A single-person multifunctional hyperbaric oxygen chamber

Abstract

The present invention belongs to the technical field of hyperbaric oxygen chambers, and is particularly concerned with a single-person multifunctional hyperbaric oxygen chamber, comprising a cover body and an oxygen chamber main body, wherein a thin film curtain is installed on the cover body, and an arc-shaped main airway is connected to the lower side of the thin film curtain, and the arc-shaped main airway is connected to a lower curled bronchus, and the position of the thin film curtain in the vertical and horizontal directions can be adjusted, and an exhalation port is also installed on the cover body, and the exhalation port is connected to an exhaust pipe, and the middle part of the exhaust pipe is connected to the arc-shaped main airway of the thin film curtain, and the end of the exhaust pipe is connected to the exhaust port, and a patient vital sign monitoring module, an oxygen chamber environment monitoring module, a data transmission module, and an oxygen chamber control module are installed in the oxygen chamber main body, and the oxygen chamber environment monitoring module completes the monitoring of environmental parameters such as oxygen content and carbon dioxide content in the oxygen chamber. The present application can avoid the influence of the patient's breathing state on the oxygen control accuracy and response speed of the control system of the single-person hyperbaric oxygen chamber.

Description

Technical Field

[0001] The invention relates to the technical field of hyperbaric oxygen chambers, and in particular to a single-person multifunctional hyperbaric oxygen chamber. Background Art

[0002] Hyperbaric oxygen therapy technology can be used to treat carbon monoxide poisoning, decompression sickness, hypoxic diseases, cerebrovascular diseases, etc. Since the patient's breathing state has a certain influence on the oxygen control accuracy and response speed of the control system of the single-person hyperbaric oxygen chamber, the present invention proposes a single-person multifunctional hyperbaric oxygen chamber, which reduces the oxygen supply range and adjusts the supply module to improve the oxygen accuracy and response speed without affecting the patient's use experience. Summary of the invention

[0003] The present invention provides a single-person multifunctional hyperbaric oxygen chamber, which solves the disadvantage in the prior art that the patient's breathing state has a certain influence on the oxygen control accuracy and response speed of the control system of the single-person hyperbaric oxygen chamber.

[0004] The present invention provides the following technical solutions:

[0005] A single-person multifunctional hyperbaric oxygen chamber comprises a cover body and an oxygen chamber main body, wherein a thin film curtain is installed on the cover body, an arc-shaped main airway is connected to the lower side of the thin film curtain, and the arc-shaped main airway is connected to a lower curled bronchus, and the position of the thin film curtain in the vertical and horizontal directions can be adjusted, and an exhalation port is also installed on the cover body, and the exhalation port is connected to an exhaust pipe, the middle part of the exhaust pipe is connected to the arc-shaped main airway of the thin film curtain, and the end of the exhaust pipe is connected to the exhaust port, and a patient vital sign monitoring module, an oxygen chamber environment monitoring module, a data transmission module, and an oxygen chamber control module are installed in the oxygen chamber main body, and the oxygen chamber environment monitoring module completes the monitoring of environmental parameters such as oxygen content and carbon dioxide content in the oxygen chamber.

[0006] As a further improvement of the above scheme,

[0007] Preferably, the oxygen chamber environment monitoring module is implemented in the following steps:

[0008] Step 1: Evenly place oxygen concentration sensors and carbon dioxide concentration sensors in the oxygen chamber according to the size of the chamber. According to the concentration detection situation, use Gaussian function to sparsely reconstruct the concentration field to accurately obtain the changes in oxygen and carbon dioxide concentrations at various positions in the oxygen chamber.

[0009] Step 2: Calculate the patient's respiratory rate based on changes in oxygen and carbon dioxide concentrations, and characterize the patient's inhalation and exhalation strengths based on the oxygen change rate and carbon dioxide change rate, respectively.

[0010] Preferably, the patient vital sign monitoring module completes the monitoring of the patient's vital sign parameters such as the patient's respiratory rate, blood pressure, pulse rate, etc., and tracks the patient's physical condition in real time. The implementation steps are as follows:

[0011] Step 3: Real-time measurement of the patient's vital signs such as blood pressure and pulse rate. The respiratory rate is measured using an indirect method, which is given by step 2.

[0012] Step 4: To ensure that the patient's vital sign parameters obtained in step 3 and the environmental parameters obtained in steps 1 and 2 have the same time phase, the frequency of the output data of various sensors is adjusted to be consistent to achieve the coordination of various monitoring data.

[0013] Preferably, the data transmission module completes the effective transmission of the above-mentioned various parameters, and the implementation steps are as follows:

[0014] Step 5: Send the above monitoring data to the data processing module in the oxygen chamber control module in real time.

[0015] Preferably, the oxygen chamber control module includes a data processing module and a prediction control module based on a radial basis function (RBF) neural network, which is used to complete the coordinated real-time processing of the patient's vital sign parameter data and environmental parameters, and provide an oxygen chamber control strategy, that is, the oxygen delivery amount, delivery speed and concentration of the delivered oxygen; the implementation steps are as follows:

[0016] Step 6: Preprocess the data obtained from step 5, including data normalization, outlier cleaning, and data smoothing to ensure the validity and reliability of the data source;

[0017] Step 7: The core of the predictive control model based on radial basis neural network is to adjust the control strategy in real time according to the prediction results to adapt to the dynamic changes of the system;

[0018] Step 8: The patient's vital signs parameters and oxygen chamber environmental parameters are used as model inputs, and the oxygen delivery volume, delivery speed and delivered oxygen concentration are used as model outputs to train the above-mentioned radial basis function neural network-based predictive control model.

[0019] Preferably, a predictive control model based on radial basis function neural network optimized by genetic algorithm is adopted in the model training process. The specific steps are as follows:

[0020] Step 9: Apply genetic algorithm to optimize the weights and thresholds of the neural network, and give the individual with the best fitness value, which is used as the initial weights and initial thresholds of the RBF neural network to minimize the amount of data required for model training;

[0021] Step 10: Design a nonlinear optimizer to convert the output of the neural network into the control quantity of the system. The optimization method is the rolling optimization method based on the dynamic matrix.

[0022] Step 11: Input the patient's vital signs parameters, oxygen chamber environmental parameters and other input quantities obtained by real-time monitoring into the neural network predictive control model, and finally obtain the optimal oxygen delivery volume, delivery speed and concentration of the delivered oxygen at this time.

[0023] It is to be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention.

[0024] In the present invention, the thin film partition curtain is adjusted so that it can enclose a small oxygen supply range with the cover body, thereby improving the oxygen supply accuracy without affecting the patient's use experience;

[0025] When the patient inhales, the chest is slightly raised, and the chest is close to the partition curtain to block the gas on the other side, preventing the gas on the other side from changing the oxygen concentration on the breathing side. When the patient exhales, the chest will move down slightly as the gas is exhaled, and the exhalation port will draw away the exhaled carbon dioxide while inflating the arc-shaped main trachea and bronchi. After the bronchi are inflated, they stretch to fill the gap left by the downward movement of the chest, thereby playing a blocking role without direct contact with the patient.

[0026] And through the patient vital signs monitoring module, oxygen chamber environment monitoring module, data transmission module, and oxygen chamber control module, the oxygen chamber environment monitoring module completes the monitoring of environmental parameters such as oxygen content and carbon dioxide content in the oxygen chamber, and can provide the optimal control strategy for the oxygen chamber according to the patient's real-time physical condition, which can effectively improve the working efficiency of the compressed oxygen chamber and improve the treatment effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic structural diagram of a single-person multifunctional hyperbaric oxygen chamber provided in an embodiment of the present invention;

[0028] Figure 2 A schematic diagram of a curtain structure of a single-person multifunctional hyperbaric oxygen chamber provided in an embodiment of the present invention;

[0029] Figure 3 A schematic diagram of the bronchial structure of a single-person multifunctional hyperbaric oxygen chamber provided in an embodiment of the present invention;

[0030] Figure 4 A schematic diagram of predictive control based on RBF neural network for a single-person multifunctional hyperbaric oxygen chamber provided in an embodiment of the present invention;

[0031] Figure 5 The present invention provides a flowchart of a single-person multifunctional hyperbaric oxygen chamber.

[0032] Reference numerals:

[0033] 1. Oxygen chamber body; 2. Cover; 3. Exhalation port; 4. Partition curtain; 5. Arc-shaped main trachea; 6. Bronchi. DETAILED DESCRIPTION

[0034] The embodiments of the present invention are described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0035] In the description of the embodiments of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms, "connection" and "installation" should be understood in a broad sense. For example, "connection" can be a detachable connection or a non-detachable connection; it can be a direct connection or an indirect connection through an intermediate medium. In addition, "connection" can be a direct connection or an indirect connection through an intermediate medium. Among them, "fixed" means that they are connected to each other and the relative position relationship after connection remains unchanged. The directional terms mentioned in the embodiments of the present invention, such as "inside", "outside", "top", "bottom", etc., are only reference to the directions of the accompanying drawings. Therefore, the directional terms used are for better and clearer explanation and understanding of the embodiments of the present invention, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present invention.

[0036] Example

[0037] Reference Figure 1-Figure 5 A single-person multifunctional hyperbaric oxygen chamber comprises a cover body 2 and an oxygen chamber body 1, wherein a thin film curtain 4 is mounted on the cover body 2, wherein the thin film curtain 4 is mounted on a sliding bracket, wherein the sliding bracket is mounted on a Z-axis slide rail, wherein the Z-axis slide rail is mounted on a slider, and wherein the slider is mounted on an X-axis slide rail, so that the thin film curtain 4 can be adjusted along the length direction and the vertical direction of the oxygen chamber, wherein an arc-shaped main airway 5 is connected to the lower side of the thin film curtain 4, wherein the arc-shaped main airway 5 is connected to a lower curled bronchus 6. The position of the film-like partition curtain 4 in the vertical and horizontal directions can be adjusted. An exhalation port 3 is also installed on the cover body 2. The exhalation port 3 is connected to an exhaust pipe. The middle part of the exhaust pipe is connected to the arc-shaped main air pipe 5 of the film-like partition curtain 4. The end of the exhaust pipe is connected to the exhaust port. A patient vital sign monitoring module, an oxygen chamber environment monitoring module, a data transmission module, and an oxygen chamber control module are installed in the oxygen chamber body 1. The oxygen chamber environment monitoring module completes the monitoring of environmental parameters such as oxygen content and carbon dioxide content in the oxygen chamber.

[0038] The implementation steps of the oxygen chamber environment monitoring module are as follows:

[0039] Step 1: Evenly place oxygen concentration sensors and carbon dioxide concentration sensors in the oxygen cabin according to the size of the cabin. According to the concentration detection situation, use Gaussian function to sparsely reconstruct the concentration field to accurately obtain the changes in oxygen and carbon dioxide concentrations at various positions in the oxygen cabin. Specifically:

[0040] Assume that the oxygen content monitored at the sensor position (i, j, k) is The carbon dioxide content is , using the Gaussian function The concentration field is sparsely reconstructed, (x, y, z) is the three-dimensional space coordinate system, (x0, y0, z0) is the center of the oxygen chamber, that is, the patient's breathing position. A is the amplitude of the Gaussian function, To expand the concentration range in each coordinate system, the oxygen content monitored at each sensor position (i, j, k) is The carbon dioxide content is Substitute into the Gaussian distribution function and fit it using the least squares method to get the fitting coefficient , we can accurately obtain the changes in oxygen and carbon dioxide concentrations at various positions in the oxygen chamber. Let f1 be the oxygen concentration field and f2 be the carbon dioxide concentration field.

[0041] Step 2: Calculate the patient's respiratory rate based on the changes in oxygen and carbon dioxide concentrations, and characterize the patient's inhalation and exhalation strengths based on the oxygen change rate and carbon dioxide change rate, respectively. As the characteristic value of oxygen concentration in the patient's breathing area a3, As the characteristic value of the carbon dioxide concentration in the patient's breathing area a3 (a3 is 1 / 5 of the volume V in the oxygen chamber). The frequency is taken as the patient's respiratory frequency F, and the inspiratory force Q and expiratory force R of the patient are represented according to the oxygen change rate and carbon dioxide change rate. , is the correlation coefficient. Since the inhalation force and exhalation force are linearly positively correlated with the change rate of oxygen and carbon dioxide respectively, The correct answer is 1.

[0042] The patient vital signs monitoring module monitors the patient's respiratory rate, blood pressure, pulse rate and other vital signs parameters, and tracks the patient's physical condition in real time. The implementation steps are as follows:

[0043] Step 3: Real-time measurement of the patient's vital signs such as blood pressure and pulse rate. The respiratory rate is measured using an indirect method, which is given by step 2.

[0044] Step 4: To ensure that the patient's vital sign parameters obtained in step 3 and the environmental parameters obtained in steps 1 and 2 have the same time phase, the frequency of the output data of various sensors is adjusted to be consistent to achieve the coordination of various monitoring data.

[0045] The data transmission module completes the effective transmission of the above-mentioned parameters, and the implementation steps are as follows:

[0046] Step 5: Send the above monitoring data to the data processing module in the oxygen chamber control module in real time.

[0047] The oxygen chamber control module includes a data processing module and a prediction control module based on a radial basis function (RBF) neural network, which is used to complete the coordinated real-time processing of patient vital sign parameter data and environmental parameters, and provide an oxygen chamber control strategy, that is, the oxygen delivery amount, delivery speed and concentration of the delivered oxygen; the implementation steps are as follows:

[0048] Step 6: Preprocess the data obtained from step 5, including data normalization, outlier cleaning, and data smoothing to ensure the validity and reliability of the data source;

[0049] Step 7: The core of the predictive control model based on radial basis neural network is to adjust the control strategy in real time according to the prediction results to adapt to the dynamic changes of the system. The model diagram is shown in Figure 4 ;

[0050] Step 8: The patient's vital signs parameters and oxygen chamber environment parameters are used as model inputs, and the oxygen delivery volume, delivery speed and delivered oxygen concentration are used as model outputs. Specifically, the model input The oxygen delivery amount I, delivery speed V and delivered oxygen concentration C are used as model outputs to train the above-mentioned radial basis function neural network-based predictive control model.

[0051] In the model training process, a predictive control model based on radial basis neural network optimized by genetic algorithm is adopted. The specific steps are as follows:

[0052] Step 9: Apply genetic algorithm to optimize the weights and thresholds of the neural network, give the optimal fitness value individual, and use it as the initial weight and initial threshold of the RBF neural network to minimize the amount of data required for model training. Use genetic algorithm to optimize the hidden node weight update process of the neural network to prevent the traditional RBF training process from falling into the local optimum and minimize the amount of data required for model training. Specific methods: ① Binary encoding of RBF weights; ② Initialization; ③ Fitness function ; ④ Genetic operation: crossover (probability is Pa), mutation (probability is Pb), the above probabilities need to be adjusted according to the specific training samples, generally the crossover probability is between 0.9-0.97, and the mutation probability is between 0.1-0.001; ⑤ Repeat steps ③~④ until the fitness function is minimized, and the optimal weight encoding is obtained at this time, thereby achieving the purpose of optimizing the RBF network training process;

[0053] Step 10: Design a nonlinear optimizer to convert the output of the neural network into the control quantity of the system. The optimization method is the rolling optimization method based on the dynamic matrix.

[0054] Step 11: Input the patient's vital signs parameters, oxygen chamber environmental parameters and other input quantities obtained by real-time monitoring into the neural network predictive control model, and finally obtain the optimal oxygen delivery volume, delivery speed and concentration of the delivered oxygen at this time.

[0055] The working principle of this application is:

[0056] By arranging a partition curtain 4 with adjustable position in the cover body 2, the space inside the cover body 2 is divided, and the range of oxygen concentration that needs to be regulated is reduced, thereby improving the accuracy of oxygen supply;

[0057] The partition curtain 4 is close to the chest but not in direct contact with the chest. When the patient inhales, the chest moves upward to further reduce the distance between the partition curtain 4 and the chest, preventing the gas on the other side of the partition curtain 4 from entering the oxygen supply space. When the patient exhales, the gas discharged from the exhalation port 3 passes through the bronchus 6 to stretch the bronchus 6, thereby reducing the distance between the partition curtain 4 and the chest. By setting the partition curtain 4, the oxygen supply space is maintained from exchanging too much air with the other side without direct contact with the patient, thereby improving the accuracy of oxygen supply.

[0058] The oxygen chamber environment monitoring module completes the monitoring of environmental parameters such as oxygen content and carbon dioxide content in the oxygen chamber, which is used for feedback of oxygen control on the one hand, and can indirectly reflect the patient's respiratory condition again on the other hand; the patient vital sign monitoring module completes the monitoring of patient vital sign parameters such as patient respiratory rate, blood pressure, pulse rate, etc., and tracks the patient's physical condition in real time; the data transmission module completes the effective transmission of the above-mentioned various parameters, and adopts a predictive control model based on radial basis function neural network optimized by genetic algorithm, which can give the optimal control strategy of the oxygen chamber according to the patient's real-time physical condition, which can effectively improve the working efficiency of the pressure oxygen chamber and improve the treatment effect. Combined with the setting of the partition curtain 4, the oxygen supply space is reduced in hardware, so that the influence of the patient's respiratory state is more concentrated, thereby improving the degree of influence of each module on the patient's respiratory state more accurately, and through precise adjustment of each module, the ventilation between the oxygen supply space and the other side is reduced, thereby reducing the fluctuation range of oxygen concentration in the oxygen supply space, and at the same time, the control module of the hyperbaric oxygen chamber performs dynamic adaptive control based on the patient's respiratory state, further improving the oxygen supply accuracy and improving the treatment effect.

[0059] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention; the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A single-person multifunctional hyperbaric oxygen chamber, characterized in that: It includes a cover body and an oxygen chamber body, wherein a thin film curtain is installed on the cover body, an arc-shaped main airway is connected to the lower side of the thin film curtain, and the arc-shaped main airway is connected to a lower curled bronchus, and the position of the thin film curtain in the vertical and horizontal directions can be adjusted, and an exhalation port is also installed on the cover body, and the exhalation port is connected to an exhaust pipe, and the middle part of the exhaust pipe is connected to the arc-shaped main airway of the thin film curtain, and the end of the exhaust pipe is connected to the exhaust port, and a patient vital sign monitoring module, an oxygen chamber environment monitoring module, a data transmission module, and an oxygen chamber control module are installed in the oxygen chamber body, and the oxygen chamber environment monitoring module completes the monitoring of the oxygen content and the carbon dioxide content in the oxygen chamber; the implementation steps of the oxygen chamber environment monitoring module are as follows, Step 1: Evenly place oxygen concentration sensors and carbon dioxide concentration sensors in the oxygen chamber according to the size of the chamber. According to the concentration detection situation, use Gaussian function to sparsely reconstruct the concentration field to accurately obtain the changes in oxygen and carbon dioxide concentrations at various positions in the oxygen chamber. Step 2: Calculate the patient's respiratory rate according to the changes in oxygen and carbon dioxide concentrations, and characterize the patient's inhalation and exhalation strengths according to the oxygen change rate and carbon dioxide change rate, respectively; the patient's vital signs monitoring module completes the monitoring of the patient's respiratory rate, blood pressure, and pulse rate, and tracks the patient's physical condition in real time. The implementation steps are as follows: Step 3: Measure the patient's blood pressure and pulse rate in real time. The respiratory rate is measured using an indirect method, which is given by step 2. Step 4: To ensure that the patient's vital sign parameters obtained in step 3 and the environmental parameters obtained in steps 1 and 2 have the same time phase, the frequency of the output data of various sensors is adjusted to be consistent to achieve the coordination of various monitoring data; the data transmission module completes the effective transmission of the above-mentioned various parameters, and the implementation steps are as follows: Step 5: Send the above monitoring data to the data processing module in the oxygen chamber control module in real time; the oxygen chamber control module includes a data processing module and a prediction control module based on a radial basis function (RBF) neural network, which is used to complete the coordinated real-time processing of the patient's vital sign parameter data and environmental parameters, and provide an oxygen chamber control strategy, that is, the oxygen delivery volume, delivery speed and concentration of the delivered oxygen; the implementation steps are as follows: Step 6: Preprocess the data obtained from step 5, including data normalization, outlier cleaning, and data smoothing to ensure the validity and reliability of the data source; Step 7: The core of the predictive control model based on radial basis neural network is to adjust the control strategy in real time according to the prediction results to adapt to the dynamic changes of the system; Step 8: The patient's vital signs parameters and oxygen chamber environmental parameters are used as model inputs, and the oxygen delivery volume, delivery speed and delivered oxygen concentration are used as model outputs to train the above-mentioned radial basis function neural network-based predictive control model.

2. The single-person multifunctional hyperbaric oxygen chamber according to claim 1, characterized in that: In the model training process, a predictive control model based on radial basis neural network optimized by genetic algorithm is adopted. The specific steps are as follows: Step 9: Apply genetic algorithm to optimize the weights and thresholds of the neural network, and give the individual with the best fitness value, which is used as the initial weights and initial thresholds of the RBF neural network to minimize the amount of data required for model training; Step 10: Design a nonlinear optimizer to convert the output of the neural network into the control quantity of the system. The optimization method is the rolling optimization method based on the dynamic matrix. Step 11: Input the patient's vital sign parameters and oxygen chamber environmental parameter input obtained by real-time monitoring into the neural network predictive control model, and finally obtain the optimal oxygen delivery volume, delivery speed and concentration of the delivered oxygen at this time.

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