Data processing method and device for micro-pressure oxygen cabin control
By constructing a multi-dimensional feedback model and automatic control methods, the problem of low intelligence level in the regulation of micro-hyperbaric oxygen chambers was solved, realizing real-time optimization of in-chamber parameters and automatic adjustment of user physiological feedback, thus improving the intelligence level of micro-hyperbaric oxygen chambers.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JILIN DANA INTELLIGENT MANUFACTURING TECHNOLOGY CO LTD
- Filing Date
- 2026-02-28
- Publication Date
- 2026-06-12
AI Technical Summary
The existing micro-pressure oxygen chambers have a low level of intelligence in their control, lack real-time control and reliance on professional personnel, resulting in complicated operation.
By acquiring external environmental data and internal demand data, a multi-dimensional feedback model is constructed to achieve automatic control of the internal parameters of the micro-pressure oxygen chamber. This includes the identification and optimization of basic characteristic data, and real-time parameter adjustment by matching the control mode with a preset database.
It improves the intelligence level of the micro-hyperbaric oxygen chamber, enabling real-time monitoring and automatic adjustment of the chamber environment and user physiological feedback, and simplifies the operation process.
Smart Images
Figure CN122194777A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of medical devices, and more specifically, to a data processing method and apparatus for controlling a microbarotropic oxygen chamber. Background Technology
[0002] A microbaric oxygen chamber is a device that creates a micro-high-pressure environment to simulate and optimize oxygen intake. It can be applied in fields such as economics, medicine, health management, beauty, sports, military, scientific research, aviation, and home use. For example, in the hypoxic natural environment of high altitudes, people who rapidly ascend to high altitudes are prone to acute altitude sickness; a microbaric oxygen chamber can simulate this hypoxic environment for acclimatization training. However, current technologies require extensive parameter adjustments and numerous steps, necessitating professional personnel for parameter tuning. Furthermore, the lack of real-time control during the simulation process results in a low level of intelligence in the microbaric oxygen chamber's control.
[0003] Therefore, this application is proposed to address the technical problem of the low level of intelligence in the control of micro-pressure oxygen chambers in the prior art. Summary of the Invention
[0004] The main objective of this application is to provide a data processing method and apparatus for controlling a micro-pressure oxygen chamber, so as to solve the technical problem of low intelligence level of existing micro-pressure oxygen chamber control, and achieve the technical effect of improving the intelligence level of the micro-pressure oxygen chamber.
[0005] To achieve the above objectives, a first aspect of this application proposes a data processing method for controlling a micro-pressure oxygen chamber, comprising: Acquire the first data to be processed, wherein the first data to be processed includes external environment data and internal demand data; The internal parameters of the micro-pressure oxygen chamber are controlled based on the external environment data and the internal demand data to obtain first control data, wherein the first control data is data used to represent the initial internal control parameters of the micro-pressure oxygen chamber; Acquire second data to be processed, wherein the second data to be processed is cabin feedback data used to represent the execution of the first control data for a first preset time period; The second data to be processed is processed by constructing a multi-dimensional feedback model to obtain a multi-dimensional feedback model, wherein the multi-dimensional feedback model is a model used to represent the correlation between the cabin control parameters and the cabin feedback data; The cabin parameters within the second preset time period are controlled according to the multidimensional feedback model to obtain second control data, wherein the second control data is used to represent the control parameters of the micro-pressure oxygen chamber within the second preset time period.
[0006] Furthermore, the internal parameters of the micro-pressure oxygen chamber are controlled based on the external environment data and the internal demand data to obtain the first control data, including: The cabin demand data is identified and processed to obtain basic feature data and usage feature data, wherein the basic feature data is data used to represent the user's basic physiological characteristics, and the usage feature data is data used to represent the user's usage demand characteristics; The control mode corresponding to the usage feature data is matched in the preset oxygen chamber control database to obtain mode control data, wherein the mode control data is data used to represent the control parameters corresponding to the current working mode of the oxygen chamber; The mode control data is subjected to parameter optimization processing based on the basic feature data to obtain the first control data.
[0007] Further, the mode control data is subjected to parameter optimization processing based on the basic feature data to obtain the first control data, which includes: Matching the reference environment parameters corresponding to the basic feature data yields first reference environment parameter data, wherein the first reference environment parameter data is data used to represent the external reference environment parameters; The first reference environment parameter data is subjected to parameter optimization processing based on the preset reference environment parameter data to obtain cabin parameter optimization feature data. The mode control data is optimized by performing parameter optimization processing based on the cabin parameter optimization feature data to obtain the first control data.
[0008] Furthermore, a multi-dimensional feedback model is constructed on the second data to be processed, resulting in a multi-dimensional feedback model including: The second data to be processed is identified and processed, including the second in-cabin feedback data and the second physiological feedback data. The first control data and the second in-cabin feedback data to be processed are processed by a first feedback model to obtain a first feedback model, wherein the first feedback model is a model used to represent the relationship between in-cabin environmental feedback and in-cabin control parameters; The first control data and the second physiological feedback data to be processed are subjected to a second feedback model construction process to obtain a second feedback model, wherein the second feedback model is a model used to represent the relationship between user physiological feedback and cabin control parameters; The multidimensional feedback model is determined based on the first feedback model and the second feedback model.
[0009] Furthermore, the cabin parameters within the second preset time period are controlled according to the multidimensional feedback model to obtain the second control data, including: Acquire third data to be processed, wherein the third data to be processed is cabin feedback data used to represent the second preset time period; The third data to be processed is subjected to feature extraction processing of feedback change to obtain feedback change feature data, wherein the feedback change feature data is feature data used to represent the change trend of the in-cabin feedback data; The feedback change characteristic data is subjected to feedback adjustment processing based on the multidimensional feedback model to obtain the second control data.
[0010] Furthermore, the cabin parameters within the second preset time period are controlled according to the multidimensional feedback model to obtain the second control data, including: Obtain the third piece of data to be processed; The third data to be processed is identified and processed, including the third in-cabin feedback data and the third physiological feedback data. The third in-cabin feedback data to be processed is subjected to feature extraction processing to obtain in-cabin feedback change feature data, wherein the in-cabin feedback change feature data is feature data used to represent the change trend of in-cabin environmental feedback data; Feature extraction processing is performed on the third physiological feedback data to be processed to obtain physiological feedback change feature data, wherein the physiological feedback change feature data is feature data used to represent the change trend of physiological feedback data; The cabin feedback change characteristic data and the physiological feedback change characteristic data are subjected to feedback adjustment processing based on the multidimensional feedback model to obtain the second control data.
[0011] According to a second aspect of this application, a data processing device for controlling a micro-pressure oxygen chamber is provided, comprising: The first data acquisition module is used to acquire the first data to be processed, wherein the first data to be processed includes external environment data and internal demand data; The first control module is used to control the internal parameters of the micro-pressure oxygen chamber according to the external environment data and the internal demand data to obtain first control data, wherein the first control data is data used to represent the initial internal control parameters of the micro-pressure oxygen chamber; The second data acquisition module is used to acquire the second data to be processed, wherein the second data to be processed is cabin feedback data used to represent the execution of the first control data for a first preset time period; The feedback model construction module is used to construct a multi-dimensional feedback model for the second data to be processed, thereby obtaining a multi-dimensional feedback model, wherein the multi-dimensional feedback model is a model used to represent the correlation between the cabin control parameters and the cabin feedback data. The feedback control module is used to control the cabin parameters within a second preset time period according to the multidimensional feedback model to obtain second control data, wherein the second control data is data used to represent the control parameters of the micro-pressure oxygen chamber within a second preset time period.
[0012] Furthermore, the first control module includes: The identification module is used to identify and process the cabin demand data to obtain basic feature data and usage feature data, wherein the basic feature data is data used to represent the user's basic physiological characteristics, and the usage feature data is data used to represent the user's usage demand characteristics. The pattern matching module is used to match the control mode corresponding to the usage feature data in a preset oxygen chamber control database to obtain pattern control data, wherein the pattern control data is data used to represent the control parameters corresponding to the current working mode of the oxygen chamber; The mode parameter optimization module is used to perform parameter optimization processing on the mode control data based on the basic feature data to obtain the first control data.
[0013] According to a third aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing the computer to perform the above-described data processing method for controlling a micro-barrier oxygen chamber.
[0014] According to a fourth aspect of this application, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the at least one processor to perform the data processing method for controlling a micro-barrier oxygen chamber as described above.
[0015] The technical solutions provided by the embodiments of this application may include the following beneficial effects: In this application, the process involves: acquiring first data to be processed, including external environment data and internal demand data; controlling the internal parameters of the micro-hyperbaric oxygen chamber based on the external environment data and the internal demand data to obtain first control data, which represents the initial internal control parameters of the micro-hyperbaric oxygen chamber; acquiring second data to be processed, representing internal feedback data for a first preset time period after executing the first control data; constructing a multi-dimensional feedback model on the second data to be processed to obtain a multi-dimensional feedback model, which represents the correlation between the internal control parameters and the internal feedback data; and controlling the internal parameters within a second preset time period based on the multi-dimensional feedback model to obtain second control data, which represents the control parameters of the micro-hyperbaric oxygen chamber within a second preset time period. By constructing a feedback model using in-cabin feedback data within the first time period, the in-cabin control parameters are adjusted in the subsequent second time period based on the constructed feedback model, thereby achieving automatic control of the micro-pressure oxygen chamber and improving the intelligence level of micro-pressure oxygen chamber control. Attached Figure Description
[0016] The accompanying drawings, which form part of this application, are used to provide a further understanding of the application and to make other features, objects, and advantages of the application more apparent. The illustrative embodiments and descriptions of this application are used to explain the application and do not constitute an undue limitation of the application. In the drawings: Figure 1 A flowchart of a data processing method for controlling a micro-pressure oxygen chamber provided in this application; Figure 2 A flowchart of a data processing method for controlling a micro-pressure oxygen chamber provided in this application; Figure 3 A schematic diagram of a data processing device for controlling a micro-pressure oxygen chamber provided in this application; Figure 4 A schematic diagram of another data processing device for controlling a micro-pressure oxygen chamber provided in this application. Detailed Implementation
[0017] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.
[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0019] In this application, the terms "upper," "lower," "left," "right," "front," "rear," "top," "bottom," "inner," "outer," "middle," "vertical," "horizontal," "lateral," and "longitudinal" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are primarily for the purpose of better describing this application and its embodiments, and are not intended to limit the indicated device, element, or component to having a specific orientation, or to be constructed and operated in a specific orientation.
[0020] Furthermore, in addition to indicating location or positional relationship, some of the aforementioned terms may also have other meanings. For example, the term "above" may also be used in some cases to indicate a certain dependency or connection relationship. Those skilled in the art can understand the specific meaning of these terms in this application based on the specific circumstances.
[0021] Furthermore, the terms "installation," "setup," "equipped with," "connection," "linked," and "socketing" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or an internal connection between two devices, components, or parts. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.
[0022] In some optional embodiments of this application, a data processing method for controlling a micro-pressure oxygen chamber is provided. Figure 1 A flowchart of a data processing method for controlling a micro-barrier oxygen chamber provided in this application is shown below. Figure 1 As shown, the method includes the following steps: S101: Obtain the first data to be processed; The first set of data to be processed includes external environmental data and internal demand data. External environmental data refers to the environmental conditions outside the micro-hyperbaric oxygen chamber. Internal demand data includes user information and reasons for using the chamber. User information includes basic physiological information such as age and gender. Reasons for use indicate the purpose of using the chamber, including, for example: for individuals experiencing high-intensity mental exertion or those in a sub-healthy state, to alleviate fatigue and lack of concentration caused by fast-paced lifestyles and work pressures; for athletes, to facilitate rapid physical recovery after high-intensity training or competition and accelerate the excretion of metabolic products such as lactic acid; for the elderly, to improve hypoxia symptoms that may occur due to weakened cardiopulmonary function and reduced activity; for specific disease adjuvant therapy, serving as an auxiliary means to increase tissue oxygen supply and promote functional recovery in the rehabilitation of cardiovascular, respiratory, and neurological diseases; for altitude acclimatization and relief, helping people traveling to high-altitude areas alleviate altitude sickness symptoms or conduct acclimatization training; and for beauty and skin care, improving skin quality by improving oxygen supply to skin cells and promoting metabolism and repair.
[0023] S102: Control the internal parameters of the micro-pressure oxygen chamber based on the external environment data and the internal demand data to obtain the first control data; The first control data is used to represent the initial in-chamber control parameters of the micro-pressure oxygen chamber; In some optional embodiments of this application, a data processing method for controlling a micro-barrier oxygen chamber is proposed to determine the chamber control parameters based on external environmental data and internal demand data. Figure 2 A flowchart of a data processing method for controlling a micro-barrier oxygen chamber provided in this application is shown below. Figure 2 As shown, the method includes the following steps: S201: Identify and process cabin demand data to obtain basic characteristic data and usage characteristic data; Basic feature data refers to data representing the user's basic physiological characteristics, while usage feature data refers to data representing the user's usage needs. The basic feature data includes basic physiological features such as user age and user health characteristics. User health characteristics include: health status characteristics, sub-health status characteristics, and disease status characteristics.
[0024] S202: Match the control mode corresponding to the characteristic data in the preset oxygen chamber control database to obtain the mode control data; The mode control data is the data used to represent the control parameters corresponding to the current working mode of the oxygen chamber; for example, if the user's usage characteristic data is for altitude acclimatization and relief, the control mode corresponding to the altitude acclimatization and relief usage characteristic is matched, and the oxygen chamber's internal parameter control data corresponding to the mode is generated.
[0025] S203: Perform parameter optimization processing on the mode control data based on the basic feature data to obtain the first control data.
[0026] In some optional embodiments of this application, a data processing method for controlling a micro-barrier oxygen chamber is proposed, comprising: Matching the reference environment parameters corresponding to the basic feature data yields first reference environment parameter data, wherein the first reference environment parameter data represents the external reference environment parameters and the parameters of the adaptive environment corresponding to the basic feature data; performing parameter optimization processing on the first reference environment parameter data based on preset reference environment parameter data yields internal parameter optimized feature data; performing parameter optimization processing on the mode control data based on the internal parameter optimized feature data yields first control data.
[0027] In this embodiment, the mode control data is the oxygen chamber parameter control data corresponding to the standard mode. Taking the above-mentioned high-altitude adaptation and relief mode as an example, the mode control data corresponding to the high-altitude adaptation and relief mode is the chamber parameters under preset age characteristics and preset health conditions, such as the chamber parameters under the preset health conditions of 20-40 years old. The basic characteristic data of the current user is 50 years old and disease status characteristics. The parameters of the reference external adaptation environment corresponding to the user's basic characteristic data are matched to obtain the first reference environment parameter data. The first reference environment parameter data is optimized based on the parameters of the external adaptation environment corresponding to the above-mentioned preset age characteristics and preset health conditions to obtain the optimized chamber parameter characteristics. Then, the parameters of the high-altitude adaptation and relief mode of the user with basic characteristic data of 50 years old and disease status are optimized according to the optimized chamber parameter characteristics data to obtain the first control data corresponding to the user.
[0028] S103: Obtain the second data to be processed; The second data to be processed is the cabin feedback data used to represent the first preset time period of the execution of the first control data; the first preset time period is the adaptation period, the cabin feedback data within the first preset time period is collected, the corresponding feedback monitoring model is constructed based on the feedback data, and then the real-time cabin parameter control is realized for the second preset time period after the adaptation period.
[0029] S104: Construct a multi-dimensional feedback model for the second data to be processed to obtain a multi-dimensional feedback model; The multidimensional feedback model is a model used to represent the correlation between in-cabin control parameters and in-cabin feedback data; In some optional embodiments of this application, a data processing method for micro-barrier oxygen chamber control is proposed to realize the construction of a multi-dimensional feedback model, including: The second data to be processed is identified and processed, including the second in-cabin feedback data and the second physiological feedback data. The in-cabin feedback data includes environmental feedback data such as in-cabin oxygen concentration, carbon dioxide concentration, air pressure, and temperature. The physiological feedback data includes user blood oxygen saturation, heart rate, and body movement data. The physiological feedback data is collected through a contactable sensor. The first control data and the second in-cabin feedback data to be processed are processed to construct a first feedback model, which is used to represent the relationship between the in-cabin environmental feedback and the in-cabin control parameters. The first control data and the second physiological feedback data to be processed are processed to construct a second feedback model, which is used to represent the relationship between the user's physiological feedback and the cabin control parameters. A multidimensional feedback model is determined based on the first feedback model and the second feedback model.
[0030] S105: Control the cabin parameters within the second preset time period according to the multidimensional feedback model to obtain the second control data.
[0031] The second control data is used to represent the control parameters within the second preset time period of the micro-pressure oxygen chamber.
[0032] In some optional embodiments of this application, a data processing method for controlling a micro-barrier oxygen chamber is proposed, comprising: Acquire third data to be processed, which represents in-cabin feedback data within a second preset time period. This third data is feedback data for shorter time periods within the second preset time period, such as in-cabin feedback data every 30 seconds, including user physiological feedback data and in-cabin environmental feedback data. Perform feature extraction processing on the third data to be processed to obtain feedback change feature data, which represents the trend of in-cabin feedback data changes. Perform feedback adjustment processing based on a multi-dimensional feedback model on the feedback change feature data to obtain second control data.
[0033] In some optional embodiments of this application, a data processing method for controlling a micro-pressure oxygen chamber is proposed to achieve parameter control of the micro-pressure oxygen chamber within a second preset period, including: Acquire the third data to be processed; identify and process the third data to be processed, including the third in-cabin feedback data and the third physiological feedback data; perform feature extraction processing on the third in-cabin feedback data to obtain in-cabin feedback change feature data, wherein the in-cabin feedback change feature data is feature data used to represent the changing trend of in-cabin environmental feedback data; perform feature extraction processing on the third physiological feedback data to obtain physiological feedback change feature data, wherein the physiological feedback change feature data is feature data used to represent the changing trend of physiological feedback data; perform feedback regulation processing based on a multi-dimensional feedback model on the in-cabin feedback change feature data and the physiological feedback change feature data to obtain the second control data.
[0034] In some alternative embodiments of this application, a data processing device for controlling a micro-pressure oxygen chamber is proposed. Figure 3 A schematic diagram of a data processing device for controlling a micro-pressure oxygen chamber provided in this application is shown below. Figure 3 As shown, it includes: The first data acquisition module 31 is used to acquire the first data to be processed, wherein the first data to be processed includes external environment data and internal demand data. The first control module 32 is used to control the internal parameters of the micro-pressure oxygen chamber according to the external environment data and the internal demand data to obtain first control data, wherein the first control data is data used to represent the initial internal control parameters of the micro-pressure oxygen chamber; The second data acquisition module 33 is used to acquire the second data to be processed, wherein the second data to be processed is cabin feedback data used to represent the execution of the first control data for a first preset time period; The feedback model construction module 34 is used to construct a multi-dimensional feedback model for the second data to be processed, thereby obtaining a multi-dimensional feedback model, wherein the multi-dimensional feedback model is a model used to represent the correlation between the cabin control parameters and the cabin feedback data. The feedback control module 35 is used to control the cabin parameters within a second preset time period according to the multidimensional feedback model to obtain second control data, wherein the second control data is data used to represent the control parameters of the micro-pressure oxygen chamber within a second preset time period.
[0035] In some alternative embodiments of this application, a data processing device for controlling a micro-pressure oxygen chamber is proposed. Figure 4 A schematic diagram of another data processing device for controlling a micro-pressure oxygen chamber provided in this application is shown below. Figure 4 As shown, it includes: The identification module 41 is used to identify and process the cabin demand data to obtain basic feature data and usage feature data, wherein the basic feature data is data used to represent the user's basic physiological characteristics, and the usage feature data is data used to represent the user's usage demand characteristics. The pattern matching module 42 is used to match the control mode corresponding to the usage feature data in the preset oxygen chamber control database to obtain the mode control data, wherein the mode control data is data used to represent the control parameters corresponding to the current working mode of the oxygen chamber; The mode parameter optimization module 43 is used to perform parameter optimization processing on the mode control data based on the basic feature data to obtain the first control data.
[0036] The specific methods of execution of each unit in the above embodiments have been described in detail in the embodiments of the method, and will not be elaborated here.
[0037] In summary, in this application, by acquiring first data to be processed, which includes external environment data and internal demand data; controlling the internal parameters of the micro-hyperbaric oxygen chamber based on the external environment data and the internal demand data to obtain first control data, wherein the first control data is data representing the initial internal control parameters of the micro-hyperbaric oxygen chamber; acquiring second data to be processed, wherein the second data to be processed is internal feedback data representing the execution of the first control data for a first preset time period; constructing a multi-dimensional feedback model on the second data to be processed to obtain a multi-dimensional feedback model, wherein the multi-dimensional feedback model is a model representing the correlation between the internal control parameters and the internal feedback data; controlling the internal parameters within a second preset time period based on the multi-dimensional feedback model to obtain second control data, wherein the second control data is data representing the control parameters of the micro-hyperbaric oxygen chamber within a second preset time period. By constructing a feedback model using in-cabin feedback data within the first time period, the in-cabin control parameters are adjusted in the subsequent second time period based on the constructed feedback model, thereby achieving automatic control of the micro-pressure oxygen chamber and improving the intelligence level of micro-pressure oxygen chamber control.
[0038] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0039] Obviously, those skilled in the art should understand that the various units or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device, or fabricating them separately as individual integrated circuit modules, or fabricating multiple modules or steps into a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.
[0040] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A data processing method for controlling a micro-pressure oxygen chamber, characterized in that, include: Acquire the first data to be processed, wherein the first data to be processed includes external environment data and internal demand data; The internal parameters of the micro-pressure oxygen chamber are controlled based on the external environment data and the internal demand data to obtain first control data, wherein the first control data is data used to represent the initial internal control parameters of the micro-pressure oxygen chamber; Acquire second data to be processed, wherein the second data to be processed is cabin feedback data used to represent the execution of the first control data for a first preset time period; The second data to be processed is processed by constructing a multi-dimensional feedback model to obtain a multi-dimensional feedback model, wherein the multi-dimensional feedback model is a model used to represent the correlation between the cabin control parameters and the cabin feedback data; The cabin parameters within the second preset time period are controlled according to the multidimensional feedback model to obtain second control data, wherein the second control data is used to represent the control parameters of the micro-pressure oxygen chamber within the second preset time period.
2. The data processing method according to claim 1, characterized in that, The internal parameters of the micro-pressure oxygen chamber are controlled based on the external environment data and the internal demand data to obtain the first control data, including: The cabin demand data is identified and processed to obtain basic feature data and usage feature data, wherein the basic feature data is data used to represent the user's basic physiological characteristics, and the usage feature data is data used to represent the user's usage demand characteristics; The control mode corresponding to the usage feature data is matched in the preset oxygen chamber control database to obtain mode control data, wherein the mode control data is data used to represent the control parameters corresponding to the current working mode of the oxygen chamber; The mode control data is subjected to parameter optimization processing based on the basic feature data to obtain the first control data.
3. The data processing method according to claim 2, characterized in that, The first control data is obtained by performing parameter optimization processing on the mode control data based on the basic feature data, which includes: Matching the reference environment parameters corresponding to the basic feature data yields first reference environment parameter data, wherein the first reference environment parameter data is data used to represent the external reference environment parameters; The first reference environment parameter data is subjected to parameter optimization processing based on the preset reference environment parameter data to obtain cabin parameter optimization feature data. The mode control data is optimized by performing parameter optimization processing based on the cabin parameter optimization feature data to obtain the first control data.
4. The data processing method according to claim 1, characterized in that, The second data to be processed is subjected to a multi-dimensional feedback model, resulting in a multi-dimensional feedback model including: The second data to be processed is identified and processed, including the second in-cabin feedback data and the second physiological feedback data. The first control data and the second in-cabin feedback data to be processed are processed by a first feedback model to obtain a first feedback model, wherein the first feedback model is a model used to represent the relationship between in-cabin environmental feedback and in-cabin control parameters; The first control data and the second physiological feedback data to be processed are subjected to a second feedback model construction process to obtain a second feedback model, wherein the second feedback model is a model used to represent the relationship between user physiological feedback and cabin control parameters; The multidimensional feedback model is determined based on the first feedback model and the second feedback model.
5. The data processing method according to claim 1, characterized in that, The cabin parameters within the second preset time period are controlled according to the multidimensional feedback model, resulting in the following second control data: Acquire third data to be processed, wherein the third data to be processed is cabin feedback data used to represent the second preset time period; The third data to be processed is subjected to feature extraction processing of feedback change to obtain feedback change feature data, wherein the feedback change feature data is feature data used to represent the change trend of the in-cabin feedback data; The feedback change characteristic data is subjected to feedback adjustment processing based on the multidimensional feedback model to obtain the second control data.
6. The data processing method according to claim 1, characterized in that, The cabin parameters within the second preset time period are controlled according to the multidimensional feedback model, resulting in the following second control data: Obtain the third piece of data to be processed; The third data to be processed is identified and processed, including the third in-cabin feedback data and the third physiological feedback data. The third in-cabin feedback data to be processed is subjected to feature extraction processing to obtain in-cabin feedback change feature data, wherein the in-cabin feedback change feature data is feature data used to represent the change trend of in-cabin environmental feedback data; Feature extraction processing is performed on the third physiological feedback data to be processed to obtain physiological feedback change feature data, wherein the physiological feedback change feature data is feature data used to represent the change trend of physiological feedback data; The cabin feedback change characteristic data and the physiological feedback change characteristic data are subjected to feedback adjustment processing based on the multidimensional feedback model to obtain the second control data.
7. A data processing device for controlling a micro-pressure oxygen chamber, characterized in that, include: The first data acquisition module is used to acquire the first data to be processed, wherein the first data to be processed includes external environment data and internal demand data; The first control module is used to control the internal parameters of the micro-pressure oxygen chamber according to the external environment data and the internal demand data to obtain first control data, wherein the first control data is data used to represent the initial internal control parameters of the micro-pressure oxygen chamber; The second data acquisition module is used to acquire the second data to be processed, wherein the second data to be processed is cabin feedback data used to represent the execution of the first control data for a first preset time period; The feedback model construction module is used to construct a multi-dimensional feedback model for the second data to be processed, thereby obtaining a multi-dimensional feedback model, wherein the multi-dimensional feedback model is a model used to represent the correlation between the cabin control parameters and the cabin feedback data. The feedback control module is used to control the cabin parameters within a second preset time period according to the multidimensional feedback model to obtain second control data, wherein the second control data is data used to represent the control parameters of the micro-pressure oxygen chamber within a second preset time period.
8. The data processing apparatus according to claim 7, characterized in that, The first control module includes: The identification module is used to identify and process the cabin demand data to obtain basic feature data and usage feature data, wherein the basic feature data is data used to represent the user's basic physiological characteristics, and the usage feature data is data used to represent the user's usage demand characteristics. The pattern matching module is used to match the control mode corresponding to the usage feature data in a preset oxygen chamber control database to obtain pattern control data, wherein the pattern control data is data used to represent the control parameters corresponding to the current working mode of the oxygen chamber; The mode parameter optimization module is used to perform parameter optimization processing on the mode control data based on the basic feature data to obtain the first control data.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the data processing method for controlling a micro-barrier oxygen chamber as described in any one of claims 1-6.
10. An electronic device, characterized in that, include: At least one processor; The at least one processor is also connected in communication with a memory, wherein the memory stores a computer program that can be executed by the at least one processor to cause the at least one processor to perform the data processing method for controlling a micro-barrier oxygen chamber as described in any one of claims 1-6.