Respiratory rehabilitation training guiding system

Abstract

The invention relates to the technical field of respiratory training, in particular to a respiratory rehabilitation training guide system which comprises a rhythm regulation and control module, a dynamic guide module, a structure adaptation module, a steady-state balance module and an execution path regulation and control module. According to the method, the airflow deviation value and the target rhythm deviation are extracted in real time, the airflow rate change trend and the response time characteristic can be accurately recognized, and therefore dynamic fluctuation in the breathing process is effectively controlled, a personalized guide scheme is provided, airflow distribution and the cavity movement law are optimized, continuous and stable training is ensured, and the training efficiency is improved. The airflow distribution and the cavity matching sequence are dynamically adjusted, steady-state gaps caused by airflow fluctuation are effectively filled, each link can achieve an ideal effect, the dependence of a traditional system on fixed frequency and proportion setting is exceeded, training is more flexible, accurate and efficient, especially when a patient is in a fatigue state, training guidance can be adjusted according to real-time feedback, and the training efficiency is improved. The rehabilitation effect is maximized, and the long-term stability is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of respiratory training, in particular to a respiratory rehabilitation training guidance system. BACKGROUND

[0002] The technical field of respiratory training involves guiding and regulating human respiration through scientific methods to improve respiratory system function, including the regulation of respiratory frequency, the control of inhalation and exhalation ratio, the training of respiratory depth, and the maintenance of respiratory rhythm. This technical field covers respiratory rehabilitation, sports training, relaxation training, and related auxiliary equipment and guidance methods, which are completed through sound prompts, image indications, rhythm control devices, and training methods based on physiological signal feedback for systematic guidance. Among them, the traditional respiratory rehabilitation training guidance system refers to a system that, in the process of respiratory rehabilitation, presents the rhythm curve of inhalation and exhalation to patients with weak self-breathing control ability through an image display screen, or provides audio signal rhythm through a metronome to guide patients to breathe according to the prompts. The traditional system sets fixed respiratory frequency and inhalation and exhalation time ratio as the basis and realizes the guidance of respiratory training of patients through single visual or auditory prompts.

[0003] The existing respiratory training guidance method relies on fixed respiratory frequency and inhalation and exhalation ratio settings, and is guided through single visual or auditory prompts. This method cannot be adjusted in real time according to the different needs of individuals in actual application, resulting in limited training effect. Since the respiratory capacity, airflow change, and rhythm characteristics of patients have individual differences, the traditional method cannot effectively reflect the differences and cannot achieve dynamic adaptation and precise adjustment. Especially when patients are in a state of fatigue, respiratory training is difficult to maintain stability and continuity. This fixed and single training method cannot meet the complex and variable rehabilitation needs, limiting its effectiveness in different clinical situations. SUMMARY

[0004] To solve the technical problems existing in the prior art, the present application provides a respiratory rehabilitation training guidance system. The technical solution is as follows: On the one hand, a respiratory rehabilitation training guidance system is provided, which comprises: The rhythm control module extracts airflow boundary parameters of key nodes according to the airflow dynamic change curve in the patient's breathing process, analyzes the influence of differential breathing stages on airflow stability, sorts out the airflow distribution and rhythm change relationship model, and obtains a set of rhythm optimization parameters; The dynamic guidance module extracts real-time airflow deviation and target rhythm deviation based on the set of rhythm optimization parameters, identifies airflow rate change trend and response time characteristics, quantifies dynamic fluctuation cumulative effect, induces dynamic guidance weight, and generates real-time airflow guidance data stream; The structural adaptation module extracts the frequency and key node positions of the respiratory cavity structure based on the real-time airflow guidance data stream, and performs hierarchical weighted analysis by combining the rhythmic characteristics of the airflow change process and the cavity motion law to generate a dynamic model of the respiratory cavity. Based on the dynamic model of the respiratory cavity, the steady-state equilibrium module sorts the cavity functions according to their priority and dynamic demand ratio, identifies the steady-state relationship between airflow distribution and key areas, adjusts the guidance sequence through steady-state node characteristics, and constructs a steady-state basic scheme for respiratory rehabilitation training.

[0005] As a further embodiment of the present invention, the rhythm optimization parameter set includes frequency threshold, stability factor, and boundary tolerance; the real-time airflow guidance data stream includes offset, delay value, and weight coefficient; the respiratory cavity dynamic model includes hierarchical structure, matching relationship, and adjustment factor; and the respiratory rehabilitation training steady-state basic scheme includes priority sequence, distribution nodes, and regulation rules.

[0006] As a further aspect of the present invention, the rhythm regulation module includes: The boundary parameter extraction submodule extracts the airflow boundary parameters of key nodes based on the dynamic change curve of airflow during the patient's breathing process, classifies the influencing factors of respiratory rate and depth, and generates a dynamic change distribution table. Based on the dynamic change distribution table, the stability analysis submodule analyzes the influence of breathing frequency and depth on airflow stability, calculates the stability adaptation value under different operating conditions, and generates a model of the relationship between airflow distribution and rhythm change. The adaptation table generation submodule organizes airflow distribution parameters based on the airflow distribution and rhythm change relationship model, analyzes the correspondence with key node boundaries, and generates a rhythm optimization parameter set.

[0007] As a further aspect of the present invention, the dynamic boot module includes: The dynamic offset extraction submodule extracts the real-time airflow offset value and the target rhythm deviation based on the rhythm optimization parameter set, records the airflow rate change trend and response time data, and generates a dynamic offset data table. The fluctuation accumulation quantification submodule quantifies the dynamic fluctuation accumulation effect based on the dynamic offset data table, analyzes the difference between fluctuation distribution and demand ratio, and generates a dynamic fluctuation accumulation effect table. The guidance weight summarization submodule extracts the dynamic offset magnitude and correction frequency based on the dynamic fluctuation cumulative effect table, filters high-frequency fluctuation segments and marks the offset direction, summarizes the dynamic guidance weight, and generates a real-time airflow guidance data stream.

[0008] As a further aspect of the present invention, the structural adaptation module includes: The cavity structure extraction submodule extracts the frequency and key node positions of the respiratory cavity structure based on the real-time airflow-guided data stream, classifies the cavity function priority data, and generates a cavity structure frequency table. The rhythm characteristic analysis submodule extracts the real-time airflow offset value and the target rhythm deviation based on the cavity structure frequency table, records the airflow rate change trend and response time, and obtains the rhythm state table. The hierarchical weighted analysis submodule performs a multidimensional comparison of cavity structure and airflow rhythm based on the rhythm state table, filters the cavity structure adaptation relationship, and generates a dynamic model of the respiratory cavity.

[0009] As a further aspect of the present invention, the frequency of the breathing cavity structure refers to the frequency of the breathing cavity structure obtained by performing a Fourier transform on the real-time airflow guidance data stream, obtaining the spectrum of the real-time airflow guidance data stream, and identifying the peak frequency from the spectrum. The key node location refers to the center of the cross-sectional area through which the airflow passes, determined by combining the spatial distribution characteristics of the real-time airflow guidance data stream with a preset respiratory tract anatomical coordinate system. The real-time airflow offset value and the target rhythm deviation refer to the Euclidean distance between the airflow center axis and the preset ideal center axis in the real-time airflow guidance data stream, which is used as the real-time airflow offset value. The difference between the respiratory cavity structure frequency and the preset target rhythm frequency is calculated to obtain the target rhythm deviation.

[0010] As a further aspect of the present invention, the steady-state equilibrium module includes: The priority ranking submodule extracts the peak position and boundary difference threshold of the cavity based on the dynamic model of the respiratory cavity, analyzes the functional contribution of the cavity under a unit boundary difference, and generates a cavity function priority ranking table. The steady-state distribution identification submodule extracts the steady-state change trajectory of the main channel node based on the cavity function priority ranking table, identifies the steady-state equilibrium point and offset direction, and generates a steady-state distribution relationship table. Based on the steady-state distribution relationship table, the steady-state adjustment submodule adjusts the guidance sequence by adjusting the steady-state node characteristics, extracts the frequency and amplitude sequence of steady-state changes of nodes, counts the offset amplitude and duration of nodes exceeding the steady-state limit, divides the stable interval and steady-state transition segment, and generates a steady-state basic scheme for respiratory rehabilitation training.

[0011] As a further aspect of the present invention, the peak position of the cavity refers to the spatial coordinate point where the airflow velocity reaches a local extreme value in the three-dimensional distribution data of the airflow velocity in the dynamic model of the respiratory cavity, and the spatial coordinate point is determined as the peak position of the cavity. The boundary difference threshold refers to setting a range of five to ten percent of the key dimensions of the cavity as the boundary difference threshold based on the geometric dimensions and structural characteristics of the cavity in the dynamic model of the respiratory cavity. The so-called steady-state equilibrium point refers to identifying the main channel node in the steady-state distribution relationship table, where the fluctuation amplitude of the airflow velocity and airflow pressure is less than the preset steady-state tolerance value within a continuous preset time period, and determining the center point of the area that meets the condition as the steady-state equilibrium point. The offset direction refers to the calculated three-dimensional Euclidean distance between the current position of the main channel node and the center position of the steady-state equilibrium point, and the offset direction is determined based on the direction vector of the distance.

[0012] As a further aspect of the present invention, the system also includes an execution path control module: The execution path control module, based on the respiratory rehabilitation training steady-state basic scheme, monitors the heart rate status and the execution status of key area boundaries during breathing, compares unmet boundary requirements and remaining capacity in real time, and fills steady-state gaps by adjusting the boundary distribution and cavity matching sequence, generating a global respiratory steady-state control execution scheme. The respiratory global homeostasis regulation execution scheme includes compensation sequence, matching path, and stability coefficient.

[0013] As a further aspect of the present invention, the execution path control module includes: The status monitoring submodule, based on the aforementioned respiratory rehabilitation training steady-state baseline scheme, collects heart rate node values ​​and key area boundary feedback, records jump times and deviation amplitudes, and generates a status monitoring data table. Based on the status monitoring data table, the demand comparison submodule extracts the time points corresponding to the unmet boundaries, identifies the remaining capacity and instantaneous gaps, matches the target gaps with the capacity segments, and generates a demand comparison result table. Based on the demand comparison result table, the dynamic adjustment submodule fills the steady-state gap by adjusting the boundary distribution and cavity matching order, identifies the capability gap nodes and response lag segments, updates the boundary output timing and cavity curves, and generates a global steady-state control execution plan for respiration.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By extracting airflow offset values ​​and target rhythm deviations in real time, the system can accurately identify airflow rate change trends and response time characteristics, thereby achieving effective control over dynamic fluctuations during respiration. This method provides personalized guidance for each breathing stage, fully considering airflow stability and rhythm changes, thus optimizing airflow distribution and cavity movement patterns during breathing training and ensuring continuous and stable training. By dynamically adjusting the sequence of airflow distribution and cavity matching, it effectively fills the steady-state gaps caused by airflow fluctuations during breathing training, ensuring that each stage of training achieves ideal results. This surpasses the reliance of traditional training systems on fixed frequency and ratio settings, making breathing training more flexible, precise, and efficient under varying training environments and individual differences. Especially after the patient enters a state of fatigue, the system can adjust training guidance based on real-time feedback, ensuring maximum and long-term stability of rehabilitation effects. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of a respiratory rehabilitation training guidance system provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention; Figure 3 This is a flowchart of the rhythm regulation module in this invention; Figure 4 This is a flowchart of the dynamic guidance module in this invention; Figure 5 This is a flowchart of the structural adaptation module in this invention; Figure 6 This is a flowchart of the steady-state equilibrium module in this invention; Figure 7 This is a flowchart of the execution path control module in this invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] This invention provides a respiratory rehabilitation training guidance system, such as... Figures 1-2 The diagram shown illustrates a respiratory rehabilitation training guidance system, which includes: The rhythm regulation module extracts airflow boundary parameters at key nodes based on the dynamic airflow change curve during the patient's breathing process, analyzes the impact of differentiated breathing stages on airflow stability, organizes the relationship model between airflow distribution and rhythm change, and obtains a set of rhythm optimization parameters. The dynamic guidance module extracts the real-time airflow offset value and the target rhythm deviation based on the rhythm optimization parameter set, identifies the airflow rate change trend and response time characteristics, quantifies the cumulative effect of dynamic fluctuations, summarizes the dynamic guidance weight, and generates a real-time airflow guidance data stream. The structural adaptation module extracts the frequency and key node positions of the respiratory cavity structure based on real-time airflow-guided data streams, and performs hierarchical weighted analysis by combining the rhythmic characteristics of airflow changes and the cavity motion law to generate a dynamic model of the respiratory cavity. The steady-state equilibrium module is based on the dynamic model of the respiratory cavity. It sorts the cavity functions according to their priority and dynamic demand ratio, identifies the steady-state relationship between airflow distribution and key areas, adjusts the guidance sequence through the characteristics of steady-state nodes, and constructs a steady-state basic scheme for respiratory rehabilitation training. The execution path control module is based on the steady-state basic scheme of respiratory rehabilitation training. It monitors the heart rate status and the execution status of key area boundaries during the breathing process, compares the unmet boundary requirements and remaining capacity in real time, and fills the steady-state gaps by adjusting the boundary distribution and cavity matching sequence, thereby generating a global steady-state control execution scheme for breathing.

[0023] The rhythm optimization parameter set includes frequency threshold, stability factor, and boundary tolerance; the real-time airflow guidance data stream includes offset, delay value, and weight coefficient; the respiratory cavity dynamic model includes hierarchical structure, matching relationship, and regulation factor; the respiratory rehabilitation training steady-state basic scheme includes priority sequence, distribution node, and regulation rule; and the respiratory global steady-state regulation execution scheme includes compensation sequence, matching path, and stability coefficient.

[0024] Specifically, such as Figure 2 , 3 As shown, the rhythm regulation module includes: The boundary parameter extraction submodule extracts the airflow boundary parameters of key nodes based on the dynamic change curve of airflow during the patient's breathing process, classifies the influencing factors of respiratory rate and depth, and generates a dynamic change distribution table. Real-time airflow data from patients was continuously collected using respiratory physiological monitoring equipment to obtain instantaneous airflow rate values, forming an airflow rate time series. Key airflow boundary parameters were identified and extracted. These key nodes included the inspiratory start point, inspiratory peak point, expiratory start point, expiratory peak point, and expiratory end point of each respiratory cycle. The inspiratory peak point was identified as the local maximum value before the airflow rate changed from positive to negative, and the expiratory peak point was identified as the local minimum value before the airflow rate changed from negative to positive. The inspiratory start point and expiratory end point were identified as the points when the airflow rate first exceeded 0.05 L / s or dropped below 0.05 L / s. The expiratory start point was identified as the point when the airflow rate dropped below 0.05 L / s and remained below 0.05 L / s for 0.1 seconds. In one respiratory cycle, the inspiratory peak airflow rate was extracted as 0.8 L / s, and the expiratory peak airflow rate was -0.7 L / s. The exhalation time was 1.5 seconds, the expiratory time was 2.5 seconds, and the tidal volume was 500 mL. Respiratory rate and depth influencing factors were categorized based on the extracted airflow boundary parameters. The respiratory rate influencing factor was calculated based on the total respiratory time; when the total respiratory time was 4.0 seconds, the respiratory rate was 15 breaths / minute. The respiratory depth influencing factor was assessed based on tidal volume and peak airflow rate. Physiological parameters such as patient position, activity level, and airway resistance were classified as external or internal factors affecting respiratory rate or depth, and each influencing factor was assigned an influence weight. For example, the influence weight of increased airway resistance on respiratory depth was 0.7. The parameters and classification information were compiled to generate a dynamic change distribution table. This table contains the key airflow boundary parameters for each respiratory cycle, the calculated respiratory rate and depth, and the corresponding physiological influencing factors and their weights, serving as input for subsequent analysis.

[0025] The stability analysis submodule analyzes the impact of breathing frequency and depth on airflow stability based on the dynamic change distribution table, calculates the stability adaptation value under different operating conditions, and generates a model of the relationship between airflow distribution and rhythm changes. The stability adaptation value under different operating conditions is calculated using the following formula: ; in, This represents the stability adaptation value under different operating conditions. This represents the breathing rate under the i-th working condition. Represents the reference respiratory rate. This represents the stability influencing factor under the i-th operating condition. It is an index representing the stability influencing factor. Represents the total number of operating conditions; Respiratory rate and depth data of patients under different physiological states (such as resting and mild activity) were extracted from the dynamic variation distribution table and compared with the preset stable breathing mode (such as a resting respiratory rate of 12-18 breaths / minute and a tidal volume of 400-600 mL for healthy adults). Deviating respiratory conditions from the stable mode were identified, and the degree of deviation was quantified. Then, the stability fit value under the different conditions was calculated. ,in, This represents the stability adaptation value under different operating conditions, with a value ranging from 0 to 10. The smaller the value, the better the stability. Representing the The respiratory rate under each working condition is expressed in breaths per minute. For example, the patient's respiratory rate is 18 breaths per minute under mild activity condition 1 and 22 breaths per minute under condition 2. This represents a reference respiratory rate. For example, based on a patient's age, height, weight, and health condition, a reference respiratory rate of 15 breaths per minute is set. This value is determined through statistical analysis of physiological data from a large number of healthy individuals, combined with clinical guidelines, to ensure that patients are at a stable breathing rate in most cases. The rationality of this value has been verified by multiple clinical studies. For instance, a study of 300 healthy adults showed that the average respiratory rate at rest was 14.8 breaths per minute, with a standard deviation of 1.2 breaths per minute. Therefore, 15 breaths per minute is set as a general reference value. Alternatively, for a specific patient, the median respiratory rate can be used as their personalized reference value by continuously monitoring their respiratory rate for 7 days in a healthy state. Representing the The stability influencing factor under each operating condition is a comprehensive parameter reflecting the fluctuation of airflow boundary parameters such as breathing depth, tidal volume, peak inspiratory flow rate, and peak expiratory flow rate under that operating condition. Its value ranges from 0.5 to 2.0, with a larger value indicating greater fluctuation. For example, this factor is obtained by calculating the weighted average of the ratios of the standard deviations to the average values ​​of tidal volume, peak inspiratory flow rate, and peak expiratory flow rate under each operating condition. If, under a certain operating condition, the standard deviation of tidal volume is 50 mL, the average tidal volume is 500 mL, the standard deviation of peak inspiratory flow rate is 0.1 L / s, the average peak inspiratory flow rate is 0.8 L / s, the standard deviation of peak expiratory flow rate is 0.1 L / s, the average peak expiratory flow rate is -0.7 L / s, and the weights of tidal volume, peak inspiratory flow rate, and peak expiratory flow rate are set to 0.4, 0.3, and 0.3, respectively, then... ; here The value should represent the normalized relative stability effect. If normalization is based on 0.1203, the value would be around 0.8. This is an index of the stability impact factor, used to adjust for the impact of the stability impact factor on... The influence weight of the value, ranging from 1.5 to 2.5, is set to 2.0. This value has been verified through multiple rounds of experiments to ensure that it can effectively distinguish different degrees of stability differences. For example, in multiple sets of simulated respiratory data, by... The stability fit was adjusted from 1.0 to 3.0, and the correlation between the stability fit value and the actual degree of respiratory instability was observed. The correlation is optimal at this time, therefore it is set to 2.0. This represents the total number of operating conditions. For example, this article exemplifies two operating conditions, therefore... ; The advantage of the formula lies in the fact that by introducing the relative deviation between the respiratory rate and the reference rate, and combining it with the power-weighted sum of stability influencing factors, the stability fit value can comprehensively reflect the degree of deviation of the respiratory rate and the fluctuation of airflow parameters, thereby more accurately quantifying the overall stability of the respiratory system. The following are examples of stability fit value calculation under two operating conditions: Operating Condition 1 ( =18 times / minute =1.2), Working condition 2 ( =22 times / minute =1.8), reference respiratory rate =15 times / minute =2.0, ; ; ; ; The results indicate that the average stability fit value for patients under the two different operating conditions was 0.9, indicating a certain degree of instability in their respiratory rhythm, especially showing a significant deviation in operating condition 2. This value, as a key output of the airflow distribution and rhythm variation relationship model, was further used to evaluate and adjust the breathing pattern. By calculating the stability fit values ​​under multiple different operating conditions and combining the stability performance of respiratory rate and depth under different combinations, an airflow distribution and rhythm variation relationship model was generated. This model is a multidimensional lookup table or regression equation that maps different combinations of respiratory rate and depth to corresponding stability fit values. For example, at a rate of 12 breaths / minute and a tidal volume of 500 mL... At a frequency of 20 breaths / minute and a tidal volume of 650 mL This clearly demonstrates the airflow stability under different rhythm combinations.

[0026] The adaptation table generation submodule is based on the relationship model between airflow distribution and rhythm change. It organizes airflow distribution parameters, analyzes their correspondence with key node boundaries, and generates a set of rhythm optimization parameters. Based on the relationship model between airflow distribution and rhythm variation, respiratory rate and depth combinations with stability fit values ​​below 0.3 were selected from the model. Parameters such as peak inspiratory flow rate, peak expiratory flow rate, inspiratory time, expiratory time, and tidal volume corresponding to these combinations were extracted. Optimized airflow distribution parameters included, for example, a respiratory rate of 14 breaths / min, a tidal volume of 520 mL, a peak inspiratory flow rate of 0.9 L / s, a peak expiratory flow rate of -0.8 L / s, an inspiratory time of 1.8 seconds, and an expiratory time of 2.5 seconds. The correspondence with key node boundaries was analyzed. Key node boundaries are preset physiological thresholds to ensure the healthy operation of the respiratory system: peak inspiratory flow rate should be between 0.6 and 1.0 L / s, peak expiratory flow rate should be between -0.9 and -0.6 L / s, and inspiratory time should be between 1.5 and 2.0 seconds. The inhalation time should be between 2.0 and 3.0 seconds. The optimized airflow distribution parameters are compared one by one with the preset key node boundaries to determine whether each parameter falls within its corresponding healthy physiological range. If the peak inhalation flow rate of 0.9 L / s falls within the boundary of 0.6-1.0 L / s, the parameter is considered to be in line with the boundary. If the exhalation time of 2.5 seconds falls within the boundary of 2.0-3.0 seconds, it is also considered to be in line with the boundary. If the parameter exceeds the boundary, an adjacent parameter combination with a lower fit value is selected for re-comparison until all parameters are in line with the boundary. The airflow distribution parameters that have been sorted and verified are summarized to generate a rhythm optimization parameter set. This parameter set contains a set of optimized respiratory rate, respiratory depth, and key airflow boundary parameters, which serve as the target rhythm in the patient's respiratory rehabilitation training.

[0027] Specifically, such as Figure 2 , 4 As shown, the dynamic boot module includes: The dynamic offset extraction submodule extracts the real-time airflow offset value and the target rhythm deviation based on the rhythm optimization parameter set, records the airflow rate change trend and response time data, and generates a dynamic offset data table. Based on a rhythm optimization parameter set, real-time airflow data of the patient is continuously monitored. Instantaneous airflow rate is acquired through a differential pressure flow sensor. The real-time airflow rate is compared in real time with the target airflow waveform generated based on the rhythm optimization parameter set. The real-time airflow offset value and the target rhythm deviation are extracted. The real-time airflow offset value is obtained by calculating the difference between the current instantaneous airflow rate and the instantaneous airflow rate at the corresponding moment of the target airflow waveform. The target inspiratory peak is 0.88 L / s, the real-time inspiratory peak is 0.80 L / s, and the real-time airflow offset value is -0.08 L / s. The target rhythm deviation is obtained by comparing the difference between the real-time respiratory rate and the target respiratory rate, as well as the difference between the real-time tidal volume and the target tidal volume. The real-time respiratory rate is 15 breaths / min, the target respiratory rate is 14 breaths / min, and the respiratory rate deviation is +1 breaths / min. The system measures airflow rate at a rate of 480 mL / min, with a target tidal volume of 520 mL and a tidal volume deviation of -40 mL. It records the trend of airflow rate changes and response time data. The airflow rate trend is obtained by calculating the first derivative of the real-time airflow rate time series to acquire acceleration or deceleration information. Response time data refers to the time required from detecting a deviation of the patient's real-time airflow from the target rhythm to the airflow returning to the target rhythm range. For example, if a peak inspiratory deviation of 0.08 L / s is detected, and the patient adjusts back to the target range within 3 seconds, the response time is 3 seconds. The real-time monitoring and calculation data are integrated to generate a dynamic deviation data table. This table contains the real-time airflow deviation value, target rhythm deviation, airflow rate trend, and corresponding response time for each timestamp, used for subsequent fluctuation quantification analysis.

[0028] The volatility accumulation quantification submodule quantifies the dynamic volatility accumulation effect based on the dynamic offset data table, analyzes the difference between volatility distribution and demand ratio, and generates a dynamic volatility accumulation effect table. Based on the dynamic offset data table, within a preset sliding time window, the absolute values ​​of all instantaneous airflow offset values ​​and target rhythm deviations are accumulated to obtain the total fluctuation within that time window. For example, within a 5-second window, the cumulative effect after accumulating the airflow offset value sequence is 0.21 L / s. The fluctuation distribution and demand ratio difference are analyzed. The fluctuation distribution analysis identifies the periods of frequent fluctuations and the types of fluctuations with large amplitudes by statistically analyzing the cumulative effect. The demand ratio difference is calculated by comparing the difference between the patient's actual respiratory demand and the target respiratory demand set by the rhythm optimization parameter set, and quantifying the impact of the difference on the fluctuation cumulative effect. For example, if the patient's actual oxygen consumption is 15% higher than the target, it indicates an increased respiratory demand, leading to a larger rhythm deviation and a higher fluctuation cumulative effect. The analysis results are integrated to generate a dynamic fluctuation cumulative effect table. This table includes the total value of the fluctuation cumulative effect within each time window, key fluctuation types, fluctuation distribution characteristics, and correlation with the respiratory demand ratio difference, providing a data foundation for subsequent weight induction.

[0029] The guidance weight summarization submodule extracts the dynamic offset magnitude and correction frequency based on the dynamic fluctuation cumulative effect table, filters high-frequency fluctuation segments and marks the offset direction, summarizes the dynamic guidance weight, and generates a real-time airflow guidance data stream. Based on the dynamic fluctuation cumulative effect table, the dynamic offset magnitude and correction frequency are extracted. The dynamic offset magnitude is the total value of the cumulative effect of fluctuations recorded in the cumulative effect table. The correction frequency is the number of guided or patient-initiated adjustments made to correct fluctuations within a specific time window. High-frequency fluctuation segments are screened and their offset directions are marked. A high-frequency fluctuation segment is defined as a segment where the correction frequency exceeds a preset threshold within a continuous 10-second time window. The offset direction is marked by analyzing the key fluctuation types recorded in the cumulative effect table. For example, if the inspiratory peak value is consistently lower than the target value, it is marked as "insufficient inspiratory offset direction". Dynamic guidance weights are summarized. The guidance weight is a value between 0 and 1, representing the urgency and intensity of guiding the current fluctuation segment. For the screened high-frequency fluctuation segments, different weight values ​​are assigned according to the dynamic offset magnitude, correction frequency, and offset direction. If the dynamic offset... A higher guidance weight is assigned to a larger displacement magnitude, a higher correction frequency, and a consistently stable offset direction; conversely, a lower weight is assigned to a smaller displacement magnitude. For example, if the dynamic offset magnitude of a certain segment is 0.25 L / s, the correction frequency is 6 times every 10 seconds, and the offset direction is a continuous "insufficient inspiration," the guidance weight is calculated as follows: the normalized value of the magnitude multiplied by 0.6, plus the normalized value of the frequency multiplied by 0.4. Assuming the magnitude of 0.25 L / s is normalized to 0.7 and the frequency of 6 times every 10 seconds is normalized to 0.8, the guidance weight is calculated as 0.7 multiplied by 0.6 plus 0.8 multiplied by 0.4, resulting in a final value of 0.42 plus 0.32, which equals 0.74. The summarized dynamic guidance weight is integrated with the offset information to generate a real-time airflow guidance data stream. This data stream includes a real-time timestamp, the corresponding guidance weight, the specific offset direction, and the suggested correction intensity, guiding the patient to adjust their breathing in real time.

[0030] Specifically, such as Figure 2 , 5 As shown, the structural adaptation module includes: The cavity structure extraction submodule extracts the frequency and key node positions of respiratory cavity structures based on real-time airflow-guided data streams, categorizes cavity function priority data, and generates a cavity structure frequency table. The respiratory cavity structure frequency refers to the frequency of the respiratory cavity structure obtained by performing a Fourier transform on the real-time airflow guidance data stream and identifying the peak frequency from the spectrum. The key node location refers to the location of the center of the cross-sectional area through which the airflow passes, determined by the spatial distribution characteristics of the real-time airflow guidance data stream and the preset respiratory anatomical coordinate system. Based on real-time airflow guidance data stream, the frequency of respiratory cavity structures and the location of key nodes are extracted. The frequency of respiratory cavity structures is determined by performing a Fourier transform on the real-time airflow guidance data stream to obtain its spectrum. Peak frequencies are identified from the spectrum. Peak frequencies are determined by setting an amplitude threshold; all frequency points exceeding this threshold are identified, and the frequency with the largest amplitude is selected as the main peak frequency. If the spectrum analysis shows an energy peak at 0.2 Hz, it is identified as a respiratory cavity structure frequency. Key node locations are determined based on the spatial distribution characteristics of the real-time airflow guidance data stream, combined with a preset respiratory tract anatomical coordinate system. The center of the cross-sectional area through which the airflow passes is identified as the key node location. The preset respiratory tract anatomical coordinate system includes key respiratory tract structures such as the trachea, bronchi, and alveoli. If the guidance command points to "increase airflow in the upper respiratory tract," then the key node location is determined in the trachea. Within the region, the three-dimensional distribution of airflow rate is analyzed to identify the area with the highest airflow rate. The geometric center of this area is calculated as the critical node location. For example, in the anatomical coordinate system, the cross-sectional diameter of the middle section of the trachea is 18 mm, and its geometric center coordinates are (0, 0, 100). If the guiding data indicates that the airflow critically passes through this area, then (0, 0, 100) is determined as the critical node location. At the same time, the cavity function priority data is classified. The cavity function priority is a numerical ranking of the importance of different cavity structures in respiratory function. The trachea, as the main airway, has a functional priority of 1, the left and right main bronchi have a priority of 2, and the bronchioles have a priority of 3. The priority is set based on respiratory physiology and clinical experience. The information is integrated to generate a cavity structure frequency table, which includes the identified respiratory cavity structures, the corresponding structure frequency, the critical node location, and the functional priority.

[0031] The rhythm characteristic analysis submodule extracts the real-time airflow offset value and the target rhythm deviation based on the cavity structure frequency table, records the airflow rate change trend and response time, and obtains the rhythm state table. The real-time airflow offset value and the target rhythm deviation refer to the Euclidean distance between the airflow center axis and the preset ideal center axis in the real-time airflow guidance data stream as the real-time airflow offset value. The difference between the respiratory cavity structure frequency and the preset target rhythm frequency is calculated to obtain the target rhythm deviation. Combining the structural frequency information of each cavity in the cavity structure frequency table, the real-time airflow offset value and the target rhythm deviation are extracted. The real-time airflow offset value refers to the Euclidean distance between the airflow center axis and the preset ideal center axis in the real-time airflow guidance data stream. The actual spatial position of the airflow center axis of each key node in the three-dimensional anatomical coordinate system is tracked in real time. For example, at a certain moment, the centroid of the airflow distribution in the tracheal cross section is located at (0.2mm, 0.1mm, 0mm) relative to its anatomical center, and the ideal center axis is at (0mm, 0mm, 0mm) at the anatomical center. Then, the real-time airflow offset value is calculated as the square root of (0.2 minus 0 squared plus 0.1 minus 0 squared plus 0 minus 0 squared), which is approximately 0.22mm. This value represents the degree of physical deviation of the airflow path. The target rhythm deviation refers to the difference between the respiratory cavity structure frequency and the preset target rhythm frequency. The calculation was performed with the target rhythm frequency set at 0.25Hz. The target rhythm deviation was calculated as the absolute difference between 0.20Hz and 0.25Hz, resulting in 0.05Hz. Simultaneously, the airflow rate change trend and response time were recorded. The airflow rate change trend refers to the determination of whether the airflow is accelerating, decelerating, or remaining stable at each critical node of the cavity by performing real-time differential calculations on the instantaneous airflow rate. The response time refers to the time required from detecting a deviation in the airflow inside the cavity to the airflow inside the cavity returning to the normal range. For example, after guidance, if the central axis of the airflow in the trachea returns from a deviation of 0.22mm to within 0.05mm within 2 seconds, the response time is 2 seconds. The real-time data and calculation results were compiled to obtain a rhythm status table, which details the real-time airflow deviation value, target rhythm deviation, airflow rate change trend, and response time for each respiratory cavity.

[0032] The hierarchical weighted analysis submodule performs multidimensional comparison between cavity structure and airflow rhythm based on the rhythm state table, filters cavity structure adaptation relationships, and generates a dynamic model of the respiratory cavity. The rhythmic characteristic parameters, such as real-time airflow offset, target rhythm deviation, airflow rate change trend, and response time, from the rhythm status table are correlated with structural parameters, such as cavity function priority, structural frequency, and key node location, from the cavity structure frequency table. For the tracheal cavity, the priority is 1, the airflow offset is 0.22 mm, the target rhythm deviation is 0.05 Hz, and the response time is 2 seconds. These parameters are treated as multi-dimensional vectors for comprehensive evaluation. A scoring mechanism is set: 1 point is deducted for every 0.1 mm increase in airflow offset, 1 point is deducted for every 0.01 Hz increase in rhythm deviation, and 1 point is deducted for every 0.5 seconds increase in response time. The higher the cavity function priority, the greater the deduction weight. For example, the deduction for a cavity with priority 1 is multiplied by 1.5. Cavity structure fit relationships are screened, and the fit relationship is judged by setting a total score threshold. If the total score is lower than a certain threshold, the fit relationship is considered less favorable. A cavity score of 5 is considered "good fit," 5-10 is considered "moderate fit," and above 10 is considered "poor fit." The total score for the tracheal cavity is calculated as follows: airflow deviation value 0.22mm divided by 0.1mm and multiplied by 1.5, plus rhythm deviation 0.05Hz divided by 0.01Hz and multiplied by 1.5, plus response time 2 seconds divided by 0.5 seconds and multiplied by 1.5. The result is 3.3 plus 7.5 plus 6, for a total score of 16.8. This cavity is considered "poor fit." Key factors leading to poor fit are identified, and the compared and screened fit relationships are integrated to generate a dynamic model of the respiratory cavities. This model displays the current fit status of each respiratory cavity structure in a three-dimensional visualization and dynamically updates its airflow parameters and rhythm characteristics, providing an intuitive and comprehensive view of the respiratory system's operating status.

[0033] Specifically, such as Figure 2 , 6 As shown, the steady-state equilibrium module includes: The priority ranking submodule is based on the dynamic model of the respiratory cavity, extracts the peak position and boundary difference threshold of the cavity, analyzes the functional contribution of the cavity under a unit boundary difference, and generates a priority ranking table of cavity functions. The peak position of the cavity refers to the spatial coordinate point where the airflow velocity reaches a local extreme value in the three-dimensional distribution data of the airflow velocity in the dynamic model of the respiratory cavity, and the spatial coordinate point is determined as the peak position of the cavity. Boundary difference threshold refers to setting a range of 5% to 10% of the critical dimensions of the cavity as the boundary difference threshold based on the geometric dimensions and structural characteristics of the cavity in the dynamic model of the respiratory cavity. The peak position of the respiratory cavity refers to the spatial coordinates of the point where the airflow velocity reaches a local extreme value in the three-dimensional distribution data of the airflow velocity in the dynamic model of the respiratory cavity. This local extreme value is determined by comparing it with its neighboring points; if the velocity value at this point is greater than the velocity values ​​of all its neighboring points, it is identified as the peak position of the cavity. For example, there may be a local maximum value of 0.95 L / s in the central region of the trachea, with spatial coordinates (0, 0, 105) mm. This point is the peak position of the cavity. The boundary difference threshold refers to setting a range of 5% to 10% of the critical dimensions of the cavity as the boundary difference threshold based on the geometric dimensions and structural characteristics of the cavity in the dynamic model of the respiratory cavity. For the trachea, with a typical inner diameter of 18 mm, the boundary difference threshold can be set to 7%, which is 18 mm multiplied by 0.07, resulting in a value of 1.26. mm is a threshold used to quantify the acceptable range of structural or functional deviations. It analyzes the functional contribution of a cavity per unit boundary difference. The contribution is calculated by combining the cavity's functional priority with its airflow efficiency loss and structural deviation in the dynamic model of the respiratory cavity. For example, if the actual inner diameter of the trachea is 1.0 mm smaller than the ideal inner diameter, the functional priority is 1, and the airflow efficiency loss is 0.1, then the functional contribution per unit boundary difference is calculated as follows: priority coefficient 5 multiplied by efficiency loss ratio 0.1, then divided by the actual boundary difference of 1.0 mm, resulting in a contribution of 0.5 per mm. The higher the value, the greater the impact of the cavity on the overall respiratory function per unit structural or functional deviation. The information is integrated to generate a cavity functional priority ranking table. This table lists the peak position, boundary difference threshold, and functional contribution per unit boundary difference for each cavity, and sorts them from high to low according to the contribution.

[0034] The steady-state distribution identification submodule extracts the steady-state change trajectory of the main channel node based on the cavity function priority ranking table, identifies the steady-state equilibrium point and offset direction, and generates a steady-state distribution relationship table. Identifying the steady-state equilibrium point means identifying the center point of the area that meets the condition in the steady-state distribution table, where the fluctuation amplitude of the airflow velocity and airflow pressure at the main channel node is less than the preset steady-state tolerance value within a continuous preset time period. The offset direction is determined by calculating the three-dimensional Euclidean distance between the current position of the main channel node and the center position of the steady-state equilibrium point, and the offset direction is determined based on the direction vector of the distance. Based on the cavity function priority ranking table, the steady-state change trajectory of the main channel nodes is extracted. The main channel nodes refer to the cavities with the highest functional contribution. Airflow velocity and pressure data of the main channel nodes are continuously monitored, and their trajectories over time are recorded. For example, at the trachea node, real-time monitoring shows airflow velocity fluctuating between 0.75 L / s and 0.85 L / s, and airflow pressure fluctuating between 4.8 cmH2O and 5.2 cmH2O. The steady-state equilibrium point and offset direction are identified. The steady-state equilibrium point is identified in the steady-state distribution table. Within a continuously preset time period, the fluctuation amplitudes of airflow velocity and airflow pressure at the tracheal node are both less than preset steady-state tolerance values. The center point of the area meeting these conditions is determined as the steady-state equilibrium point. The preset steady-state tolerance values ​​are determined through statistical analysis of physiological fluctuations in healthy subjects at rest. The airflow velocity tolerance is set to ±0.05 L / s, and the airflow pressure tolerance is set to ±0.2 cmH2O. The continuously preset time period is set to 5 seconds. If, during 5 seconds of continuous monitoring, the airflow velocity fluctuation amplitude at the tracheal node is 0.04 L / s, and the airflow pressure fluctuation amplitude is... If the H2O concentration is 0.2 cmH2O, and all values ​​are within the tolerance range, then this time period is considered steady state. The average velocity and pressure within this time period (e.g., velocity 0.80 L / s, pressure 5.0 cmH2O) is taken as the steady-state equilibrium point. The offset direction refers to the calculated three-dimensional Euclidean distance between the current position of the main channel node and the center position of the steady-state equilibrium point, and the offset direction is determined based on the direction vector of the distance. The current position of the node refers to the instantaneous state point of airflow velocity and pressure in multi-dimensional space, and the center position of the steady-state equilibrium point refers to the identified steady-state equilibrium point. The Euclidean distance... The distance is calculated as the square root of (0.70 minus 0.80 squared plus 4.5 minus 5.0 squared), resulting in approximately 0.51. The direction vector points from the equilibrium point to the current point. For example, the direction from (0.80, 5.0) to (0.70, 4.5) is the vector direction indicating "low speed and low pressure". The equilibrium point and offset direction information are integrated to generate a steady-state distribution relationship table. This table records in detail the steady-state equilibrium point of each main channel node, the Euclidean distance between the current state and the equilibrium point, and the specific offset direction, providing a basis for subsequent steady-state adjustments.

[0035] The steady-state adjustment submodule is based on the steady-state distribution relationship table. It adjusts the guidance sequence by adjusting the steady-state node characteristics, extracts the frequency and amplitude sequence of steady-state changes of nodes, counts the offset amplitude and duration of nodes exceeding the steady-state limit, divides the stable interval and steady-state transition segment, and generates a steady-state basic plan for respiratory rehabilitation training. Based on the steady-state distribution table, the characteristics of steady-state nodes include the distance of the node from the steady-state equilibrium point, the direction of offset, and the functional priority. Nodes with large deviations, offset directions that are detrimental to stability, and high functional priorities are prioritized for adjustment. For example, if the steady-state distribution table shows that the "low velocity, low pressure" offset distance of the trachea node is 0.51 and has the highest functional priority, then the trachea adjustment guidance sequence is set to the highest priority. The frequency and amplitude sequences of steady-state changes of nodes are extracted. The number of times each main channel node deviates from its steady-state equilibrium point and exceeds the minimum fluctuation threshold is continuously monitored and recorded as the change frequency, along with the amplitude sequence of each deviation. The offset amplitude and duration of steady-state over-limit nodes are statistically analyzed. A steady-state over-limit node refers to a node whose airflow velocity or pressure fluctuation amplitude exceeds the preset steady-state tolerance. The system records the maximum offset amplitude at each time an over-limit occurs and calculates the duration of the offset. It then divides the system into stable intervals and steady-state transition segments. A stable interval is defined as a period within which the frequency and amplitude of steady-state changes at all main channel nodes are below the "instability threshold," and no steady-state over-limit events occur. A steady-state transition segment is defined as a period where steady-state over-limit events occur or the frequency / amplitude of changes exceeds the threshold. For example, 00:00:00-00:00:30 is a stable interval, and 00:00:30-00:00:45 is a steady-state transition segment. Based on the analysis results, a basic steady-state plan for respiratory rehabilitation training is generated. This plan includes specific training instructions and adjustment strategies for different steady-state intervals, providing guidance for subsequent execution path control.

[0036] Specifically, such as Figure 2 , 7 As shown, the execution path control module includes: The status monitoring submodule is based on the steady-state basic scheme of respiratory rehabilitation training. It collects heart rate node values ​​and key area boundary feedback, records jump times and deviation amplitudes, and generates a status monitoring data table. Based on the steady-state baseline protocol for respiratory rehabilitation training, heart rate node values ​​and key area boundary feedback are collected. Heart rate node values ​​are acquired in real-time using an ECG sensor worn on the patient's chest to obtain the current heart rate value, for example, 75 beats / minute. Key area boundary feedback refers to the real-time monitoring of physical boundary parameters of specific key areas in the rehabilitation training protocol using miniature pressure sensors or external optical sensing technology. For example, the real-time internal diameter of the trachea is 17.5 mm, and the real-time airflow pressure of the left and right main bronchi is 5.2 cmH2O. Real-time data are compared with the target values ​​or thresholds set in the rehabilitation protocol, and the jump time and deviation magnitude are recorded. The jump time refers to a rapid and significant increase in heart rate or key area boundary feedback data exceeding a preset threshold within a short period of time. The time points of change, such as heart rate changes exceeding 10 beats / minute, tracheal diameter changes exceeding 1.0 mm, and bronchial pressure changes exceeding 0.5 cmH2O, are recorded as jump moments if the heart rate rises from 75 beats / minute to 90 beats / minute within 3 seconds. The deviation amplitude refers to the difference between the heart rate or boundary parameter and the benchmark or target value set in the training program at the jump moment. For example, if the target heart rate is 70 beats / minute, the heart rate deviation amplitude is 20 beats / minute; if the target tracheal diameter is 18.0 mm, the tracheal diameter deviation amplitude is -0.5 mm. The collected heart rate, boundary feedback data, and the identified jump moments and deviation amplitudes are integrated to generate a status monitoring data table, providing real-time status information for subsequent demand comparisons.

[0037] The demand comparison submodule extracts the time points corresponding to the unmet boundaries based on the status monitoring data table, identifies the remaining capacity and instantaneous gaps, matches the target gaps with the capacity segments, and generates a demand comparison result table. Based on the status monitoring data table, the unmet boundary refers to the time point where the feedback value of the critical area boundary deviates significantly from the ideal or target boundary value set in the steady-state baseline plan for respiratory rehabilitation training. For example, if the status monitoring data table shows that the tracheal diameter is 17.5 mm at 00:01:30, which deviates from the target of 18.0 mm by -0.5 mm, then 00:01:30 is marked as the time point corresponding to the unmet boundary. Residual capacity and transient gaps are identified. Residual capacity refers to the physiological reserve that the patient can still mobilize to correct respiratory rhythm or cavity structure problems under the current physiological state. By monitoring the difference between the patient's maximum inspiratory pressure, maximum expiratory pressure, and current actual respiratory pressure during training, the residual function capacity of the respiratory muscles is assessed. If the maximum inspiratory pressure is 80 cmH2O and the current inspiratory pressure is 20 cmH2O, then the residual capacity is... The capacity is 60 cmH2O. The instantaneous gap refers to the specific quantitative difference between the patient's actual state and the target state at the time point corresponding to the unmet boundary. For example, at 00:01:30, the instantaneous gap in tracheal diameter is 0.5 mm. Matching target gaps with capacity segments, the target gap refers to all identified instantaneous gaps, and the capacity segment refers to the part of the patient's remaining capacity that can effectively cope with a specific gap. Matching is performed according to the type of gap and the remaining capacity. For example, if there is a structural gap of 0.5 mm in tracheal diameter, it is checked whether the patient's remaining respiratory muscle capacity and lung volume reserve are sufficient to temporarily expand the airway through deeper or more powerful breathing. If there is 60 cmH2O of inspiratory muscle remaining capacity, it is considered that the gap can be matched. The comparison results are integrated to generate a demand comparison result table, which provides a basis for subsequent dynamic adjustments.

[0038] The dynamic adjustment submodule fills steady-state gaps by adjusting the boundary distribution and cavity matching order based on the demand comparison result table, identifies capacity gap nodes and response lag segments, updates the boundary output timing and cavity curves, and generates a global steady-state respiratory regulation execution plan. Based on the demand comparison results table, steady-state gaps are filled by adjusting the boundary distribution and cavity matching order. A steady-state gap refers to an instantaneous gap in the demand comparison results table that cannot be effectively matched or filled by the patient's current remaining capacity. According to the nature and severity of the steady-state gap, the boundary distribution and cavity matching order in the global respiratory homeostasis control execution plan are dynamically adjusted to alleviate current respiratory instability to the greatest extent possible. For example, the target value of the tracheal diameter is adjusted to 17.8 mm in the short term, and patients are prioritized to perform diaphragmatic breathing training to strengthen inspiratory muscle strength. Capacity gap nodes and response lag segments are identified. Capacity gap nodes refer to locations in the demand comparison results table where specific cavities or functional areas cannot effectively fill the gap due to insufficient capacity. Response lag segments refer to times when the patient's physiological response is significantly slower than the preset response after receiving the guidance instruction. The time period of the threshold is defined. For example, if a patient receives guidance to "increase inspiratory flow" and the airflow rate does not change significantly within 3 seconds, this 3 seconds is identified as a response lag. The boundary output timing and cavity curve are updated. The boundary output timing refers to the real-time adjustment of the issuance time, duration, and repetition frequency of various guidance instructions in the global steady-state control execution plan based on the identification results of the capacity gap node and the response lag. The cavity curve refers to the curve of the ideal airflow velocity or pressure changing with time for a specific cavity. According to the patient's current state and ability, the target curve is dynamically smoothed or adjusted to make it more in line with the patient's actual achievable range. After the dynamic adjustment of instructions, timing, and curves are integrated, a global respiratory steady-state control execution plan is generated to ensure that the patient is gradually guided to achieve the best respiratory steady state within the patient's existing ability range.

[0039] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A respiratory rehabilitation training guidance system, characterized in that, The system includes: The rhythm regulation module extracts airflow boundary parameters at key nodes based on the dynamic airflow change curve during the patient's breathing process, analyzes the impact of differentiated breathing stages on airflow stability, organizes the relationship model between airflow distribution and rhythm change, and obtains a set of rhythm optimization parameters. Based on the rhythm optimization parameter set, the dynamic guidance module extracts the real-time airflow offset value and the target rhythm deviation, identifies the airflow rate change trend and response time characteristics, quantifies the cumulative effect of dynamic fluctuations, summarizes the dynamic guidance weight, and generates a real-time airflow guidance data stream. The structural adaptation module extracts the frequency and key node positions of the respiratory cavity structure based on the real-time airflow guidance data stream, and performs hierarchical weighted analysis by combining the rhythmic characteristics of the airflow change process and the cavity motion law to generate a dynamic model of the respiratory cavity. Based on the dynamic model of the respiratory cavity, the steady-state equilibrium module sorts the cavity functions according to their priority and dynamic demand ratio, identifies the steady-state relationship between airflow distribution and key areas, adjusts the guidance sequence through steady-state node characteristics, and constructs a steady-state basic scheme for respiratory rehabilitation training.

2. The respiratory rehabilitation training guidance system according to claim 1, characterized in that: The rhythm optimization parameter set includes frequency threshold, stability factor, and boundary tolerance; the real-time airflow guidance data stream includes offset, delay value, and weight coefficient; the respiratory cavity dynamic model includes hierarchical structure, matching relationship, and adjustment factor; and the respiratory rehabilitation training steady-state basic scheme includes priority sequence, distribution nodes, and regulation rules.

3. The respiratory rehabilitation training guidance system according to claim 1, characterized in that: The rhythm regulation module includes: The boundary parameter extraction submodule extracts the airflow boundary parameters of key nodes based on the dynamic change curve of airflow during the patient's breathing process, classifies the influencing factors of respiratory rate and depth, and generates a dynamic change distribution table. Based on the dynamic change distribution table, the stability analysis submodule analyzes the influence of breathing frequency and depth on airflow stability, calculates the stability adaptation value under different operating conditions, and generates a model of the relationship between airflow distribution and rhythm change. The adaptation table generation submodule organizes airflow distribution parameters based on the airflow distribution and rhythm change relationship model, analyzes the correspondence with key node boundaries, and generates a rhythm optimization parameter set.

4. The respiratory rehabilitation training guidance system according to claim 3, characterized in that: The dynamic boot module includes: The dynamic offset extraction submodule extracts the real-time airflow offset value and the target rhythm deviation based on the rhythm optimization parameter set, records the airflow rate change trend and response time data, and generates a dynamic offset data table. The fluctuation accumulation quantification submodule quantifies the dynamic fluctuation accumulation effect based on the dynamic offset data table, analyzes the difference between fluctuation distribution and demand ratio, and generates a dynamic fluctuation accumulation effect table. The guidance weight summarization submodule extracts the dynamic offset magnitude and correction frequency based on the dynamic fluctuation cumulative effect table, filters high-frequency fluctuation segments and marks the offset direction, summarizes the dynamic guidance weight, and generates a real-time airflow guidance data stream.

5. The respiratory rehabilitation training guidance system according to claim 4, characterized in that: The structural adaptation module includes: The cavity structure extraction submodule extracts the frequency and key node positions of the respiratory cavity structure based on the real-time airflow-guided data stream, classifies the cavity function priority data, and generates a cavity structure frequency table. The rhythm characteristic analysis submodule extracts the real-time airflow offset value and the target rhythm deviation based on the cavity structure frequency table, records the airflow rate change trend and response time, and obtains the rhythm state table. The hierarchical weighted analysis submodule performs a multidimensional comparison of cavity structure and airflow rhythm based on the rhythm state table, filters the cavity structure adaptation relationship, and generates a dynamic model of the respiratory cavity.

6. The respiratory rehabilitation training guidance system according to claim 5, characterized in that: The frequency of the respiratory cavity structure refers to the frequency of the respiratory cavity structure obtained by performing a Fourier transform on the real-time airflow guidance data stream, and identifying the peak frequency from the spectrum. The key node location refers to the center of the cross-sectional area through which the airflow passes, determined by combining the spatial distribution characteristics of the real-time airflow guidance data stream with a preset respiratory tract anatomical coordinate system. The real-time airflow offset value and the target rhythm deviation refer to the Euclidean distance between the airflow center axis and the preset ideal center axis in the real-time airflow guidance data stream, which is used as the real-time airflow offset value. The difference between the respiratory cavity structure frequency and the preset target rhythm frequency is calculated to obtain the target rhythm deviation.

7. The respiratory rehabilitation training guidance system according to claim 5, characterized in that: The steady-state equilibrium module includes: The priority ranking submodule extracts the peak position and boundary difference threshold of the cavity based on the dynamic model of the respiratory cavity, analyzes the functional contribution of the cavity under a unit boundary difference, and generates a cavity function priority ranking table. The steady-state distribution identification submodule extracts the steady-state change trajectory of the main channel node based on the cavity function priority ranking table, identifies the steady-state equilibrium point and offset direction, and generates a steady-state distribution relationship table. Based on the steady-state distribution relationship table, the steady-state adjustment submodule adjusts the guidance sequence by adjusting the steady-state node characteristics, extracts the frequency and amplitude sequence of steady-state changes of nodes, counts the offset amplitude and duration of nodes exceeding the steady-state limit, divides the stable interval and steady-state transition segment, and generates a steady-state basic scheme for respiratory rehabilitation training.

8. The respiratory rehabilitation training guidance system according to claim 7, characterized in that: The peak position of the cavity refers to the spatial coordinate point where the airflow velocity reaches a local extreme value in the three-dimensional distribution data of the airflow velocity in the dynamic model of the respiratory cavity, and the spatial coordinate point is determined as the peak position of the cavity. The boundary difference threshold refers to setting a range of five to ten percent of the key dimensions of the cavity as the boundary difference threshold based on the geometric dimensions and structural characteristics of the cavity in the dynamic model of the respiratory cavity. The so-called steady-state equilibrium point refers to identifying the main channel node in the steady-state distribution relationship table, where the fluctuation amplitude of the airflow velocity and airflow pressure is less than the preset steady-state tolerance value within a continuous preset time period, and determining the center point of the area that meets the condition as the steady-state equilibrium point. The offset direction refers to the calculated three-dimensional Euclidean distance between the current position of the main channel node and the center position of the steady-state equilibrium point, and the offset direction is determined based on the direction vector of the distance.

9. The respiratory rehabilitation training guidance system according to claim 1, characterized in that: The system also includes an execution path control module: The execution path control module, based on the respiratory rehabilitation training steady-state basic scheme, monitors the heart rate status and the execution status of key area boundaries during breathing, compares unmet boundary requirements and remaining capacity in real time, and fills steady-state gaps by adjusting the boundary distribution and cavity matching sequence, generating a global respiratory steady-state control execution scheme. The respiratory global homeostasis regulation execution scheme includes compensation sequence, matching path, and stability coefficient.

10. The respiratory rehabilitation training guidance system according to claim 9, characterized in that: The execution path control module includes: The status monitoring submodule, based on the aforementioned respiratory rehabilitation training steady-state baseline scheme, collects heart rate node values ​​and key area boundary feedback, records jump times and deviation amplitudes, and generates a status monitoring data table. Based on the status monitoring data table, the demand comparison submodule extracts the time points corresponding to the unmet boundaries, identifies the remaining capacity and instantaneous gaps, matches the target gaps with the capacity segments, and generates a demand comparison result table. Based on the demand comparison result table, the dynamic adjustment submodule fills the steady-state gap by adjusting the boundary distribution and cavity matching order, identifies the capability gap nodes and response lag segments, updates the boundary output timing and cavity curves, and generates a global steady-state control execution plan for respiration.

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