Intelligent respiratory management system and method based on closed air health assessment

Abstract

The application provides an intelligent breathing management system and method based on closed air health evaluation. The system is based on hyperventilation breathing mode and alternating breath-holding test. The intelligent correlation health status is realized through breath-holding test under different breathing modes in multiple stages, including resting closed air test under maximum and minimum weight, longest inhalation test under maximum and minimum weight, etc. The overall stress capacity of the user and the functional threshold and stress capacity of each system such as the circulatory system and the respiratory system are comprehensively evaluated. The system calculates multiple health indexes based on the test data, including the basic closed air tolerance index, the lung capacity and compliance index, etc., to form a multi-dimensional health evaluation. According to the evaluation results, a personalized breathing training program and health management suggestion are generated, and test guidance, result display and training guidance are provided through the user interface to realize non-invasive and convenient multi-system health evaluation and provide a scientific tool for personalized health management.

Description

[0001] This application claims priority to Chinese Patent Application No. 2025105072254, filed on April 22, 2025. The entire contents of the aforementioned Chinese patent application are incorporated herein by reference. Technical Field

[0002] This invention relates to the field of health management technology, and in particular to an intelligent respiratory management system and method based on breath-holding health assessment, which is used to assess the health status of multiple systems in the human body through multi-stage breath-holding tests—ideally simulating the position of a fetus at birth—and to provide personalized breathing training and health intervention programs. Background Technology

[0003] With the deepening development of health concepts, people's demand for health assessment and management is increasing. Traditional health assessment methods are often limited to the assessment of a single system. For example, respiratory system assessment is limited to pulmonary function tests, cardiovascular system assessment is limited to blood pressure and electrocardiogram examinations, and autonomic nervous system assessment is limited to heart rate variability analysis, failing to comprehensively reflect the health status of the human body as a whole. Current technologies for health assessment typically rely on complex medical equipment and specialized operations, making it difficult to meet the needs of daily health management. While wearable devices are convenient, they usually only collect basic physiological parameters, lacking in-depth health interpretation capabilities and failing to provide systematic health status assessments and personalized intervention recommendations. Breath-holding, as a simple physiological challenge, can trigger a coordinated response from multiple systems in the body. During breath-holding, the respiratory, cardiovascular, and autonomic nervous systems exhibit a series of characteristic changes, which contain rich health information. However, currently, there is a lack of systematic methods to use breath-holding responses as a health assessment tool, failing to fully explore and utilize the health information contained in this simple test. Existing breathing training applications mostly provide standardized training programs, lacking personalized design based on individual physiological characteristics, making it difficult to achieve optimal training results. Furthermore, these applications often lack scientific assessment systems, making it difficult to objectively evaluate training effects and adjust training plans. Therefore, there is an urgent need for an intelligent health management system that can comprehensively assess the health status of multiple human systems in a simple and non-invasive manner and provide personalized training programs. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide an intelligent respiratory management system and method based on breath-holding health assessment. Through multi-stage breath-holding tests, especially stress tests simulating the posture of a fetus at birth, the functional status of the respiratory system, autonomic nervous system and cardiovascular system is comprehensively assessed, a multi-dimensional health index system is constructed, and personalized breathing training and health management plans are generated based on the assessment results, so as to achieve accurate assessment, personalized intervention and long-term management of health status.

[0005] To address the aforementioned technical problems, this invention provides an intelligent respiratory management system based on breath-holding health assessment, the system comprising:

[0006] The data acquisition module is used to collect physiological data from the user during a multi-stage breath-holding test, which includes a resting breath-holding test. ), Maximum inhalation test ( Hyperventilation mode breath-holding test ) and the test of holding one's breath by contracting the whole body's muscle groups at the critical point of the highest carbon dioxide concentration. The physiological data includes blood oxygen saturation, heart rate, heart rate variability, peripheral perfusion index, respiratory status, and basic user information.

[0007] The data processing module is used to preprocess, extract features, and calculate parameters of the collected physiological data. The preprocessing includes signal filtering, artifact removal, and data smoothing. The feature extraction includes the extraction of time-domain features, frequency-domain features, and nonlinear features. The parameter calculation includes the calculation of parameters such as breath-holding duration, blood oxygen saturation rate, heart rate change pattern, and perfusion index change.

[0008] The health assessment module is used to calculate multidimensional health indices based on processed data and generate a health assessment report. These health indices include the Basal Breath-Holding Tolerance Index (BRTI), the VLCI (Vulnerability-Limited Lung Index), and the Lung Expansion Tolerance Index (LTI). The system includes several performance indicators, such as the Caloric Tolerance Improvement Index (CRT), Autonomic Nerve Regulation Index (ANSRI), Vagal Innervation Index (VNDI), Cardiovascular Adaptation Index (CRI), Vasoconstriction Index (VRI), Hypoxia Tolerance Index (HTI), Breath-Holding Oxygen Efficiency Index (BOI), Forced Breath-Holding Tolerance Index (FRTI), and Autonomic Regulation Optimization Index (ARTI). A health management module is used to generate personalized training plans and health management recommendations based on health assessment results. The training plans include training objectives, training types, training intensity, training frequency, and progress strategies. The health management recommendations include health risk alerts, lifestyle optimization suggestions, and progress tracking plans.

[0009] The user interaction module provides a user interface to guide users through tests, display evaluation results, guide the training process, and collect user feedback. The user interface includes a test guidance interface, an evaluation result display interface, a training guidance interface, and a progress tracking interface.

[0010] This invention also provides an intelligent respiratory management method based on breath-holding health assessment, the method comprising: a multi-stage breath-holding test procedure, guiding the user to complete the resting breath-holding test sequentially. ), Maximum inhalation test ( ), deep breathing and breath-holding test ( ) and full-body breath-holding test ( );

[0011] The data acquisition step involves collecting physiological data from the user during the test, including blood oxygen saturation, heart rate, heart rate variability, peripheral perfusion index, respiratory status, and basic user information; the data processing step involves preprocessing, feature extraction, and parameter calculation of the collected physiological data.

[0012] The health assessment step involves calculating a multidimensional health index based on the processed data and generating a health assessment report.

[0013] The training program generation process involves generating personalized training programs and health management recommendations based on health assessment results.

[0014] The training guidance steps guide users through the training plan via the user interface and collect training feedback.

[0015] The effectiveness evaluation process involves periodically reassessing the user's health status, analyzing the training effect, and adjusting the training plan. Attached Figure Description

[0016] Figure 1 This is a diagram showing the overall architecture of the intelligent respiratory management system based on breath-holding health assessment of the present invention.

[0017] Figure 2 This is a structural diagram of the data acquisition module of the present invention;

[0018] Figure 3 This is a structural diagram of the health assessment module of the present invention;

[0019] Figure 4 This is a flowchart of the health index calculation process of the present invention;

[0020] Figure 5 This is a structural diagram of the data processing module of the present invention;

[0021] Figure 6 This is a structural diagram of the health management module of the present invention;

[0022] Figure 7 This is a structural diagram of the user interaction module of the present invention;

[0023] Figure 8 This is a flowchart of the multi-stage air tightness test of the present invention;

[0024] Figure 9 This is an example diagram of the health index assessment results of the present invention;

[0025] Figure 10 This is a flowchart for generating the personalized training scheme of the present invention.

[0026] Figure 11 This is a schematic diagram of the structure of the electronic device provided in Embodiment 1 of the present invention.

[0027] Figure 12 This is a schematic diagram of the first test association of the present invention.

[0028] Figure 13 This is a schematic diagram of the second test association of the present invention.

[0029] Figure 14 This is a schematic diagram of the third test association of the present invention.

[0030] Figure 15 This is a schematic diagram of the fourth test association of the present invention.

[0031] Figure 16 This is a schematic diagram of the fifth test association of the present invention.

[0032] Figure 17 This is a schematic diagram of the sixth test association of the present invention.

[0033] Figure 18 This is a schematic diagram of the seventh test association of the present invention.

[0034] Figure 19 This is a schematic diagram of the eighth test association of the present invention.

[0035] Figure 20 This is a schematic diagram of the ninth test association of the present invention.

[0036] Figure 21 This is a schematic diagram of the tenth test association of the present invention.

[0037] Figure 22 This is a schematic diagram of the eleventh test association of the present invention.

[0038] Figure 23 This is a schematic diagram of the twelfth test association of the present invention.

[0039] Figure 24 This is a schematic diagram of the thirteenth test association of the present invention.

[0040] Figure 25 This is a schematic diagram of the fourteenth test association of the present invention.

[0041] Figure 26 This is a schematic diagram of the fifteenth test association of the present invention.

[0042] Figure 27 This is a schematic diagram of the sixteenth test association of the present invention.

[0043] Figure 28 This is a schematic diagram of the seventeenth test association of the present invention.

[0044] Figure 29 This is a schematic diagram of the eighteenth test association of the present invention.

[0045] Figure 30 This is a schematic diagram of the nineteenth test association of the present invention.

[0046] Figure 31 This is a schematic diagram of the twentieth test association of the present invention.

[0047] Figure 32 This is a schematic diagram of the twenty-first test association of the present invention.

[0048] Figure 33 This is a schematic diagram of the twenty-second test association of the present invention. Detailed Implementation

[0049] The present disclosure is further illustrated below by way of embodiments, but the present disclosure is not limited to the scope of the embodiments described herein.

[0050] Example 1

[0051] This embodiment provides the overall system architecture.

[0052] like Figure 1 As shown, the intelligent respiratory management system based on breath-holding health assessment provided by this invention mainly includes five core modules: a data acquisition module, a data processing module, a health assessment module, a health management module, and a user interaction module. These modules form a closed-loop system, enabling end-to-end management from data acquisition to health assessment, protocol generation, and training guidance.

[0053] Example 2

[0054] This embodiment provides a data acquisition module.

[0055] like Figure 2 As shown, the data acquisition module is responsible for collecting the user's physiological data during the multi-stage breath-holding test, and mainly includes the following units:

[0056] Blood oxygen acquisition unit: Monitors the user's blood oxygen saturation in real time via a pulse oximeter. Record the breath-holding process The curve of change, marked with the lowest Value and rate of decline. Sampling frequency not less than 1Hz, accuracy not less than ±2%. Heart rate monitoring unit: Simultaneously monitors the user's heart rate, heart rate variability, and peripheral perfusion index (PI), recording the change patterns during breath-holding. Heart rate monitoring sampling frequency not less than 10Hz, supporting time-domain and frequency-domain analysis of heart rate variability.

[0057] Breath-holding time recording unit: accurately records the duration of each stage of the breath-holding test, automatically determines the start and end times of breath-holding through a breathing sensor, evaluates the quality of breath-holding, and eliminates unqualified tests.

[0058] User information collection unit: Collects basic user information (such as age, gender, height, weight), health history, and past training records to provide background information and reference baseline for health assessment.

[0059] Example 3

[0060] This embodiment provides a multi-stage air closure test method.

[0061] like Figure 8 As shown, the multi-stage breath-holding test of the present invention includes four test stages, each designed for a specific physiological state to assess different aspects of health:

[0062] 1) Resting breath-holding test ( ):

[0063] Test objective: To assess basic breath-holding ability and autonomic nervous system balance at rest.

[0064] Test method: The user assumes a relaxed supine position and breathes naturally for 3 minutes. Then, at the end of a calm exhalation, the user begins to hold their breath and continues until noticeable discomfort is felt.

[0065] Monitoring parameters: breath-holding duration, Changes, heart rate changes, PI changes

[0066] 2) Maximum inspiratory breath-hold test ( ):

[0067] Test objective: To assess lung volume, airway compliance, and respiratory muscle strength.

[0068] Test method: The user slowly inhales deeply through the gaps between their teeth to the maximum extent, then holds their breath until discomfort is obvious.

[0069] Monitoring parameters: breath-holding duration, Changes, heart rate changes, PI changes

[0070] 3) Breath-holding test after deep breathing ( ):

[0071] Test objective: Evaluation Tolerance and chemoreceptor sensitivity

[0072] Test method: The user takes 30 deep breaths (similar to the Wenckebach breathing method), and then holds their breath after the last deep inhalation until discomfort is obvious.

[0073] Monitoring parameters: breath-holding duration, Changes, heart rate changes, PI changes

[0074] 4) Full-body breath-holding test ( ):

[0075] Test objective: To assess cardiovascular regulatory capacity under sympathetic nervous system activity.

[0076] Test method: The user clenches both fists forcefully (approximately 30% of maximum strength), holds their breath while maintaining full muscle tension, and continues until noticeable discomfort is felt.

[0077] Monitoring parameters: breath-holding duration, There should be a 5-10 minute interval between each test phase, including changes in heart rate and PI, to ensure that physiological parameters return to baseline levels. The testing environment should be quiet, at a suitable temperature, and free from external interference.

[0078] Example 4

[0079] This embodiment provides a data processing module.

[0080] like Figure 5 As shown, the data processing module is responsible for processing the collected raw physiological data, extracting effective features, and providing a basis for health assessment. It mainly includes the following functional units:

[0081] 1) Data preprocessing:

[0082] Median filtering: Applying a median filter to blood oxygen and heart rate data to remove burst noise.

[0083] Artifact Detection and Removal: Identifying and removing signal artifacts caused by actions, speech, etc.

[0084] Signal smoothing: A Savitzky-Golay filter is applied to smooth the signal, preserving signal characteristics while reducing noise.

[0085] 2) Feature extraction:

[0086] Feature extraction: Extracting features such as baseline value, minimum value, rate of decline, and shape of the decline curve.

[0087] Heart rate feature extraction: Extracting features such as baseline heart rate, maximum / minimum heart rate, rate of change, and change pattern.

[0088] HRV Feature Extraction: Calculate heart rate variability indicators such as SDNN, RMSSD, and LF / HF ratio.

[0089] PI Feature Extraction: Extracting features such as perfusion index variation patterns, minimum values, and rates of change.

[0090] 3) Parameter calculation:

[0091] Breath-holding related parameters: Calculate the breath-holding duration and relative duration ratio at each stage, etc.

[0092] Heart rate related parameters: Calculate the amplitude of heart rate changes, rate of change, recovery time, etc.

[0093] Blood oxygenation-related parameters: Calculate the rate of decrease in blood oxygen, the lowest point, and the recovery rate, etc.

[0094] Perfusion-related parameters: Calculate the magnitude of change and fluctuation characteristics of the perfusion index.

[0095] 4) Data storage:

[0096] Local database: Securely stores processed data on the user's device.

[0097] Secure cloud storage: Encrypted uploads are sent to cloud servers, supporting cross-device access.

[0098] Data backup and synchronization: Establish a data backup mechanism to ensure data security and synchronization. Data processing adopts a combination of edge computing and cloud computing, with simple processing completed on user devices and complex analysis performed in the cloud, balancing response speed and computing power.

[0099] Example 5

[0100] This embodiment provides a health assessment module.

[0101] like Figure 3 As shown, the health assessment module is the core of this invention, responsible for calculating a multidimensional health index based on the processed data to assess the user's health status. It mainly includes the following functional units:

[0102] The main health indices calculated by the health assessment module and their definitions are as follows:

[0103] 1) Basic Health Index:

[0104] Basic Breath-Holding Tolerance Index (BRTI): Assesses baseline breath-holding ability and reflects overall respiratory regulation status. Calculation formula: Where T1 is the resting breath-holding duration. The degree of decrease in blood oxygen. This is the correction factor.

[0105] Hypoxia Tolerance Index (HTI): Assesses the body's tolerance to low-oxygen environments. Calculation formula: ,in This is the correction factor.

[0106] Oxygen efficiency index (BOI): Assesses the efficiency of maintaining an open airway per unit of oxygen consumption. Calculation formula: ,in This is the correction factor.

[0107] 2) Respiratory system index:

[0108] Lung Volume and Compliance Index (VLCI): Assesses lung volume and airway compliance. Calculation formula: ,in This is the maximum inspiratory breath-holding time. This is the correction factor.

[0109] Lung expansion endurance index (LTI): Assesses respiratory muscle strength and endurance. Calculation formula: ,in This is the correction factor.

[0110] Tolerance Improvement Index (CRT): Assessment Tolerance and chemoreceptor sensitivity. Calculation formula: ,in The duration of breath-holding after a deep breath. This is the correction factor.

[0111] 3) Autonomic nervous system index:

[0112] Autonomic Regulation Index (ANSRI): Assesses the regulatory capacity of the autonomic nervous system. Calculation formula: The HRV coefficient is based on the heart rate variability parameter, where ΔHR is the heart rate change during breath-holding. This is the correction factor.

[0113] Vagus innervation index (VNDI): Assesses the level of vagal nerve activity. Calculation formula: FRTI is the total effort breath-holding tolerance index. This is the correction factor.

[0114] Autonomic Regulation Optimization Index (ARTI): This index assesses the regulatory effect of deep breathing on the autonomic nervous system. Calculation formula: ,in The duration of breath-holding using full body force. This is the correction factor.

[0115] 4) Cardiovascular index:

[0116] Cardiovascular Adaptability Index (CRI): Assesses the cardiovascular system's ability to adapt to breath-holding. Calculation formula: Where ΔHR is the maximum change in maximum heart rate, This is the correction factor.

[0117] Vasoconstriction Index (VRI): Assesses the intensity and efficiency of the vasomotor response. Calculation formula: Where PI is the perfusion index, This is the correction factor.

[0118] Forced Breath-Holding Tolerance Index (FRTI): Assesses breath-holding ability under sympathetic nervous system activation. Calculation formula: ,in This is the correction factor.

[0119] 5) Generation of comprehensive score:

[0120] System classification scoring: Classification scores for the respiratory system, autonomic nervous system, and cardiovascular system are generated based on indices for each system.

[0121] Comprehensive Health Score: Integrates all health indices to generate a comprehensive health score from 0 to 100.

[0122] Risk warning analysis: Identifying potential health risks based on health index models and generating risk warnings.

[0123] Figure 4 This demonstrates the overall process of calculating the health index:

[0124] The health index is calculated taking into account individual differences such as age and gender, and is adjusted for individual needs using a correction factor. The assessment results are expressed as percentiles: 90 points and above is excellent, 70-90 points is good, 50-70 points is average, 30-50 points is poor, and below 30 points is unhealthy.

[0125] Example 6

[0126] This embodiment provides a health management module.

[0127] like Figure 6 As shown, the health management module provides users with personalized training plans and health management suggestions based on health assessment results. It mainly includes the following functional units:

[0128] 1) Training scheme generation unit:

[0129] Training Goal Setting: Based on the weaknesses in health indices, determine the training focus and set reasonable short-term, medium-term, and long-term goals.

[0130] Training type selection: from basic breath-holding training, lung capacity training, Choose the combination of training types that best suits the user, such as endurance training and autonomic balance training.

[0131] Training parameter customization: Set training duration, intensity, and frequency according to the user's ability to ensure that the training is both challenging and achievable.

[0132] Progression strategy planning: Design a progressive training path, including increasing intensity, increasing difficulty, and variation training.

[0133] 2) Health Risk Assessment Unit:

[0134] Systemic risk assessment: Analyze health index patterns to identify potential risks to the respiratory, autonomic nervous, and cardiovascular systems.

[0135] Early warning mechanism: High-risk indicators are marked, and warning thresholds are set. An early warning is triggered when an indicator approaches or exceeds the threshold.

[0136] Risk intervention recommendations: For identified risks, provide lifestyle modification and training intervention recommendations, and suggest professional medical consultation if necessary.

[0137] 3) Progress Tracking and Analysis Unit:

[0138] Short-term progress analysis: Tracking immediate feedback and short-term changes during training to evaluate the effectiveness of individual training sessions.

[0139] Training effectiveness evaluation: Compare changes in health indicators before and after training to assess the effectiveness of the training plan.

[0140] Solution Adjustment Engine: Dynamically adjusts training parameters and optimizes training results based on training performance and user feedback.

[0141] 4) Long-term data analysis unit:

[0142] Health Trend Analysis: Analyze the long-term trends of health indices and assess the overall direction of health development.

[0143] Seasonal variation analysis: Identifying seasonal fluctuation patterns in health indicators and developing seasonal adjustment strategies.

[0144] Life event correlation analysis: Analyzing the correlation between life events (such as work stress, sleep changes) and changes in health indices.

[0145] Figure 10 The process of generating personalized training plans is shown:

[0146] Training scheme example:

[0147] For users with low BRTI and ANSRI, prioritize autonomic balance training, such as the "4-7-8 breathing method" (inhale for 4 seconds, hold your breath for 7 seconds, exhale for 8 seconds), 2-3 times a day, 5-10 minutes each time;

[0148] For users with low VLCI and LTI, prioritize lung capacity training, such as "stepwise inhalation exercise" (inhaling in stages to maximum lung capacity), 1-2 times a day, 15-20 minutes each time;

[0149] For users with lower CRT settings, priority will be given. Endurance training, such as "extended exhalation training" (extending the exhalation time to twice the inhalation time), twice a day for 10 minutes each time;

[0150] The training program will be dynamically adjusted based on user feedback and progress to ensure maximum training effectiveness.

[0151] Example 7

[0152] This embodiment provides a user interaction module.

[0153] like Figure 7 As shown, the user interaction module provides an interface for users to interact with the system, guiding them through testing and training, and displaying evaluation results and progress. It mainly includes the following functional units:

[0154] 1) Test guide interface:

[0155] Test Preparation Guide: Provides pre-test preparation instructions, including environmental requirements, posture adjustments, and mental preparation.

[0156] Real-time status feedback: Displays physiological parameters such as heart rate and blood oxygen in real time during the test, enhancing user perception.

[0157] Test process guidance: Guides users through each step of the multi-stage breath-holding test using text, images, and voice.

[0158] 2) Evaluation Results Display:

[0159] Health index visualization: Health index assessment results are displayed intuitively through dashboards, radar charts, and other methods.

[0160] Health Interpretation Explanation: Provides a detailed explanation of each health index, including its physiological significance and health impact.

[0161] Improvement suggestions are provided: For each index, targeted improvement suggestions and training directions are given.

[0162] 3) Training guidance interface:

[0163] Training Demonstration Videos: Provides high-quality training demonstration videos showcasing correct training methods and techniques.

[0164] Real-time training guidance: Provides real-time guidance during training, including voice prompts, timing, and progress display.

[0165] Training feedback collection: Collect user subjective feelings and objective data after training to provide a basis for adjusting the plan.

[0166] 4) Progress Analysis Interface:

[0167] Historical data charts: Show the historical trends of health indicators, intuitively presenting progress.

[0168] Goal Achievement Display: Shows users the training goals and milestones they have achieved, enhancing their sense of accomplishment and motivation.

[0169] Trend Predictive Analysis: Predicting future health trends based on historical data and providing forward-looking health guidance.

[0170] 5) Community interaction features:

[0171] User Community: Establish a platform for users to communicate, share experiences and insights, and form a mutual support network.

[0172] Challenge Activities: Organize health challenge activities to increase fun and social motivation, and improve persistence.

[0173] Expert Consultation: We offer online consultations with professional health advisors to answer your health questions and training inquiries.

[0174] The user interface design follows the principles of simplicity, intuitiveness, and ease of use, catering to the needs of users of different ages. It offers multilingual support and accessibility features to ensure broad user accessibility.

[0175] Example 8

[0176] This embodiment provides the system implementation and technical execution.

[0177] 1) Hardware Implementation: The system's hardware mainly consists of a physiological monitoring terminal and a data processing unit. The physiological monitoring terminal integrates a high-precision pulse oximeter, ECG monitoring electrodes, and a respiratory status sensor, and connects to a smartphone or tablet via Bluetooth wireless technology. The device is compact, easy to wear, and has a battery life of at least 8 hours, meeting daily usage needs.

[0178] 2) Software Architecture: The software system adopts a client-server architecture. The client is a mobile application responsible for data acquisition, basic processing, and user interaction; the server is responsible for complex analysis, data storage, and user management. The system also supports an offline working mode to ensure basic functionality availability in environments without network access.

[0179] 3) Data Security and Privacy Protection: The system adopts a multi-layered security architecture, including data transmission encryption, storage encryption, and access control. User data is stored using AES-256 encryption, and sensitive data transmission uses the TLS 1.3 protocol. The system strictly adheres to the principle of data minimization, collecting only necessary health data, and provides user data export and deletion functions to ensure users have complete control over their personal data.

[0180] 4) System Deployment and Integration: The system supports both cloud and on-premises deployment modes to adapt to different scenario requirements. It also provides standardized API interfaces to support integration with other health platforms and medical systems, enabling data sharing and functional expansion. The system conforms to major medical data standards, such as HL7 FHIR, facilitating integration with the healthcare ecosystem.

[0181] 5) System performance indicators:

[0182] Data acquisition accuracy: Error ≤ ±2%, heart rate error ≤ ±3 bpm

[0183] System response time: Local processing < 1 second, cloud analysis < 3 seconds

[0184] Concurrency handling capacity: A single server supports >10,000 users online simultaneously.

[0185] Data storage capacity: Annual data volume per user <100MB, supports long-term historical data storage.

[0186] System availability: >99.9%, annual planned downtime <8 hours

[0187] Example 9

[0188] Figure 11 This is a schematic diagram of the structure of an electronic device provided in Embodiment 9 of this disclosure. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the intelligent respiratory management system and method based on breath-holding health assessment as described in Embodiment 1 and any of its embodiments. Figure 3 The electronic device 30 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0189] like Figure 11 As shown, the electronic device 30 can be represented in the form of a general computing device, such as a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including memory 32 and processor 31).

[0190] Bus 33 includes a data bus, an address bus, and a control bus.

[0191] The memory 32 may include volatile memory, such as random access memory (RAM) 321 and / or cache memory 322, and may further include read-only memory (ROM) 323.

[0192] The memory 32 may also include a program / utility 325 having a set (at least one) of program modules 324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0193] The processor 31 executes various functional applications and data processing by running computer programs stored in the memory 32, such as the intelligent respiratory management system and method based on breath-holding health assessment described in Embodiment 1 of this disclosure and any of its embodiments.

[0194] Electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 35. Furthermore, the model-generating device 30 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 36. Figure 3 As shown, network adapter 36 communicates with other modules of the model-generated device 30 via bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the model-generated device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0195] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0196] Example 10

[0197] This embodiment provides a computer-readable storage medium storing a computer program thereon. When the program is executed by a processor, it implements the intelligent respiratory management system and method based on breath-holding health assessment as described in Embodiment 1 and any of its embodiments.

[0198] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0199] In possible implementations, this disclosure can also be implemented as a program product comprising program code, which, when the program product is run on a terminal device, causes the terminal device to execute the intelligent respiratory management system and method based on breath-holding health assessment as described in Embodiment 1 and any of its embodiments.

[0200] The program code for executing this disclosure can be written using any combination of one or more programming languages. The program code can be executed entirely on a user device, partially on a user device, as a standalone software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0201] Example 11

[0202] Data: A Han Chinese male born in 1971. The experiment involved gene expression resetting within 48 hours, specifically optimizing the thermodynamic state of a multi-level, nonlinear, open giant system under environmental stimuli. More specifically, it involved an individual undergoing a specific scenario-designed extreme operation process—that is, extreme work and hot / cold stimulation operations guided by the Maximum Entropy Production Principle (MEPP), combined with the lowest possible energy supplementation and sufficient redundant electrolyte, vitamin, and protein supplementation—to reach the lowest weight of the day. Data from multiple practice sessions are as follows:

[0203] Lowest body weight and blood pressure after initial extreme training: 70.40 kg at 5:23 PM. Figure 12 As shown.

[0204] Enter a 16-degree air-conditioned room and practice inhalation and breath-holding.

[0205] During T1 and T3 testing, the baby's position should be as close to the fetal position as possible, minimizing the fetal curl-up during birth, and the baby should be wrapped in a blanket to keep warm; Figure 13 As shown.

[0206] When testing for T4, try to mimic the posture of a fetus taking its first deep breath, such as throwing off the blanket. Figure 14 and 15 As shown.

[0207] like Figure 16 As shown, the first T1; as Figure 17 As shown, the second T1; as Figure 18 As shown, a simplified statistical analysis and comparison of T1 under different conditions on the same day is presented; as... Figure 19 As shown, a brief statistical analysis and comparison of T1 over multiple days is presented.

[0208] like Figure 20 As shown, measure T2;

[0209] like Figure 21 As shown, T3 and T4 were measured during multiple rounds of exercise: More significantly, the heart rate rapidly decreased to the body's resting state after multiple rounds of exercise, even though at this time - at the lowest body weight - i.e., 4.9% of body fluid relative to the lowest body weight of the day - 3.45 kg of body fluid was expelled - from the maximum of 73.85 kg to the limit state of 70.40 kg.

[0210] like Figure 22 As shown, after practicing Qigong, blood pressure and heart rate gradually returned to normal.

[0211] like Figure 23 As shown, the extreme operation was repeated on April 19th to continue the measurement.

[0212] like Figure 24 As shown, the T1 was measured in a simulated birth position in an air-conditioned room at 16 degrees Celsius while the baby was wrapped in a blanket.

[0213] like Figure 25 As shown, the statistics of multiple t2 measurements on April 19th are presented.

[0214] like Figure 26 As shown, the statistics of multiple t3 measurements on April 19th are presented.

[0215] like Figure 27 As shown in Figure 4.19, the data for multiple rounds of inspiratory breath-holding: T3 and T4 were measured during multiple rounds of practice. More significantly, after multiple rounds of practice, the heart rate rapidly decreased to the level of the body at rest, even though at this time - at the lowest body weight - i.e., 4.8% (3.35 / 70.25) of body fluid relative to the lowest body weight of the day - 3.35 kg was expelled - from the maximum of 73.60 kg to the limit state of 70.25 kg.

[0216] like Figure 28 As shown, blood pressure, heart rate, etc. returned to normal very quickly, even though nothing was replenished except for gas.

[0217] like Figure 29 As shown, the body can continue to do work, such as climbing stairs, about 125 meters high, which is 47 floors with 17 steps on each floor and 28 steps on the top, each step is 15cm, reaching a new limit in weight, blood pressure, etc.

[0218] like Figure 30 As shown, this will be followed by a supplementary dinner.

[0219] like Figure 31 As shown, the Qi cultivation data was generated at dawn on April 20th.

[0220] The first T1, which is when the body restarts its peripheral motor nervous system, especially active breathing and muscle contraction, after a night of internal digestion, absorption, autonomic nervous system, smooth muscle and endocrine system working, can be approximated as the state when the entropy of the body's open system is at its maximum. One night of breathing can result in a weight loss of 72.00 kg to 71.70 kg.

[0221] like Figure 32 As shown, gas storage measurement - data analysis.

[0222] like Figure 33 As shown, this is the data from multiple rounds of inhalation and breath-holding.

[0223] In summary, this method clearly demonstrates the typical changes in Qi and blood state during stress testing and recovery from a nonlinear, multi-level, open megasystem like the human body. It can also correlate and evaluate the switching rate power of the human body's physiological function limit intervals and assess its intrinsic capacity in real time. This is expected to further help in the development of methods to correlate and optimize the switching capabilities of intrinsic gene expression mechanisms.

[0224] While specific embodiments of this disclosure have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of this disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of this disclosure, but all such changes and modifications fall within the scope of protection of this disclosure.

Claims

1. An intelligent respiratory management system based on breath-holding health assessment, characterized in that, include: The data acquisition module is used to collect physiological data of users during the multi-stage breath-holding test, which includes resting breath-holding test (T1), maximum inhalation test (T2), breath-holding test after deep breathing (T3), and breath-holding test when the whole body or part of the body exerts force (T4). The data processing module is used to preprocess, extract features, and calculate parameters from the collected physiological data. The health assessment module is used to calculate a multidimensional health index based on the processed data and generate a health assessment report. The health management module is used to generate personalized training plans and health management suggestions based on health assessment results. The user interaction module provides a user interface to guide users through tests, display evaluation results, guide the training process, and collect user feedback.

2. The system according to claim 1, characterized in that, The data acquisition module includes: a blood oxygen acquisition unit for monitoring the user's blood oxygen saturation. The system includes: a heart rate monitoring unit for monitoring the user's heart rate, heart rate variability, and peripheral perfusion index (PI); a breath-holding time recording unit for recording the duration of each stage of the breath-holding test; and a user information collection unit for collecting the user's basic information, health history, and training records.

3. The system according to claim 1, characterized in that, The data processing module includes: a data preprocessing unit for filtering, artifact removal, and signal smoothing of raw physiological data; a feature extraction unit for extracting effective features from the processed data; a parameter calculation unit for calculating occlusion-related parameters, heart rate-related parameters, blood oxygenation-related parameters, and perfusion-related parameters; and a data storage unit for securely storing and managing user data.

4. The system according to claim 1, characterized in that, The health assessment module calculates the following health indices: Basal Breath-Holding Tolerance Index (BRTI), Hypoxia Tolerance Index (HTI), Breath-Holding Oxygen Efficiency Index (BOI), Lung Volume and Compliance Index (VLCI), and Lung Expansion Tolerance Index (LTI). The tolerance enhancement index (CRT), autonomic nervous system regulation index (ANSRI), vagal nerve innervation index (VNDI), autonomic regulation optimization index (ARTI), cardiovascular adaptation index (CRI), vasoconstriction index (VRI), and forceful breath-holding tolerance index (FRTI).

5. The system according to claim 1, characterized in that, The health management module includes: a training program generation unit for generating personalized training programs based on weaknesses in the health index; a health risk assessment unit for analyzing health index patterns and identifying potential health risks; a progress tracking and analysis unit for tracking training effectiveness and adjusting training programs; and a long-term data analysis unit for analyzing long-term trends in the health index and assessing the overall direction of health development.

6. The system according to claim 1, characterized in that, The user interaction module includes: a test guidance interface for guiding users to complete a multi-stage breath-holding test; an assessment result display interface for displaying health assessment results and interpretations; a training guidance interface for providing training demonstrations and real-time guidance; a progress analysis interface for displaying historical changes and progress in health indices; and a community interaction function for supporting communication and interaction among users.

7. An intelligent respiratory management method based on breath-holding health assessment, characterized in that, Includes the following steps: The multi-stage breath-holding test procedure guides users to complete the resting breath-holding test sequentially. ), Maximum inhalation test ( Hyperventilation breathing pattern followed by breath-holding test ) and breath-holding test when exerting force on the whole body or part of the body at the highest carbon dioxide concentration in the body ( ); The data collection steps involve collecting physiological data from users during the testing process; The data processing steps include preprocessing, feature extraction, and parameter calculation of the collected physiological data. The health assessment step involves calculating a multidimensional health index based on the processed data and generating a health assessment report. The training program generation process involves generating personalized training programs and health management recommendations based on health assessment results. The training guidance steps guide users through the training plan via the user interface and collect training feedback; the effectiveness evaluation steps periodically reassess the user's health status, analyze the training effect, and adjust the training plan.

8. The method according to claim 7, characterized in that, The resting breath-holding test ( The steps are as follows: The user adopts a relaxed supine or sitting position and breathes naturally for 3 minutes in a specific temperature and setting. Then, at the end of a calm exhalation, the user begins to hold their breath and persists until discomfort is obvious and the heart's compensatory contraction pattern is triggered. During the test, blood oxygen saturation, heart rate, heart rate variability, and peripheral perfusion index are continuously monitored.

9. The method according to claim 7, characterized in that, The steps of the maximum inspiratory time test (T2) are as follows: the user slowly inhales deeply through the gaps between his teeth to the maximum lung capacity and persists until discomfort is obvious; during the test, blood oxygen saturation, heart rate, heart rate variability and peripheral perfusion index are continuously monitored.

10. The method according to claim 7, characterized in that, The deep breathing and breath-holding test ( The steps are as follows: The user performs multiple long inhalations and short exhalations (Hyperventilation mode) of breathing, such as 10-50 times. After the last deep inhalation, the user holds their breath, during which they can only exhale and not inhale. The body posture should be as close as possible to the birth canal, that is, to achieve the longest breath-holding time by curling the body with the least amount of energy. The user should continue until discomfort is obvious, triggering the compensatory contraction mode of the heart. During the test, blood oxygen saturation, heart rate, heart rate variability and peripheral perfusion index are continuously monitored.

11. The method according to claim 7, characterized in that, The whole-body force breath-holding test ( The steps are as follows: After the longest breath-holding period, the user reaches the critical point of the lowest oxygen concentration and the highest carbon dioxide concentration in the body, simulating the state of a fetus just emerging from the birth canal and taking its first breath. At this time, the user clenches their fists and exerts force to simulate the posture of a fetus taking its first breath at birth as closely as possible. After inhaling, the user holds their breath and forcefully contracts all muscle groups, especially the neck muscles, until discomfort is obvious. At this time, the user tries to maintain the compensatory contraction pattern of the heart, that is, to measure the individual's muscle function under the maximum pressure of the heart at the highest carbon dioxide concentration in the body. During the test, blood oxygen saturation, heart rate, heart rate variability and peripheral perfusion index are continuously monitored.

12. The method according to claim 7, characterized in that, The health assessment steps include: calculating basic health indices such as the Basal Breath-Holding Tolerance Index (BRTI), Hypoxia Tolerance Index (HTI), and Breath-Holding Oxygen Efficiency Index (BOI); calculating the Lung Volume and Compliance Index (VLCI), Lung Expansion Tolerance Index (LTI), and... Respiratory system indices such as the Tolerance Improvement Index (CRT) are calculated; autonomic nervous system indices such as the Autonomic Regulation Index (ANSRI), Vagal Innervation Index (VNDI), and Autonomic Regulation Optimization Index (ARTI) are calculated; cardiovascular indices such as the Cardiovascular Adaptation Index (CRI), Vasoconstriction Index (VRI), and Forced Breath-Holding Tolerance Index (FRTI) are calculated; and a system classification score and comprehensive health score are generated based on the above indices.

13. The method according to claim 7, characterized in that, The training plan generation steps include: analyzing the strengths and weaknesses of health indices and determining training priorities; integrating user basic information and selecting suitable training types; setting training intensity, frequency, and duration based on user capabilities; designing a progressive training path, including increasing intensity, increasing difficulty, and variation training; and generating detailed training plans, instructions, and expected results.

14. The method according to claim 7, characterized in that, The effectiveness evaluation steps include: periodically re-performing multi-stage breath-holding tests to obtain the latest physiological data; calculating the updated health index and comparing it with the baseline data; evaluating the training effect and identifying indicators that have improved significantly and still need to be strengthened; adjusting the training program based on the evaluation results to optimize the training effect; and updating the health risk assessment and long-term health trend prediction.

15. A method for calculating a health index based on a multi-stage breath-holding test, characterized in that, Includes the following steps: Obtain user data during resting breath-holding tests ( ), Maximum inspiratory breath-hold test ( ), deep breathing and breath-holding test ( ) and full-body breath-holding test ( Physiological data from the data; parameters such as breath-holding duration, blood oxygenation changes, heart rate changes, and perfusion index changes were extracted. Calculate the basic air-holding tolerance index: ,in The duration of breath-holding at rest. The degree of decrease in blood oxygen. The correction factor is used to calculate the hypoxia tolerance index. ,in As a correction factor; calculate the closed-air oxygen efficiency index: ,in The correction factor is used to calculate lung volume and compliance index. ,in This is the maximum inspiratory breath-holding time. The correction factor is used to calculate the lung expansion tolerance index. ,in For the correction factor; calculate Tolerance Improvement Index: ,in The duration of breath-holding after a deep breath. The correction coefficient is used; the autonomic nervous system regulation index is calculated as follows: The HRV coefficient is based on the heart rate variability parameter, where ΔHR is the heart rate change during breath-holding. For the correction factor; calculate the vagus nerve innervation index: FRTI is the total effort breath-holding tolerance index. The correction coefficient is used; the self-adjusting optimization index is calculated: ,in The duration of breath-holding using full body force. The correction factor is used to calculate the cardiovascular fitness index. Where ΔHR is the maximum change in maximum heart rate, For correction factors; Calculate the vasomotor index: Where PI is the perfusion index, The correction factor is used to calculate the whole-body exertion breath-holding tolerance index. ,in The above index is used as a correction factor; the system classification score and comprehensive health score are calculated based on the above index.

16. A personalized breathing training method based on breath-holding health assessment, characterized in that, Includes the following steps: Obtain users' health index assessment results, including the Baseline Breath-Holding Tolerance Index (BRTI) and the VLCI (Vulnerability-Limited Index). Tolerance Improvement Index (CRT), Autonomic Nervous Regulation Index (ANSRI), etc.; determine the training focus and priority based on the low scores of the health index; design basic breath-holding ability training for users with low BRTI, such as intermittent breath-holding training and gradually increasing breath-holding time training. For users with low VLCI, lung volume training is designed, such as step-by-step inhalation exercises and diaphragmatic breathing exercises; for users with low CRT, [further training is needed]. Tolerance training, such as prolonged exhalation training and respiratory rate control training; For users with low ANSRI, we designed autonomic balance training, such as the 4-7-8 breathing method and the square breathing method. Set training parameters based on the user's basic abilities, including training duration, intensity, and frequency; Detailed training guidance is provided through the user interface, including posture requirements, breathing rhythm, and precautions; Collect user feedback and physiological data during the training process to dynamically adjust the training plan; Regularly evaluate training effectiveness, update training programs, and ensure continuous progress.

17. A method for health risk assessment using an intelligent respiratory management system based on breath-holding health assessment, characterized in that, Includes the following steps: Obtain users' health index assessment results; analyze the scores of the Autonomic Regulation Index (ANSRI) and Vagal Innervation Index (VNDI), with scores below 40 indicating a risk of autonomic dysfunction; analyze the scores of the Cardiovascular Adaptation Index (CRI) and Vasoconstriction Index (VRI), with scores below 35 indicating a risk of poor cardiovascular regulation; analyze... The CRT score is below 30 points, indicating a risk of abnormal respiratory regulation sensitivity; the Hypoxia Tolerance Index (HTI) and Closed-Air Oxygen Efficiency Index (BOI) scores are analyzed, and scores below 25 points are identified as a risk of poor tissue oxygen utilization efficiency; based on the identified risks, risk level classifications and early warning prompts are generated. Provide targeted risk intervention recommendations, including lifestyle modifications and training strategies; Establish a risk monitoring plan and regularly reassess changes in risk; If necessary, professional medical consultation should be recommended to assist in early intervention.

18. The method according to claim 17, characterized in that, Further steps include: Track long-term trends in users' health indices; identify early warning signs of health deterioration, such as a continuous decline in indices or increased volatility. Compare the differences between users' health index and reference values ​​for their age group; assess users' susceptibility to related diseases and provide prevention suggestions; establish personalized health prediction models to predict future health trends.

19. A data analysis method for an intelligent respiratory management system based on breath-holding health assessment, characterized in that, Includes the following steps: Collect multi-stage breath-holding test data and health indices from multiple users; anonymize the data to protect user privacy; analyze the distribution characteristics of health indices among different age groups, genders, and health conditions. Identify the correlations and causal relationships among health indices; construct association models between health indices and the risk of specific diseases; optimize the calculation algorithm and reference range of health indices; develop new health assessment indicators and training methods; and verify the long-term effects of training interventions. To create a health knowledge graph that supports personalized health intervention decisions.

20. An integrated application method for an intelligent respiratory management system based on breath-holding health assessment, characterized in that, Includes the following steps: The system integrates with users' smart devices (such as smartwatches and mobile phones) to achieve data sharing and functional expansion; it interfaces with health management platforms to integrate a wider range of health data, such as exercise, sleep, and diet data; it provides user-authorized health data interfaces for professional medical institutions to support clinical decision-making; it provides team health assessment and intervention programs for corporate health management; it provides student cardiopulmonary function assessment and training tools for school physical education; and it provides professional health assessment and training services for personnel in special working environments (such as high altitude, diving, and aviation). Partner with insurance companies to provide personalized health insurance plans based on health indices; build user communities and expert networks to promote knowledge sharing and experience exchange.

21. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes a computer program, it implements the method for quantitatively evaluating the state of the nervous system as described in any one of claims 1-20.

22. 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 quantitative assessment method of the health index as described in any one of claims 1-9.

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