Data processing method and system for air bag in hyperbaric oxygen chamber

Abstract

The invention discloses a data processing method and system for an air bag in a hyperbaric oxygen chamber, and relates to the technical field of intelligent adjustment of an air bag system.The data processing method comprises the steps that air bag micro-pressure data and user breathing data are collected in real time, micro-pressure fluctuation characteristics are obtained based on the air bag micro-pressure data, and a micro-pressure recognition result is calculated through the characteristics; the state of the air bag is evaluated through a micro-pressure recognition result, a basic pressure adjustment curve is generated, breathing rhythm characteristics are extracted based on user breathing data collected in real time, and the pressure of the air bag is dynamically adjusted in combination with the basic pressure adjustment curve and the breathing rhythm characteristics of the user so that the pressure of the air bag can be matched with the breathing rhythm of the user. The problems of response delay, mismatching, incapability of adapting to complex breathing modes and the like in a traditional pressure adjusting system are effectively solved, dynamic and accurate management of the pressure of the air bag is realized, and the comfort level and experience of a user are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent adjustment of gasbag system, and in particular to a data processing method and system for gasbag in hyperbaric oxygen chamber. BACKGROUND

[0002] With the wide application of hyperbaric oxygen chamber, researches on gasbag adjustment have gradually developed from early simple mechanical constant pressure adjustment to dynamic adjustment based on electronic control system. In modern technology, most gasbag adjustment systems in hyperbaric oxygen chamber have been able to collect pressure data in the gasbag in real time through sensors and adjust the gasbag pressure in combination with control algorithms. However, the traditional adjustment method mainly relies on the pre-set fixed adjustment mode, lacks adaptive matching with the actual breathing rhythm of the user, and is easy to cause the gasbag pressure to fail to respond to the specific physiological state of the patient in real time. In addition, in order to further improve the working efficiency, comfort and stability of the gasbag, in recent years, some intelligent gasbag systems have begun to introduce breathing sensors to try to collect the breathing rhythm characteristics of the user and combine them with the gasbag state to achieve more accurate adjustment.

[0003] Although the existing gasbag adjustment technology has made certain breakthroughs, especially in the accuracy and response speed of gasbag pressure adjustment, there are still many limitations, which restrict its effect in actual use. First, in the existing gasbag adjustment technology, the gasbag pressure adjustment model often only relies on the pressure data in the gasbag for regulation and control, without fully considering the real-time changes of the user's breathing rhythm, which leads to the disconnection between the gasbag and the user's physiological signals. When the user's breathing changes suddenly, the system cannot quickly capture this change and make pressure adjustment, which may cause discomfort to the user and reduce the adjustment effect. Second, although some technical solutions try to introduce methods for extracting user breathing characteristics, data collection and processing are mostly in the primary stage, and the accuracy and real-time performance of the breathing characteristics are poor, which makes it difficult to support efficient adjustment in complex scenarios. Third, most current gasbag systems in hyperbaric oxygen chambers lack long-term recording and optimization mechanism for system operation data, and cannot further improve the rationality of the adjustment strategy and the system performance through long-term data analysis. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a data processing method and system for gasbag in hyperbaric oxygen chamber, which solves the technical problems of asynchronization between gasbag pressure and user breathing rhythm and lack of real-time pressure adjustment accuracy.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a data processing method for an air bag in a hyperbaric oxygen chamber, comprising: collecting air bag micro-pressure data and user breathing data in real time, obtaining micro-pressure fluctuation characteristics based on the air bag micro-pressure data, and calculating micro-pressure recognition results through the characteristics;

[0008] evaluating the air bag state through the micro-pressure recognition results, generating a basic pressure adjustment curve, extracting breathing rhythm characteristics based on the user breathing data collected in real time, combining the basic pressure adjustment curve and the user breathing rhythm characteristics, and dynamically adjusting the air bag pressure to match the air bag pressure with the user breathing rhythm;

[0009] uploading the operation data and air bag adjustment records to a database to form a closed-loop feedback.

[0010] As a preferred scheme of the data processing method for the air bag in the hyperbaric oxygen chamber, the real-time collection of air bag micro-pressure data involves arranging a high-precision micro-pressure sensor in the air bag to obtain time series data of the original pressure in the air bag , wherein refers to containing t original pressure values;

[0011] The real-time collection of user breathing data involves wearing a chest strap type breathing sensor for the user to obtain real-time volume changes of the user's thorax, and calculating the electrical signal intensity of the user's thoracic movement based on the volume changes of the thorax ;

[0012] Based on the electrical signal intensity of the user's thoracic movement, time series data of the thoracic movement electrical signal value is obtained , wherein refers to containing t electrical signal values.

[0013] As a preferred scheme of the data processing method for the air bag in the hyperbaric oxygen chamber, the obtaining of micro-pressure fluctuation characteristics based on the air bag micro-pressure data and the calculation of micro-pressure recognition results through the characteristics involve normalizing the original pressure data in the air bag to obtain a standardized air bag pressure time series ;

[0014] Based on the standardized air bag pressure time series, pressure time series characteristics are calculated, including pressure fluctuation amplitude mean , pressure fluctuation variance , pressure main frequency , and pressure signal energy ;

[0015] The obtained pressure time series characteristics are used to calculate the micro-pressure recognition results Q.

[0016] As a preferred embodiment of the data processing method for airbags in a hyperbaric oxygen chamber according to the present invention, the step of evaluating the airbag status through micro-pressure identification results and generating a baseline pressure adjustment curve based on the evaluation value of the airbag pressure status and a standardized airbag pressure time series is described. The pressure assessment value at time j was calculated. ;

[0017] Based on the pressure assessment values ​​at a single moment, a time series of airbag status assessment values ​​is obtained by integrating them. ,in, This refers to a set of t stress assessment values;

[0018] Set stress assessment threshold and ,and ;

[0019] If the evaluation value is the evaluation value at time j in the time series... If the airbag pressure is insufficient, it needs to be inflated.

[0020] like If the airbag pressure is moderate, the current state will be maintained.

[0021] like If the airbag pressure is too high, it needs to be depressurized.

[0022] Record the time series of adjusted pressure ,in, This refers to a time series containing t pressure values, which is used as the base pressure adjustment curve.

[0023] As a preferred embodiment of the data processing method for the airbag in a hyperbaric oxygen chamber described in this invention, the step of extracting respiratory rhythm features based on real-time acquired user respiratory data refers to normalizing the time series data of the electrical signal values ​​of the chest wall movement during user breathing to obtain a standardized time series of electrical signal values. ;

[0024] Determining a user's respiratory phase based on a time series of standardized electrical signal values, and specifying the standardized electrical signal value at each moment... Differentiate, if This indicates that the ribcage is expanding and the user is in the inhalation phase at time t. This indicates that the rib cage is contracting, and the user is in the exhalation phase at time t. This indicates that the user's chest cavity is in a static state at time t;

[0025] Based on the standardized time series of electrical signal values, a local peak detection method is used to extract local maxima and minima.

[0026] The time interval between two adjacent maximum values is a breathing cycle Z;

[0027] The breathing frequency H of the user is calculated based on the breathing cycle;

[0028] The breathing amplitude is calculated based on the obtained maximum and minimum value set .

[0029] As a preferred scheme of the data processing method for the gas bag in the hyperbaric oxygen chamber, wherein: the dynamic adjustment of the gas bag pressure to match the user's breathing rhythm based on the combination of the basic pressure adjustment curve and the user's breathing rhythm characteristics refers to calculating the amplitude of the gas bag adjustment micro-pressure based on the user's breathing amplitude ;

[0030] The adjustment frequency C of the gas bag is calculated based on the breathing frequency of the user;

[0031] The final pressure adjustment value is calculated according to the basic pressure adjustment curve, the amplitude of the gas bag adjustment micro-pressure, and the adjustment frequency of the gas bag .

[0032] As a preferred scheme of the data processing method for the gas bag in the hyperbaric oxygen chamber, wherein: the uploading of the running data and the gas bag adjustment record to the database to form a closed loop feedback refers to uploading the user's breathing characteristics and the record of the gas bag pressure adjustment to the database to form a closed loop feedback.

[0033] In a second aspect, the present application provides a data processing system for a gas bag in a hyperbaric oxygen chamber, comprising,

[0034] The data acquisition module is used to acquire the gas bag micro-pressure data and the user's breathing data in real time;

[0035] The micro-pressure processing module is used to extract the micro-pressure characteristics from the micro-pressure data and calculate the micro-pressure recognition result through the characteristics;

[0036] The basic pressure curve generation module is used to generate a basic pressure adjustment curve based on the micro-pressure recognition result;

[0037] The breathing feature extraction module is used to extract the breathing rhythm characteristics from the user's breathing data;

[0038] The gas bag pressure adjustment module is used to dynamically adjust the gas bag pressure to match the user's breathing rhythm;

[0039] The closed loop feedback module is used to upload the gas bag data, the user data, and the adjustment process data to the database to realize a closed loop feedback.

[0040] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program, when executed by the processor, implements any step of the data processing method for the gas bag in the hyperbaric oxygen chamber according to the first aspect of the present application.

[0041] In a fourth aspect, the present application provides a computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements any step of the data processing method for the gas bag in the hyperbaric oxygen chamber according to the first aspect of the present application.

[0042] The present application has the following beneficial effects: by collecting the gas bag micro-pressure data and the user breathing data in real time, the dual dynamic information of the equipment running state and the user physiological behavior is obtained, which provides a data basis for subsequent analysis and processing, and ensures the accuracy and real-time performance of the adjustment logic; by extracting the micro-pressure characteristics from the gas bag micro-pressure data and calculating the micro-pressure identification results through the characteristics, the system realizes comprehensive analysis of the gas bag running state, can identify abnormalities in time and generate a basic pressure adjustment curve, and provides protection for the reliability and safety of the gas bag system; based on the user breathing data, the breathing rhythm characteristics are extracted to analyze the actual breathing behavior mode of the user, so that the system can capture the dynamic needs of the user in real time; under the driving of the basic pressure adjustment curve and the user breathing characteristics, the system adjusts the gas bag pressure through dynamic adjustment, realizes real-time synchronous matching of the gas bag and the user breathing, and provides a more comfortable and safe breathing experience for the user; by uploading the system running data and the adjustment records to the database and constructing a closed-loop feedback mechanism, the system can continuously optimize according to the historical data, and improve the long-term operation efficiency and adaptability. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort.

[0044] Figure 1 The flowchart of the data processing method for the gas bag in the hyperbaric oxygen chamber in embodiment 1.

[0045] Figure 2 The structural diagram of the data processing system for the gas bag in the hyperbaric oxygen chamber in embodiment 1. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0047] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0049] Example 1, referring to Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a data processing method for an airbag in a hyperbaric oxygen chamber, comprising the following steps:

[0050] S1: Real-time acquisition of airbag micropressure data and user breathing data, acquisition of micropressure fluctuation characteristics based on airbag micropressure data, and calculation of micropressure identification results based on characteristics.

[0051] Specifically, real-time acquisition of airbag micro-pressure data refers to the placement of high-precision micro-pressure sensors inside the airbag to obtain time-series data of the original pressure inside the airbag. :

[0052] ,

[0053] in, This refers to a set of t pressure values;

[0054] The real-time acquisition of user breathing data refers to the use of a chest strap-type breathing sensor to obtain real-time changes in the user's chest volume, and to calculate the electrical signal intensity of the user's chest movement based on these changes in chest volume. :

[0055] ,

[0056] in, Let t be the change in chest cavity volume during the user's breathing. The set sensor sensitivity;

[0057] The final time series data of the thoracic motion electrical signal values ​​were obtained. :

[0058] ,

[0059] in, It refers to containing t electrical signal values.

[0060] By arranging a high-precision micro-pressure sensor inside the air bag, real-time acquisition of time series data of the original pressure inside the air bag is realized, and by using a chest belt type respiration sensor to acquire real-time volume changes of the user's thorax and convert them into electrical signal intensity, multi-dimensional and dynamic respiration state monitoring is realized. The combination of the two data acquisition methods not only can comprehensively capture the user's respiration depth, frequency and thoracic movement characteristics, but also can verify each other through data to improve the accuracy and reliability of the monitoring results.

[0061] Further, based on the micro-pressure data of the air bag, the micro-pressure fluctuation characteristics are obtained, and by characteristic calculation, the micro-pressure recognition result is obtained. The original pressure data in the air bag is normalized to obtain a standardized air bag pressure time series

[0062] Based on the standardized air bag pressure time series, the mean value of the pressure fluctuation amplitude is calculated

[0063] ,

[0064] wherein, is the pressure value at the jth moment after standardization, is the mean value of the pressure data in the time period, and the specific expression is:

[0065] ,

[0066] Based on the standardized air bag pressure time series, the pressure fluctuation variance is calculated

[0067] ,

[0068] Based on the standardized air bag pressure time series, the pressure main frequency is calculated

[0069] ,

[0070] wherein, is the frequency in the frequency domain, is the frequency domain converted from the standardized air bag pressure time series , and the specific expression is:

[0071] ,

[0072] wherein, is the conversion of the standardized air bag pressure time series into the frequency domain;

[0073] Based on the standardized air bag pressure time series, the pressure signal energy is calculated :​​​​

[0074] ,

[0075] The micro-pressure recognition result Q is calculated using the obtained pressure time sequence features:

[0076] ,

[0077] wherein Q is an evaluation value of the airbag pressure state, is a feature weight determined through data fitting.

[0078] Through the standardization processing of the original pressure data in the airbag, the influence of data noise and inconsistency is reduced, and high-quality and uniform scale pressure time sequence data is obtained; by further extracting the multi-dimensional characteristics of pressure fluctuation, the micro-pressure fluctuation characteristics can be fully and accurately represented; finally, by using the multi-dimensional feature data to calculate the micro-pressure recognition result Q, the problem of high difficulty in micro-pressure state classification and evaluation is effectively solved, and the accuracy and robustness of the algorithm are significantly improved.

[0079] S2: Evaluate the airbag state through the micro-pressure recognition result, and generate a basic pressure adjustment curve; extract the breathing rhythm features based on the real-time collected user breathing data, and combine the basic pressure adjustment curve and the user breathing rhythm features to dynamically adjust the airbag pressure to match the airbag pressure with the user breathing rhythm.

[0080] Specifically, the airbag state is evaluated through the micro-pressure recognition result, and a basic pressure adjustment curve is generated, which is based on the evaluation value of the airbag pressure state and the standardized airbag pressure time sequence The pressure evaluation value at time j is calculated as :

[0081] ,

[0082] wherein is the minimum value in the standardized airbag pressure time sequence, is the maximum value in the standardized airbag pressure time sequence;

[0083] Based on the pressure evaluation value at a single time, the time sequence q of the airbag state evaluation value is integrated:

[0084] ,

[0085] wherein indicates that t evaluation values are included;

[0086] The pressure evaluation threshold and are set, and ;

[0087] If the evaluation value is the evaluation value at time j in the time series... If the airbag pressure is insufficient, it needs to be increased. This increase in pressure is expressed as:

[0088] ,

[0089] in, To adjust the pressure value at time j, The boost pressure is specifically expressed as:

[0090] ,

[0091] in, The target pressure value is set based on the equipment's performance and specifications;

[0092] like If the airbag pressure is moderate, the current state will be maintained.

[0093] like If the airbag pressure is too high, it needs to be depressurized. The depressurization is expressed as:

[0094] ,

[0095] in, The pressure reduction is specifically expressed as:

[0096] ,

[0097] Record the time series of adjusted pressure This time series was used as the baseline pressure adjustment curve.

[0098] By setting reasonable evaluation thresholds and implementing state classification and adjustment strategies, the airbag is ensured to remain within a suitable pressure range. By recording the adjusted time series to generate a baseline pressure adjustment curve, not only is dynamic tracking of pressure adjustment achieved, but reliable data support is also provided for subsequent state optimization and data analysis.

[0099] Furthermore, extracting respiratory rhythm features based on real-time user respiratory data involves normalizing the time-series data of the electrical signal values ​​of chest wall movement during user respiration to obtain a standardized time-series of electrical signal values. ;

[0100] Determining a user's respiratory phase based on a time series of standardized electrical signal values, and specifying the standardized electrical signal value at each moment... Differentiate, if This indicates that the ribcage is expanding, and the user is in the inhalation phase at time j. This indicates that the chest cavity is contracting, and the user is in the exhalation phase at time j. , which indicates that the user's thorax is in a stationary state at the jth moment;

[0101] Based on the normalized electrical signal value time series, a local peak detection method is used to extract local maximum values Peaks and minimum values Valleys:

[0102] (Peaks, Valleys) PeakDetect ,

[0103] wherein Peaks is a set of time points containing all local maximum values, Valleys is a set of time points containing all local minimum values, and PeakDetect is a function used to extract local maximum and minimum points in the local peak detection method;

[0104] The time interval between two adjacent maximum values is a respiratory cycle Z:

[0105] ,

[0106] wherein is the time corresponding to the i+1th local maximum value in the set of local maximum time points, is the time corresponding to the ith local maximum value in the set of local maximum time points;

[0107] Based on the respiratory cycle, the user's respiratory frequency H is calculated:

[0108] ,

[0109] Based on the obtained maximum and minimum value sets, the respiratory amplitude is calculated :

[0110] ,

[0111] wherein N is the total number of pairs of maximum values and corresponding minimum values in the set, is the normalized electrical signal value corresponding to the ith maximum value point in the set of maximum values, is the normalized electrical signal value corresponding to the ith minimum value point in the set of minimum values.

[0112] By taking the derivative of the normalized electrical signal value time series and extracting local features, the user's breathing stages and signal key points are comprehensively captured, and the whole breathing process is dynamically restored. By calculating the respiratory cycle, frequency and amplitude, the user's breathing rhythm and breathing depth are accurately described. The problem of difficulty in real-time acquisition and quantification of user breathing rhythm features is effectively solved, and the accuracy and stability of respiratory data analysis are significantly improved, providing reliable data support for user breathing state evaluation and abnormal detection.

[0113] Further, the airbag pressure is dynamically adjusted to match the user's breathing rhythm by combining the basic pressure adjustment curve and the user's breathing rhythm characteristics.

[0114]

[0115] wherein, is the amplitude adjustment coefficient set according to different user groups;

[0116] The airbag adjustment frequency C is calculated based on the user's breathing frequency:

[0117]

[0118] wherein, is the frequency adjustment coefficient set according to different user groups;

[0119] The final pressure adjustment value is calculated according to the basic pressure adjustment curve, the airbag adjustment micro-pressure amplitude, and the airbag adjustment frequency:

[0120]

[0121] wherein, is the final adjusted pressure value at the jth moment, is the intensity coefficient of frequency to amplitude correction, is the pressure increasing and decreasing operation according to the user's inhalation and exhalation.

[0122] By dynamically combining the user's breathing amplitude and frequency characteristics, and referring to the basic pressure adjustment curve, a real-time airbag pressure adjustment mechanism is formed, so that the airbag pressure can accurately match the user's breathing rhythm and force. The problem of airbag pressure adjustment response not being timely and the amplitude not matching the user's breathing characteristics is effectively solved, and the user's comfort and the accuracy of pressure dynamic adaptation are significantly improved.

[0123] S3: Upload the running data and airbag adjustment records to the database to form a closed-loop feedback.

[0124] Specifically, the running data and airbag adjustment records are uploaded to the database to form a closed-loop feedback, which means that the user's breathing characteristics and airbag pressure adjustment records are uploaded to the database to form a closed-loop feedback.

[0125] ​​​​​By uploading the user breathing characteristics and the airbag pressure adjustment record to the database, the unified storage and management of data are completed, forming a complete and traceable operation record; effectively solving the data breakpoint and loss problem, providing a reliable data basis for subsequent data analysis, state trend prediction and adjustment strategy optimization; through the closed-loop feedback mechanism, the system can dynamically adjust the algorithm according to the historical data, and autonomously optimize the pressure adjustment scheme, improving the intelligent level and adjustment effect of the system.

[0126] The embodiment also provides a data processing system for an airbag in a hyperbaric oxygen chamber, comprising:

[0127] A data acquisition module is configured to acquire airbag micro-pressure data and user breathing data in real time.

[0128] A micro-pressure processing module is configured to extract micro-pressure characteristics from the micro-pressure data and calculate micro-pressure recognition results through the characteristics.

[0129] A basic pressure curve generation module is configured to generate a basic pressure adjustment curve based on the micro-pressure recognition results.

[0130] A breathing feature extraction module is configured to extract breathing rhythm features from the user breathing data.

[0131] An airbag pressure adjustment module is configured to dynamically adjust the airbag pressure to match the user breathing rhythm.

[0132] A closed-loop feedback module is configured to upload the airbag data, user data and adjustment process data to a database to realize closed-loop feedback.

[0133] The embodiment also provides a computer device suitable for a data processing method for an airbag in a hyperbaric oxygen chamber, comprising a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to realize the data processing method for the airbag in the hyperbaric oxygen chamber as described in the above embodiment.

[0134] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected by a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved by WIFI, a carrier network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, a trackball or a touchpad arranged on the shell of the computer device, or an external keyboard, a touchpad or a mouse, etc.

[0135] The embodiment also provides a storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the data processing method for the gas bag in the hyperbaric oxygen chamber. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk.

[0136] In summary, the present application acquires dual dynamic information of equipment running state and user physiological behavior by collecting airbag micro-pressure data and user breathing data in real time, provides data basis for subsequent analysis and processing, and guarantees the accuracy and real-time performance of the adjustment logic; the system realizes comprehensive analysis of the airbag running state by extracting micro-pressure characteristics from the airbag micro-pressure data and calculating micro-pressure recognition results through the characteristics, can identify abnormalities in time and generate a basic pressure adjustment curve, and provides guarantee for the reliability and safety of the airbag system; the breathing rhythm features are extracted based on the user breathing data, the actual breathing behavior mode of the user is analyzed, and the system can capture the dynamic needs of the user in real time; under the driving of the basic pressure adjustment curve and the user breathing features, the system adjusts the airbag pressure dynamically, realizes real-time synchronous matching of the airbag and the user breathing, and provides a more comfortable and safe breathing experience for the user; the system running data and adjustment records are uploaded to the database and a closed-loop feedback mechanism is constructed, the system can be continuously optimized according to the historical data, and the long-term running efficiency and adaptability are improved.

[0137] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A data processing method for an airbag in a hyperbaric oxygen chamber, characterized in that: include, Real-time acquisition of airbag micro-pressure data and user breathing data; acquisition of micro-pressure fluctuation characteristics based on airbag micro-pressure data; and calculation of micro-pressure identification results based on these characteristics. The airbag status is assessed by the micro-pressure recognition results, and a baseline pressure adjustment curve is generated. Based on the real-time collected user breathing data, breathing rhythm features are extracted. By combining the baseline pressure adjustment curve with the user breathing rhythm features, the airbag pressure is dynamically adjusted to match the user's breathing rhythm. Operational data and airbag adjustment records are uploaded to the database to form a closed-loop feedback.

2. The data processing method for the airbag in a hyperbaric oxygen chamber as described in claim 1, characterized in that: The real-time acquisition of airbag micro-pressure data refers to the use of high-precision micro-pressure sensors inside the airbag to obtain time-series data of the original pressure inside the airbag. ,in, This refers to a set of t raw pressure values; The real-time acquisition of user breathing data refers to the use of a chest strap-type breathing sensor to obtain real-time changes in the user's chest volume, and to calculate the electrical signal intensity of the user's chest movement based on these changes in chest volume. ; Time series data of chest movement electrical signal values ​​obtained from the electrical signal intensity of user chest movement. ,in, It refers to containing t electrical signal values.

3. The data processing method for the airbag in a hyperbaric oxygen chamber as described in claim 2, characterized in that: The process of acquiring micro-pressure fluctuation characteristics based on airbag micro-pressure data and calculating micro-pressure identification results through characteristic calculations involves normalizing the original pressure data within the airbag to obtain a standardized airbag pressure time series. ; Pressure time series characteristics, including the mean amplitude of pressure fluctuations, are calculated based on standardized airbag pressure time series data. Pressure fluctuation variance Pressure main frequency and pressure signal energy ; The micro-pressure identification result Q is calculated using the obtained pressure time series features.

4. The data processing method for the airbag in a hyperbaric oxygen chamber as described in claim 3, characterized in that: The process involves assessing the airbag status using micro-pressure recognition results and generating a baseline pressure adjustment curve based on the airbag pressure status assessment value and a standardized airbag pressure time series. The pressure assessment value at time j was calculated. ; Based on the pressure assessment values ​​at a single moment, a time series of airbag status assessment values ​​is obtained by integrating them. ,in, This refers to a set of t stress assessment values; Set stress assessment threshold and ,and ; If the evaluation value is the evaluation value at time j in the time series... If the airbag pressure is insufficient, it needs to be inflated. like If the airbag pressure is moderate, the current state will be maintained. like If the airbag pressure is too high, it needs to be depressurized. Record the time series of adjusted pressure ,in, This refers to a time series containing t pressure values, which is used as the base pressure adjustment curve.

5. The data processing method for the airbag in a hyperbaric oxygen chamber as described in claim 4, characterized in that: The extraction of respiratory rhythm features based on real-time acquired user respiratory data refers to normalizing the time-series data of the electrical signal values ​​of chest wall movement during user breathing to obtain a standardized time-series of electrical signal values. ; Determining a user's respiratory phase based on a time series of standardized electrical signal values, and specifying the standardized electrical signal value at each moment... Differentiate, if This indicates that the ribcage is expanding, and the user is in the inhalation phase at time j. This indicates that the chest cavity is contracting, and the user is in the exhalation phase at time j. This indicates that the user's chest cavity is in a static state at time j; Based on the standardized time series of electrical signal values, a local peak detection method is used to extract local maxima and minima. The time interval between two adjacent maxima is one respiratory cycle Z; Calculate the user's respiratory rate H based on the respiratory cycle; Based on the obtained set of maxima and minima, calculate the respiratory amplitude. .

6. The data processing method for the airbag in a hyperbaric oxygen chamber as described in claim 5, characterized in that: The phrase "combining the baseline pressure adjustment curve with the user's breathing rhythm characteristics to dynamically adjust the airbag pressure to match the user's breathing rhythm" refers to calculating the magnitude of the airbag pressure adjustment based on the user's breathing amplitude. ; The adjustment frequency C of the airbag is calculated based on the user's breathing rate; The final pressure adjustment value is calculated based on the baseline pressure adjustment curve, the airbag adjustment micro-pressure amplitude, and the airbag adjustment frequency. .

7. The data processing method for an airbag in a hyperbaric oxygen chamber as described in claim 6, characterized in that: The process of uploading operational data and airbag adjustment records to the database to form a closed-loop feedback refers to uploading the user's breathing characteristics and airbag pressure adjustment records to the database to form a closed-loop feedback.

8. A data processing system for an airbag in a hyperbaric oxygen chamber, based on the data processing method for an airbag in a hyperbaric oxygen chamber according to any one of claims 1 to 7, characterized in that: include, The data acquisition module is used to collect airbag micropressure data and user breathing data in real time; The micro-pressure processing module is used to extract micro-pressure characteristics from micro-pressure data and calculate micro-pressure identification results based on these characteristics. The basic pressure curve generation module generates a basic pressure adjustment curve based on the micro-pressure identification results. The breathing feature extraction module is used to extract breathing rhythm features from user breathing data; The airbag pressure regulation module is used to dynamically adjust the airbag pressure to match the user's breathing rhythm; The closed-loop feedback module is used to upload airbag data, user data, and adjustment process data to the database to achieve closed-loop feedback.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the data processing method for the airbag in a hyperbaric oxygen chamber as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the data processing method for the airbag in a hyperbaric oxygen chamber as described in any one of claims 1 to 7.

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