A method and device for evaluating the recommended supply flow rate of hypoxic gas

Abstract

The present invention discloses a method and device for evaluating the recommended air supply flow rate of hypoxic gas, which is mainly used to supply hypoxic air of appropriate concentration for users to perform hypoxic training. The method includes: obtaining the heart rate and respiratory rate of the user to be evaluated; evaluating the user's exercise intensity based on the heart rate and respiratory rate; obtaining the heart rate variability and blood oxygen concentration of the user to be evaluated; evaluating the user's adaptability to the current altitude based on the heart rate variability and blood oxygen concentration; and recommending the hypoxic gas supply flow rate based on the comprehensive adaptability. The present invention generates hypoxic gas by recycling and reusing exhaled gas, thereby reducing costs and weight, and introduces an intelligent decision-making and recommendation system, which can recommend training modes based on the user's personal situation.

Description

Technical Field

[0001] The present invention relates to the technical field of hypoxia training, and in particular to a method and device for evaluating a recommended hypoxic gas supply flow rate. Background Art

[0002] Residents of plain areas often experience a series of altitude sicknesses upon first arriving in the plateau, with the incidence of altitude sickness reaching 53.62% within a week. Hypoxia pre-acclimatization training in plain areas is an effective means of preventing high-altitude illness. In recent years, hypoxia generators have begun to be used in hypoxia pre-acclimatization training. However, existing hypoxia generators are expensive, bulky, and cannot recommend training modes tailored to individual users. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and device for evaluating the recommended supply flow rate of hypoxic gas, which can reduce costs and weight by recycling and reusing exhaled gas to produce hypoxic gas, and introduce an intelligent decision-making and recommendation system to recommend training modes based on the user's personal situation.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] In a first aspect, the present invention discloses a method for evaluating a recommended hypoxic gas supply flow rate, comprising the following steps:

[0006] S1. Acquire the user's physiological parameters under different exercise conditions in plain areas, including heart rate HR, respiratory rate BR, heart rate variability HRv and blood oxygen saturation , according to the heart rate HR, respiratory rate BR training GWO-SVM classifier, the exercise intensity classification is obtained; according to the heart rate variability HRv and blood oxygen saturation Calculate reference values ​​for fitness levels under different exercise intensity categories and ;in, represents the mean minute heart rate variability under state i, Represents the mean minute blood oxygen drop in state i;

[0007] S2. Collect the physiological parameters of the user under different exercise states in the plateau area, input the heart rate and respiratory rate into the trained GWO-SVM classifier, and output the exercise intensity classification;

[0008] S3. Calculate the user's average heart rate variability within one minute based on heart rate variability and blood sample saturation. and blood oxygen concentration drop And compare it with the corresponding adaptation degree reference value, and the root difference value is used to judge the user's adaptation degree to the current exercise state;

[0009] S4. Recommend hypoxic gas supply flow rate based on the corresponding adaptation level.

[0010] Further solution: The training process of GWO-SVM classifier is:

[0011] Get training data , all HR and BR are scaled to the interval [0,1];

[0012] Select RBF kernel function ,in is the kernel parameter;

[0013] Set the initial penalty parameter of the SVR model and kernel parameters The initial value of

[0014] Initialize the wolf pack: Randomly initialize the positions of the wolf pack ;

[0015] Defining wolf pack hierarchy: Determine the alpha wolf based on the pack's position , suboptimal wolf , the third best wolf ;

[0016] Simulate hunting behavior:

[0017] Update formula: ,in Ci , α Indicates the The distance between a wolf and the leader.

[0018] Location Updates:

[0019] Update the parameters of the SVR model based on the location of the gray wolf and calculate the new potential solution;

[0020] The algorithm terminates when the maximum number of iterations is reached or the optimal solution reaches a preset threshold;

[0021] Use the optimized penalty parameter and kernel parameters Train the GWO-SVR model.

[0022] Further solution: Determine the user's adaptability to the current exercise state as follows:

[0023] Record heart rate variability HRv and blood oxygen saturation of multiple samples within 1 minute , calculate the mean heart rate variability and decreased blood oxygen saturation ;

[0024] According to the exercise intensity classification determined in step S2, find the corresponding and , calculate the fitness difference according to the following formula:

[0025] , where α is the coefficient;

[0026] like , then the adaptation level is recorded as Lv.1 fully adapted; if , then the adaptation level is recorded as Lv.2 basic adaptation; if , then the adaptation level is recorded as Lv.3, completely unadapted.

[0027] Further solution: Step S4 is specifically as follows:

[0028] Record continuously for at least ten minutes ,calculate The proportion of each state in

[0029] If the proportion of Lv.3 (completely unadapted) adaptation exceeds 50% of the total exercise time, it is recommended to increase the current air supply concentration, that is, lower the simulated altitude; if the proportion of Lv.2 (basic adaptation) adaptation and Lv.1 (complete adaptation) adaptation exceeds 50%, it is recommended to maintain the current air supply concentration, that is, maintain the simulated altitude; otherwise, it is recommended to reduce the current air supply concentration, that is, increase the simulated altitude.

[0030] Further solution: Exercise intensity classification includes resting, low-intensity exercise, moderate-intensity exercise, and high-intensity exercise.

[0031] Further plan: resting state is sitting or standing, METs of low-intensity exercise is less than 3.0, METs of moderate-intensity exercise is 3.0~7.0, and METs of high-intensity exercise is greater than 7.0.

[0032] Further solution: In the formula, the value of α is 0.5.

[0033] In a second aspect, the present invention discloses a computer-readable storage medium storing a plurality of acquisition and classification programs, which are used to be called by a processor and execute the method for evaluating the recommended hypoxic gas supply flow rate as described above.

[0034] In a third aspect, the present invention discloses a hypoxic gas supply device for the above-mentioned method of evaluating the recommended hypoxic gas supply flow rate, comprising a breathing mask connected to a two-way three-way valve, a first exhaust port of the two-way three-way valve being sequentially connected to an exhaled gas processing module, a first gas component analysis module, a mixed gas chamber, and a second gas component analysis module, and the gas component analysis module being connected to the second exhaust port of the two-way three-way valve;

[0035] The mixed gas chamber is further connected to a micro electric air pump, and the micro electric air pump is electrically connected to the control module, the intelligent recommendation module, and the physiological parameter monitoring module.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention generates hypoxic gas by recycling and reusing exhaled gas, thereby reducing costs and weight, and introduces an intelligent decision-making and recommendation system that can recommend training modes based on the user's personal situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of the device structure in the present invention;

[0039] Figure 2 This is a flow chart of determining the user's adaptability to the current motion state in the present invention;

[0040] Figure 3 This is the algorithm flow chart of the GWO-SVR classifier in the present invention;

[0041] Figure 4 Schematic diagram of the SVR algorithm in the present invention;

[0042] In the figure: 1- breathing mask, 2- pressure sensor, 3- two-way three-way valve, 4- exhaled gas processing module, 5- first gas component analysis module, 6- physiological parameter monitoring module, 7- intelligent recommendation module, 8- control module, 9- micro electric air pump, 10- mixing gas chamber, 11- second gas component analysis module. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] In the description of the present invention, it should be noted that the terms "upper", "lower", "left", "right", "inside", "outside", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, or are directions or positional relationships in which the inventive product is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0045] See also Figure 1-3In this embodiment, a method for evaluating a recommended hypoxic gas supply flow rate includes the following steps:

[0046] S0. Obtain physiological parameters of the user under evaluation while performing four different intensities of exercise in a plain area. Train an artificial neural network classifier based on heart rate and respiratory rate to classify exercise intensity. Calculate reference values ​​of fitness under different exercise states based on heart rate variability and blood oxygen saturation. Specifically, the following steps are included:

[0047] S01. Collect physiological parameters of the user to be evaluated while performing four different exercise intensities in a plain area, each lasting 10 minutes. The exercise states include resting (Con1. Resting), walking at 5 km / h (Con2. Low-intensity exercise), running at a slow speed of 7 km / h (Con3. Medium-intensity exercise), and running at a fast speed of 10 km / h (Con4. High-intensity exercise). Physiological parameters include heart rate (HR), respiratory rate (BR), heart rate variability (HRv), and blood oxygen saturation (SpO2).

[0048] S02. Pre-train the improved Grey Wolf algorithm support vector machine classification model (GWO-SVR) based on heart rate (HR) and respiratory rate (BR). The model categorizes exercise states into four types: Con1. Resting, Con2. Low-intensity exercise, Con3. Moderate-intensity exercise, and Con4. High-intensity exercise. This will be used for real-time exercise state classification during subsequent use. Specifically, the algorithm training process is as follows:

[0049] S021. Obtain training data , all HR and BR are scaled to the interval [0,1].

[0050] S022. Select RBF kernel function ,in is the kernel parameter.

[0051] S023. Set the initial penalty parameters of the SVR model and kernel parameters The initial value of .

[0052] S024. Initialize the wolf pack: Randomly initialize the position of the wolf pack .

[0053] S025. Define wolf pack hierarchy: Determine the leader based on the pack's position , suboptimal wolf , the third best wolf .

[0054] S026. Simulated hunting behavior:

[0055] Update formula: ,in Ci ,α Indicates the The distance between a wolf and the leader.

[0056] Location Updates:

[0057] S027. Update the parameters of the SVR model according to the location of the gray wolf and calculate the new potential solution.

[0058] S028. When the maximum number of iterations is reached or the optimal solution reaches a preset threshold, the algorithm terminates.

[0059] S029. Use optimized penalty parameters and kernel parameters Train the GWO-SVR model.

[0060] S03. Calculate the reference values ​​of fitness under different exercise states based on heart rate variability HRv and blood oxygen saturation SpO2: and .in represents the mean minute heart rate variability under state i, Represents the mean minute blood oxygen drop in state i. S1. Obtain the heart rate and respiratory rate of the user to be evaluated, and evaluate the user's exercise intensity based on the heart rate and respiratory rate. Specifically, the following steps are included:

[0061] S11. Preprocess the heart rate HR and respiratory rate BR and scale them to the range of [0,1].

[0062] S12. The pre-processed data is fed into a trained artificial neural network to output classification results, including Con1. Rest, Con2. Low-intensity exercise, Con3. Moderate-intensity exercise, and Con4. High-intensity exercise.

[0063] S13: Record the classification results and proceed to the next step of processing.

[0064] S2. Obtain the heart rate variability and blood oxygen concentration of the user to be evaluated, and evaluate the user's adaptation to the current altitude based on the heart rate variability and blood oxygen concentration. Specifically, the following steps are included:

[0065] S21, record the heart rate variability HRv and blood oxygen saturation of multiple samples within 1 minute , calculate the mean heart rate variability and decreased blood oxygen saturation .

[0066] S22, according to the motion state determined in step S1, find the corresponding and .according to Calculate the fitness difference, where is the coefficient, which is usually 0.5.

[0067] S23, according to Determine the user's adaptability: If , then the record status is Lv.1 fully adapted; if , then the record status is Lv.2 basic adaptation; if , the recorded status is Lv.3 completely unsuitable.

[0068] S3. Recommending the hypoxic gas supply flow rate based on the overall adaptation level during the training session. Specifically, the process includes the following steps:

[0069] S31. Record at least ten minutes ,calculate The proportion of each state in

[0070] S32. If the proportion of Lv3. Complete non-adaptation exceeds 50% of the total time, it is recommended to increase the current air supply concentration, that is, to lower the simulated altitude. If the proportion of Lv.2. Basic adaptation and Lv1. Complete adaptation exceeds 50%, it is recommended to maintain the current air supply concentration, that is, to maintain the simulated altitude. In other cases, it is recommended to reduce the current air supply concentration, that is, to increase the simulated altitude.

[0071] Support Vector Regression (SVR) is a regression algorithm based on support vector machines. Compared with other traditional regression methods, SVR has more advantages in training higher-dimensional and nonlinear data and can achieve higher regression accuracy.

[0072] After the collected data is preprocessed, an SVR training data set consisting of n groups of data will be obtained. ,in For the by The input is composed of dimensional input vector, Estimate the oxygen consumption for the corresponding one-dimensional output, so, for indivual dimensional input vector dimensional input array, and for Output energy consumption value -dimensional output vector.

[0073] like Figure 4 As shown in the figure, each circle represents a set of data in the data set. The purpose of SVR is to obtain the optimal hyperplane by solving , so that the distance between each data and the hyperplane is less than the set value ,in and are weight vectors and biases respectively. For energy consumption modeling, the essence is to find a mapping , making and As equal as possible, so for this regression model we have:

[0074]

[0075] In the formula is a nonlinear function, which can make Mapped to a higher dimensional feature space, and Into a linear relationship.

[0076] In traditional regression models, the model output is usually With the actual output The difference between the two is used to calculate the accuracy loss. In the SVR model, a special loss function is introduced. This loss function is used for is not sensitive to the error, that is, and The difference between When , the prediction is also considered correct and the loss is not included. This method can improve the robustness of the model. For this reason, the regression optimization problem can be written as:

[0077]

[0078] The optimization problem described in formula (2) is based on the premise that all data in the data set are in the The center is centered, and the width on both sides is However, in reality, some data are abnormal, and some points should be allowed to deviate to a certain extent. Therefore, the penalty parameter is introduced in formula (2) and slack variables and The slack variable refers to the positive or negative deviation outside the interval band, and the penalty parameter can control the weight of the deviation. At this point, the regression optimization problem can be written as:

[0079]

[0080] In order to link the constraints with the objective function, a non-negative La-grange multiplier is introduced , , , .right , , and By finding the partial derivatives and setting them equal to 0, it can be transformed into the dual problem of SVR. According to the KKT conditions, the solution of SVR is as follows:

[0081]

[0082] In formula (4) It is called the kernel function. The actual calculation may be very complicated, so in SVR, we can replace it by selecting a suitable kernel function. This paper adopts the RBF kernel function, and the expression is shown in formula (5):

[0083]

[0084] In formula (5) is the kernel parameter.

[0085] When determining the penalty parameter and kernel parameters After finding the optimal value of and output variables The SVR-based exercise oxygen consumption model can be obtained by training with a known exercise oxygen consumption data set. ,When applying this model to predict energy consumption data, it is only necessary to substitute the ,corresponding energy consumption related factors as input variables into the model to obtain ,the corresponding estimated oxygen consumption.

[0086] A device for evaluating a recommended hypoxic gas supply flow rate, comprising:

[0087] Respiratory mask 1;

[0088] a two-way three-way valve 3, a first end of which is in communication with the breathing mask 1;

[0089] The exhaled gas processing module 4 has an air inlet connected to the second end of the two-way three-way valve 3, and the third connection end of the two-way three-way valve 3 is connected to the exhaled gas processing module 4, the gas composition analysis module 5, and the mixed gas chamber 10 in sequence. It collects the exhaled gas in the breathing mask 1, processes and analyzes the exhaled gas, and then delivers it to the mixed gas chamber 10.

[0090] The micro electric air pump 9 is used to deliver air to the mixing chamber 10 ; the exhaled gas after treatment in the mixing chamber 10 is mixed with the air and outputs hypoxic gas to the breathing mask 1 .

[0091] The first gas composition analysis module 5 and the second gas composition analysis module 11 are used to monitor the oxygen content and carbon dioxide content of the flowing gas;

[0092] Physiological parameter monitoring module 6, used to detect the user's heart rate, heart rate variability, respiratory rate, and blood oxygen saturation;

[0093] The intelligent recommendation module 7 receives data from the physiological parameter monitoring module 6, evaluates the user's training effect, and recommends training air supply concentration to the user based on the evaluation effect.

[0094] And a control module 8 is used to control the working speed of the micro electric air pump 9.

[0095] Furthermore, it also includes a pressure sensor 2 for detecting the internal air pressure of the breathing mask 1. The pressure sensor 2 is electrically connected to the control single module 8. When the pressure sensor 2 detects a positive pressure, the control single module 8 controls the second end (first exhaust end) of the two-way three-way valve 3 to open and the third end (second exhaust end) to close; when the pressure sensor 2 detects a negative pressure, the control single module 8 controls the second end of the two-way three-way valve 3 to close and the third end to open.

[0096] Furthermore, the exhaled gas processing module 4 has a built-in filter composed of a CO2 adsorbent and a desiccant, and the filter is surrounded by a refrigerant for removing CO2 and moisture from the exhaled gas and reducing the temperature of the exhaled gas.

[0097] Furthermore, the second end of the two-way three-way valve 3 and the exhaled gas processing module 4 are connected by a breathing air hose and a quick connector. The quick connector is self-locking, ensuring the stability of the connection and the continuity of the gas flow during system operation, while facilitating quick disconnection when replacing the filter and refrigerant.

[0098] Furthermore, the first gas composition analysis module 5 and the second gas composition analysis module 11 both include an oxygen sensor and a carbon dioxide sensor for detecting the oxygen concentration and carbon dioxide concentration of the gas flowing therethrough and feeding back the results to the control module 8 .

[0099] Furthermore, the exhaled gas processing module 4 and the mixed gas chamber 10 are connected by a breathing air hose.

[0100] Although this specification is described according to implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0101] Therefore, the above description is only a preferred embodiment of the present application and is not intended to limit the scope of implementation of the present application; that is, all equivalent modifications made according to the scope of the claims of the present application are within the scope of protection of the claims of the present application.

Claims

1. A method for evaluating the recommended supply flow rate of hypoxic gas, characterized in that: The following steps are involved: S1. Acquire the user's physiological parameters under different exercise conditions in plain areas, including heart rate HR, respiratory rate BR, heart rate variability HRv and blood oxygen saturation , according to the heart rate HR, respiratory rate BR training GWO-SVM classifier, the exercise intensity classification is obtained; according to the heart rate variability HRv and blood oxygen saturation Calculate reference values ​​for fitness levels under different exercise intensity categories and ;in, represents the mean minute heart rate variability under state i, Represents the mean minute blood oxygen drop in state i; S2. Collect the physiological parameters of the user under different exercise states in the plateau area, input the heart rate and respiratory rate into the trained GWO-SVM classifier, and output the exercise intensity classification; S3. Calculate the user's average heart rate variability within one minute based on heart rate variability and blood sample saturation. and blood oxygen concentration drop And compare it with the corresponding adaptation degree reference value, and the root difference value is used to judge the user's adaptation degree to the current exercise state; S4. Recommend hypoxic gas supply flow rate based on the corresponding adaptation level.

2. The method for evaluating the recommended hypoxic gas supply flow rate according to claim 1, characterized in that: The training process of the GWO-SVM classifier is: Get training data , all HR and BR are scaled to the interval [0,1]; Select RBF kernel function ,in is the kernel parameter; Set the initial penalty parameter of the SVR model and kernel parameters The initial value of Initialize the wolf pack: Randomly initialize the positions of the wolf pack ; Defining wolf pack hierarchy: Determine the alpha wolf based on the pack's position , suboptimal wolf , the third best wolf ; Simulate hunting behavior: Update formula: ,in Ci , α Indicates the The distance between the wolf and the leader; Location Updates: Update the parameters of the SVR model based on the location of the gray wolf and calculate the new potential solution; The algorithm terminates when the maximum number of iterations is reached or the optimal solution reaches a preset threshold; Use the optimized penalty parameter and kernel parameters Train the GWO-SVR model.

3. The method for evaluating the recommended hypoxic gas supply flow rate according to claim 1, wherein: The degree of adaptation of the user to the current exercise state is determined as follows: Record heart rate variability HRv and blood oxygen saturation of multiple samples within 1 minute , calculate the mean heart rate variability and decreased blood oxygen saturation ; According to the exercise intensity classification determined in step S2, find the corresponding and , calculate the fitness difference according to the following formula: , where α is the coefficient; like , then the adaptation level is recorded as Lv.1 fully adapted; if , then the adaptation level is recorded as Lv.2 basic adaptation; if , then the adaptation level is recorded as Lv.3, completely unadapted.

4. The method for evaluating the recommended hypoxic gas supply flow rate according to claim 3, wherein: Step S4 is specifically as follows: Record continuously for at least ten minutes ,calculate The proportion of each state in If the proportion of Lv.3 (completely unadapted) adaptation exceeds 50% of the total exercise time, it is recommended to increase the current air supply concentration, that is, lower the simulated altitude; if the proportion of Lv.2 (basic adaptation) adaptation and Lv.1 (complete adaptation) adaptation exceeds 50%, it is recommended to maintain the current air supply concentration, that is, maintain the simulated altitude; otherwise, it is recommended to reduce the current air supply concentration, that is, increase the simulated altitude.

5. The method for evaluating the recommended hypoxic gas supply flow rate according to claim 1, wherein: Exercise intensity classification includes resting, low-intensity exercise, moderate-intensity exercise, and high-intensity exercise.

6. The method for evaluating the recommended hypoxic gas supply flow rate according to claim 1, wherein: Resting state refers to sitting or standing. The METs of low-intensity exercise are less than 3.0, the METs of moderate-intensity exercise are 3.0~7.0, and the METs of high-intensity exercise are greater than 7.

0.

7. The method for evaluating the recommended hypoxic gas supply flow rate according to claim 3, wherein: In the formula, the value of α is 0.

5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a plurality of acquisition and classification programs, which are used to be called by a processor and execute the method for evaluating the recommended hypoxic gas supply flow rate according to any one of claims 1 to 7.

9. A hypoxic gas supply device using the method for evaluating the recommended hypoxic gas supply flow rate according to any one of claims 1 to 7, characterized in that: The invention comprises a breathing mask (1), wherein the breathing mask (1) is connected to a two-way three-way valve (3), wherein a first exhaust port of the two-way three-way valve (3) is connected in sequence to an exhaled gas processing module (4), a first gas component analysis module (5), a mixed gas chamber (10), and a second gas component analysis module (11), and the gas component analysis module (11) is connected to the second exhaust port of the two-way three-way valve (3); The mixed gas chamber (10) is further connected to a micro electric air pump (9), and the micro electric air pump (9) is electrically connected to the control module (9), the intelligent recommendation module (7), and the physiological parameter monitoring module (6).

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