model for training to obtain a first evaluation model; Optionally, the evaluation model is replaced by: performing HP action evaluation through a second evaluation model to obtain an evaluation result; the second evaluation model is obtained by training the correlation between the detection values ​​of EIT data, plantar pressure data, electromyography data and the oxygenated hemoglobin value at the frontal lobe level; Optionally, the training process of the second evaluation model is: Obtaining EIT data, plantar pressure data, electromyographic data detection values, and frontal lobe oxygenated hemoglobin values ​​when the subject performs HP action; A linear regression model is constructed based on the detection values ​​of the EIT data, the plantar pressure data, the electromyography data and the oxygenated hemoglobin value at the frontal lobe level to obtain a second evaluation model.

7. The flight anti-overload HP action evaluation method according to claim 1, characterized in that: The plantar pressure data is obtained by a plantar pressure device, which includes an insole, N pressure sensors, a signal transmission circuit, and an external circuit module; the N pressure sensors are fixed on the insole and connected to form a closed loop through the signal transmission circuit, the signal transmission circuit is connected to the external circuit module, and the external circuit module is used to process the signal data, and N is a natural number greater than or equal to 4; When the pressure sensor is subjected to pressure, a pressure signal is generated, and the pressure signal is transmitted to the external circuit module through the signal transmission circuit, and the external circuit module receives the pressure signal to obtain the plantar pressure data; Optionally, the number of the pressure sensors is 4-8; Optionally, the number of the pressure sensors is 4; Optionally, when the number of the pressure sensors is 4, the pressure sensors are respectively fixed on the thumb area, the first metatarsal area, the outer side area of ​​the arch, and the rear side area of ​​the heel; Optionally, when the number of the pressure sensors is 8, the pressure sensors are respectively fixed on the thumb area, the first metatarsal area, the lateral arch area, the posterior heel area, the second and third metatarsal areas, the fourth and fifth metatarsal areas, the medial heel area, and the lateral heel area.

8. A computer program product comprising a computer program or instructions, characterized in that: The computer program or instructions are executed by a processor to implement the flight anti-overload HP action evaluation method described in any one of claims 1-7.

9. A computer device comprising a memory, a processor and a computer program or instruction stored in the memory, characterized in that: The computer program or instructions are executed by a processor to implement the flight anti-overload HP action evaluation method described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: The computer program or instructions are executed by a processor to implement the flight anti-overload HP action evaluation method described in any one of claims 1-7.

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