Medical vaporizer self-adaptive control method and system based on real-time expiration amount feedback

Abstract

The invention relates to the technical field of atomizers, and particularly discloses a medical atomizer self-adaptive control method and system based on real-time expiration amount feedback, the system comprises a control module, an atomization execution module and a respiration characteristic analysis module, and the control module is in communication connection with the atomization execution module and the respiration characteristic analysis module; the respiration characteristic analysis module is used for acquiring a dynamic respiration characteristic data set of a user in real time and sending the dynamic respiration characteristic data set of the user to the control module; and the control module is used for acquiring a respiration evaluation index according to the user dynamic respiration characteristic data set, acquiring atomization control information of operation of an atomizer in real time, and acquiring a user respiration-atomization matching degree according to the respiration evaluation index and the atomization control information. Atomization parameters are deeply matched with the breathing state and the individual characteristics of a patient, the medicine deposition efficiency and the treatment accuracy are improved, and in the aspect of data monitoring and application, the atomization execution module monitors medicine release data in stages.

Description

Technical Field

[0001] The present invention relates to the technical field of atomizers, and in particular to an adaptive control method and system for a medical atomizer based on real-time exhaled volume feedback. Background Art

[0002] A medical atomizer is an important medical device that atomizes drugs into tiny particles for patients to inhale through the respiratory tract to achieve the treatment purpose. With the development of medical technology, the application of adaptive control technology in medical atomizers has gradually attracted attention. The adaptive control of a medical atomizer aims to dynamically adjust the atomization parameters according to the individual differences and real-time physiological states of patients.

[0003] There are still many defects in the existing adaptive control technologies for medical atomizers. Specifically, single-parameter monitoring can only obtain information on one dimension of respiratory characteristics. For example, only monitoring the respiratory rate cannot consider the low drug inhalation efficiency caused by insufficient exhalation flow or the impact of tidal volume fluctuations on drug deposition effects. At the same time, these methods lack in-depth analysis of the dynamic correlations between multiple respiratory parameters, ignoring the coordinated changes of exhalation flow, respiratory rate, and tidal volume of different users during the breathing process and unable to accurately adjust adaptively. Summary of the Invention

[0004] The purpose of the present invention is to provide an adaptive control method and system for a medical atomizer based on real-time exhaled volume feedback to solve the technical problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: An adaptive control system for a medical atomizer based on real-time exhaled volume feedback, comprising: A control module, an atomization execution module, and a respiratory feature analysis module, wherein the control module is communicatively connected to the atomization execution module and the respiratory feature analysis module respectively; The respiratory feature analysis module is used to obtain a user's dynamic respiratory feature dataset in real time and send the user's dynamic respiratory feature dataset to the control module; The control module is used to obtain a respiratory evaluation index according to the user's dynamic respiratory feature dataset, obtain atomization control information of the atomizer operation in real time, and obtain the user's respiration-atomization matching degree according to the respiratory evaluation index and the atomization control information; The control module is used to determine whether the user's respiration-atomization matching degree is within a preset threshold range. If it is, it is determined that the atomizer is suitable for the user's respiration; if not, it is determined that the atomizer is not suitable for the user's respiration, and then atomization mode instruction information is generated according to the user's respiration-atomization matching degree; The atomization execution module is used to perform atomizer adjustment control according to the atomization mode instruction information and obtain drug release data of the user during atomizer control; The control module is further used to obtain drug release evaluation data according to the drug release data, obtain atomizer regulation information according to the drug release evaluation data and the user's breathing-atomization matching degree, and correct the atomization control information according to the atomizer regulation information.

[0006] Preferably, the breathing feature analysis module is used to obtain the real-time airflow data of the atomizer, obtain the exhalation flow rate data set, the breathing flow rate data set and the tidal volume data set according to the real-time airflow data, obtain the exhalation flow rate deviation data according to the exhalation flow rate data set, obtain the breathing frequency fluctuation data according to the breathing flow rate data set, obtain the tidal volume deviation data according to the tidal volume data set, and generate a user dynamic breathing feature data set according to the exhalation flow rate deviation data, the breathing frequency fluctuation data and the tidal volume deviation data; The control module is further used to intercept the exhalation flow rate data in the user dynamic breathing feature data set at a first preset time interval to obtain a high-frequency exhalation flow rate data set and a low-frequency exhalation flow rate data set, intercept the breathing frequency data at a second preset time interval to obtain a high-frequency breathing frequency data set and a low-frequency breathing frequency data set, intercept the tidal volume data at a third preset time interval to obtain a high tidal volume data set and a low tidal volume data set, map the high-frequency exhalation flow rate data set and the low-frequency exhalation flow rate data set, the high-frequency breathing frequency data set and the low-frequency breathing frequency data set, the high tidal volume data set and the low tidal volume data set to a breathing state feature tensor, and obtain a breathing evaluation index according to the breathing state feature tensor.

[0007] Preferably, the control module is further used to obtain the user's basic physiological data, obtain an exhalation flow rate correction coefficient, a breathing frequency correction coefficient, and a tidal volume correction coefficient according to the user's basic physiological data, obtain an exhalation flow rate fluctuation ratio according to the high-frequency exhalation flow rate data set and the low-frequency exhalation flow rate data set, obtain a breathing frequency ratio according to the high-frequency breathing frequency data set and the low-frequency breathing frequency data set, obtain a tidal volume ratio according to the high tidal volume data set and the low tidal volume data set, and obtain a breathing evaluation index according to the exhalation flow rate correction coefficient and the exhalation flow rate fluctuation ratio, the breathing frequency correction coefficient and the breathing frequency ratio, and the tidal volume correction coefficient and the tidal volume ratio.

[0008] Preferably, the control module is configured to obtain atomization control information of the atomizer. Among them, the atomization control information includes the real-time pulse frequency of the atomizer, the real-time air flow velocity of the atomizer, and the drug dose ejected by the atomizer each time. The atomization particle size value is obtained according to the real-time pulse frequency, the air flow intensity is obtained according to the real-time air flow velocity of the atomizer, the drug amount atomized per unit time value is obtained according to the drug dose ejected by the atomizer each time, and the user's breathing-atomization matching degree is obtained according to the atomization particle size value, the air flow intensity, the drug amount atomized per unit time, and the breathing evaluation index.

[0009] Preferably, the atomization execution module is configured to obtain the drug concentration fluctuation coefficient, the drug dose deviation coefficient, and the drug particle distribution uniformity coefficient according to the user's breathing-atomization matching degree. The air flow-drug concentration fluctuation value is obtained according to the drug concentration fluctuation coefficient and the air flow intensity. The drug dose deviation value is obtained according to the drug dose deviation coefficient and the drug amount atomized per unit time. The drug particle distribution uniformity is obtained according to the drug particle distribution uniformity coefficient and the atomization particle size value. The air flow control value is generated according to the air flow-drug concentration fluctuation value. The atomization drug amount value is obtained according to the drug dose deviation value. The pulse frequency value is obtained according to the drug particle distribution uniformity. The atomization mode instruction information is generated according to the air flow control value, the atomization drug amount value, and the pulse frequency value.

[0010] Preferably, the control module is configured to obtain the actual drug concentration parameter, the atomization drug amount parameter, and the drug particle distribution parameter according to the drug release data. The drug concentration fluctuation coefficient, the atomization drug amount fluctuation coefficient, and the drug particle distribution fluctuation coefficient are obtained according to the user's breathing-atomization matching degree. The air flow fluctuation range and the air flow adjustment direction are obtained according to the drug concentration parameter and the drug concentration fluctuation coefficient. The air flow adjustment data is generated according to the air flow fluctuation range and the air flow adjustment direction. The atomization drug amount fluctuation range and the atomization drug amount adjustment direction are obtained according to the atomization drug amount parameter and the atomization drug amount fluctuation coefficient. The atomization drug amount adjustment data is obtained according to the atomization drug amount fluctuation range and the atomization drug amount adjustment direction. The pulse frequency fluctuation value and the pulse frequency adjustment direction are obtained according to the drug particle distribution parameter and the drug particle distribution fluctuation coefficient. The pulse adjustment data is generated according to the pulse frequency fluctuation value and the pulse frequency adjustment direction. The air flow adjustment data, the atomization drug amount adjustment data, and the pulse adjustment data information.

[0011] Preferably, the control module is further configured to construct a dynamic allocation model according to the atomizer regulation information, obtain a user's real-time breathing evaluation index, drug release evaluation data, and historical treatment response data based on the dynamic allocation model, obtain correction information according to the user's real-time breathing evaluation index, drug release evaluation data, and historical treatment response data, where the correction information includes an atomization pulse frequency correction value, a single drug spray dose correction value, and an air flow velocity correction value, control the atomizer to match the patient's breathing frequency based on the atomization pulse frequency correction value, control the atomization drug amount of the atomizer per unit time based on the single drug spray dose correction value, and control the real-time gas flow velocity of the atomizer based on the air flow velocity correction value.

[0012] The present invention also provides a medical atomizer adaptive control method based on real-time exhaled volume feedback, including: Obtain a user's dynamic breathing feature dataset in real time and send the user's dynamic breathing feature dataset to the control module; Obtain a breathing evaluation index according to the user's dynamic breathing feature dataset, obtain atomization control information of the atomizer in real time, and obtain a user's breathing-atomization matching degree according to the breathing evaluation index and the atomization control information; Judge whether the user's breathing-atomization matching degree is within a preset threshold range. If it is, judge that the atomizer is suitable for the user's breathing and no adjustment is required. If not, judge that the atomizer is not suitable for the user's breathing, and generate atomization mode instruction information according to the user's breathing-atomization matching degree; Execute atomizer adjustment control according to the atomization mode instruction information and obtain drug release data of the user during atomizer control; Obtain drug release evaluation data according to the drug release data, obtain atomizer regulation information according to the drug release evaluation data and the user's breathing-atomization matching degree, and correct the atomization control information according to the atomizer regulation information.

[0013] Preferably, the step of obtaining the user's dynamic breathing feature dataset includes: Obtain the real-time air flow data of the atomizer; Obtain an exhalation flow data set, a breathing flow data set, and a tidal volume data set according to the real-time air flow data; Obtain exhalation flow deviation data according to the exhalation flow data set; Obtain breathing frequency fluctuation data according to the breathing flow data set; Obtain tidal volume deviation data according to the tidal volume data set; Generate a user's dynamic breathing feature dataset according to the exhalation flow deviation data, the breathing frequency fluctuation data, and the tidal volume deviation data.

[0014] Preferably, the step of obtaining a respiration assessment index according to the user's dynamic respiration feature dataset includes: Intercept the expiratory flow rate data in the user's dynamic respiration feature dataset at a first preset time interval to obtain a high-frequency expiratory flow rate data set and a low-frequency expiratory flow rate data set; Intercept the respiration frequency data at a second preset time interval to obtain a high-frequency respiration frequency data set and a low-frequency respiration frequency data set; Intercept the tidal volume data at a third preset time interval to obtain a high tidal volume data set and a low tidal volume data set; Map the high-frequency expiratory flow rate data set and the low-frequency expiratory flow rate data set, the high-frequency respiration frequency data set and the low-frequency respiration frequency data set, and the high tidal volume data set and the low tidal volume data set to a respiration state feature tensor; Obtain a respiration assessment index according to the respiration state feature tensor.

[0015] The beneficial effects of the present application are as follows: Through the multi-sensor collaborative data collection of the respiration feature analysis module, the control module uses technologies such as data interception and tensor mapping to comprehensively integrate parameters such as expiratory flow rate, respiration frequency, and tidal volume, accurately obtain the respiration assessment index, and realize the dynamic and multi-dimensional assessment of the patient's respiration state. In terms of adjusting treatment parameters, based on the respiration assessment index and drug release assessment data, the control module combines airflow fluctuations, dose errors, and differences in the patient's lung function to dynamically adjust parameters such as the atomization pulse frequency and dose, changing the traditional "one-size-fits-all" mode, making the atomization parameters deeply match the patient's respiration state and individual characteristics, improving the drug deposition efficiency and treatment accuracy. In terms of data monitoring and application, the atomization execution module monitors the drug release data in stages, and the control module deeply mines information such as concentration fluctuations and particle distributions to form a closed-loop feedback mechanism, providing a comprehensive basis for optimizing the treatment plan, effectively reducing drug waste, and reducing the risk of adverse reactions caused by parameter mismatches. Description of the Drawings

[0016] Figure 1 It is a schematic diagram of the system structure of an embodiment of the present application.

[0017] Figure 2 It is a schematic diagram of the method flow of an embodiment of the present application.

[0018] The realization, functional features, and advantages of the purpose of the present application will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0019] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] Such as Figure 1As shown, the present application provides an adaptive control system for a medical nebulizer based on real-time exhaled volume feedback, including: a control module, a nebulization execution module, and a respiratory feature analysis module. The control module is communicatively connected to the nebulization execution module and the respiratory feature analysis module respectively. The respiratory feature analysis module is configured to obtain a user's dynamic respiratory feature data set in real time and send the user's dynamic respiratory feature data set to the control module. The control module is configured to obtain a respiratory assessment index according to the user's dynamic respiratory feature data set, and obtain real-time nebulization control information of the nebulizer operation, and obtain a user's respiration-nebulization matching degree according to the respiratory assessment index and the nebulization control information. The control module is configured to determine whether the user's respiration-nebulization matching degree is within a preset threshold range. If it is, it is determined that the nebulizer is suitable for the user's respiration and no adjustment is required. If not, it is determined that the nebulizer is not suitable for the user's respiration, and a nebulization mode instruction information is generated according to the user's respiration-nebulization matching degree. The nebulization execution module is configured to execute nebulizer adjustment control according to the nebulization mode instruction information and obtain drug release data of the user during nebulizer control. The control module is further configured to obtain drug release assessment data according to the drug release data, and obtain nebulizer regulation information according to the drug release assessment data and the user's respiration-nebulization matching degree, and correct the nebulization control information according to the nebulizer regulation information.

[0021] As described above, the existing manual adjustment methods cannot perceive the changes in the patient's respiratory state in real time and accurately, nor can they flexibly adjust the atomization parameters according to the specific characteristics of the drug. This makes it difficult for the nebulizer to achieve the best match with the patient's respiratory state and the drug used during actual use. For example, when the patient breathes rapidly and has a large expiratory flow rate, a fixed-gear nebulizer may not be able to provide sufficiently fine and appropriate atomized particles in a timely manner, resulting in the drug not being fully deposited in the effective part of the respiratory tract and causing drug waste; when the patient breathes weakly, there may be a situation where the drug dose is too large or the particles are too large to be inhaled. Based on this, the present invention proposes an adaptive control system for a medical nebulizer based on real-time expiratory volume feedback, aiming to make up for the deficiencies of these existing technologies and achieve more efficient and accurate atomization treatment. The present invention mainly consists of a control module, an atomization execution module, and a respiratory feature analysis module, and the control module has established communication connections with the atomization execution module and the respiratory feature analysis module respectively to achieve the coordinated operation between the modules. The role of the respiratory feature analysis module is to obtain the user's dynamic respiratory feature data set in real time, and collect the real-time airflow data of the nebulizer with the help of various sensors such as a pressure sensor and a flow sensor installed on the nebulizer. From these real-time airflow data, an expiratory flow data set, a respiratory flow data set, and a tidal volume data set are extracted. Among them, the expiratory flow data set records the flow rate changes during each exhalation of the user; the respiratory flow data set covers the flow dynamics during the entire respiratory cycle; the tidal volume data set reflects the volume of gas inhaled or exhaled during each breath. Then, characteristic parameter calculations are performed on each data set. Each data point in the expiratory flow data set is compared with a preset standard expiratory flow model to obtain expiratory flow deviation data, so as to measure the difference between the user's expiratory flow and the standard; by applying a time series analysis algorithm to the respiratory flow data set, the change frequency and amplitude of the respiratory flow at adjacent time points are calculated to obtain respiratory frequency fluctuation data, which is used to reflect the stability and regularity of the respiratory frequency; the tidal volume data set is compared with the user's own basic tidal volume data and the average tidal volume data of users of the same type to obtain tidal volume deviation data to determine whether the tidal volume is within the normal range. Finally, the expiratory flow deviation data, the respiratory frequency fluctuation data, and the tidal volume deviation data are integrated into the user's dynamic respiratory feature data set and sent to the control module.

[0022] The control module receives the user's dynamic breathing feature dataset transmitted by the breathing feature analysis module. First, it intercepts the expiratory flow data according to the first preset time interval, calculates statistical features such as the mean and standard deviation of the expiratory flow in each time period, and divides them into a high-frequency expiratory flow data set and a low-frequency expiratory flow data set according to the flow rate; similarly, similar processing is performed on the breathing frequency data and tidal volume data to obtain a high-frequency breathing frequency data set, a low-frequency breathing frequency data set, a high tidal volume data set, and a low tidal volume data set. These six groups of data are mapped into a breathing state feature tensor, and a multi-dimensional fitness vector including expiratory flow stability, breathing frequency regularity, and tidal volume consistency is generated based on the tensor, and then a breathing assessment index is obtained. The breathing assessment index comprehensively reflects the stability, regularity, and consistency of the expiratory flow, breathing frequency, and tidal volume, and intuitively reflects the user's breathing stability in numerical form. The higher the index, the more stable the breathing state.

[0023] Next, the control module obtains the atomizer operation control information in real time, which includes the real-time pulse frequency of the atomizer, the real-time air flow velocity of the atomizer, and the drug dose ejected by the atomizer per single time. Calculate the atomized particle size value according to the real-time pulse frequency. The pulse frequency determines the generation rate and particle size of the atomized particles. The higher the frequency, the finer the particles; obtain the air flow intensity according to the real-time air flow velocity of the atomizer; obtain the atomized drug amount value per unit time according to the drug dose ejected by the atomizer per single time. Then, combine these values with the breathing assessment index to obtain the user's breathing-atomization matching degree through a specific algorithm. This matching degree reflects the adaptation degree between the atomizer operation parameters and the user's breathing state.

[0024] After that, the control module makes a decision according to the breathing-atomization matching degree. Judge whether the breathing-atomization matching degree is within the preset threshold range. If it is, it is determined that the atomizer is applicable and no adjustment is required; if not, an atomization mode instruction information is generated according to the matching degree.

[0025] After obtaining the drug release data, the control module obtains the actual drug concentration parameters, atomized drug amount parameters, and drug particle distribution parameters according to these data, and combines the breathing-atomization matching degree to obtain the corresponding drug concentration fluctuation coefficient, atomized drug amount fluctuation coefficient, and drug particle distribution fluctuation coefficient. Calculate data such as the air flow fluctuation range, air flow adjustment direction, atomized drug amount fluctuation range per unit time, atomized drug amount adjustment direction, pulse frequency fluctuation value, and pulse frequency adjustment direction through these coefficients and parameters, and then generate air flow adjustment data, atomized drug amount adjustment data, and pulse adjustment data, and finally obtain the atomizer control information to correct the atomizer operation control information. The control module realizes the accurate quantitative assessment of the breathing state, dynamically adjusts the atomization strategy in combination with the atomizer operation state, and makes the atomization parameters deeply match the patient's breathing state. By continuously optimizing the atomizer operation control information, the drug deposition efficiency and treatment accuracy are improved, drug waste is reduced, and the risk of adverse reactions is lowered.

[0026] Finally, there is the atomization execution module, which is responsible for adjusting the atomizer according to the atomization mode instruction information generated by the control module, and obtaining the drug release data of the user during the atomization process. It receives the atomization mode instruction information sent by the control module and adjusts the working parameters of the atomizer according to the instructions, such as pulse frequency, single-dose drug ejection amount, etc. During the atomization treatment process, the early breathing cycle set of the user in the atomization stage is intercepted according to the first preset number of breathing cycles, and the late breathing cycle set is intercepted according to the second preset number of breathing cycles, and compared with the preset standard drug release concentration respectively to obtain the early concentration deviation data and the late concentration deviation data. Through the data processing algorithm, these concentration deviation data are integrated with information such as the corresponding breathing cycle time and the patient's breathing parameters to generate drug release data and feedback it to the control module. At the same time, according to the user's breathing-atomization matching degree, the drug concentration fluctuation coefficient, the drug dose deviation coefficient, and the drug particle distribution uniformity coefficient are obtained, the air flow-drug concentration fluctuation value, the drug dose deviation value, and the drug particle distribution uniformity are calculated, and then the air flow control value, the atomization drug amount value, and the pulse frequency value are generated. Finally, the atomization mode instruction information is generated to form a closed-loop control with the control module. Among them, the drug release data includes information such as the drug release concentration and the release amount per unit time during the atomization treatment process, which is used to evaluate the drug release effect; the drug concentration fluctuation coefficient, the drug dose deviation coefficient, and the drug particle distribution uniformity coefficient are used to measure the drug concentration fluctuation, dose deviation, and particle distribution uniformity; the air flow-drug concentration fluctuation value reflects the size of the drug concentration fluctuation; the drug dose deviation value reflects the deviation between the actual drug dose and the standard dose; the drug particle distribution uniformity measures the uniformity of the drug particle distribution in space; the air flow control value, the atomization drug amount value, and the pulse frequency value are used to adjust the air flow, drug amount, and pulse frequency of the atomizer. This module realizes the precise adjustment of the atomizer, ensures that the drug is released with appropriate parameters, monitors the drug release data in stages, and provides comprehensive and accurate feedback to the control module.

[0027] In one embodiment, the respiratory feature analysis module is configured to obtain real-time airflow data of the nebulizer, acquire an exhalation flow rate data set, a respiratory flow rate data set, and a tidal volume data set based on the real-time airflow data, obtain exhalation flow rate deviation data according to the exhalation flow rate data set, obtain respiratory rate fluctuation data according to the respiratory flow rate data set, obtain tidal volume deviation data according to the tidal volume data set, generate a user's dynamic respiratory feature data set based on the exhalation flow rate deviation data, the respiratory rate fluctuation data, and the tidal volume deviation data, and the respiratory feature analysis module obtains the real-time airflow data of the nebulizer. The sensor array includes various types of sensors such as pressure sensors and flow sensors. Like a sensitive "sensory organ", it can capture subtle changes in airflow from multiple dimensions, providing a rich and accurate data basis for subsequent analysis. After obtaining the real-time airflow data, the respiratory feature analysis module begins to deeply deconstruct and analyze the data. First, through a specific data separation algorithm, the exhalation flow rate data set, the respiratory flow rate data set, and the tidal volume data set are accurately extracted from the real-time airflow data. This process is similar to separating and purifying a complex mixture. The exhalation flow rate data set records the flow rate changes during each exhalation of the user. The respiratory flow rate data set covers the flow dynamics during the entire respiratory cycle. The tidal volume data set reflects the volume of gas inhaled or exhaled during each breath. Then, the module calculates the characteristic parameters for each data set. For the exhalation flow rate data set, by comparing with a preset standard exhalation flow rate model, the deviation value of each data point from the standard model is calculated, and thus the exhalation flow rate deviation data is obtained. This is like comparing the user's exhalation flow rate performance with a "healthy template" to find the differences. Based on the respiratory flow rate data set, the module uses a time series analysis algorithm to calculate the change frequency and amplitude of the respiratory flow rate at adjacent time points, thereby obtaining the respiratory rate fluctuation data to reflect the stability and regularity of the user's respiratory rate. When processing the tidal volume data set, by comparing with the user's own baseline tidal volume data and the average tidal volume data of users of the same type, the tidal volume deviation data is calculated to determine whether the tidal volume is within the normal and reasonable range. Finally, the respiratory feature analysis module integrates the exhalation flow rate deviation data, the respiratory rate fluctuation data, and the tidal volume deviation data to generate a user's dynamic respiratory feature data set (the respiratory feature analysis module first uses a filtering algorithm to remove the noise in the exhalation flow rate deviation data, the respiratory rate fluctuation data, and the tidal volume deviation data, then aligns the data collected at different frequencies through time synchronization technology, and then adopts a weighted fusion algorithm to dynamically assign weights to each data according to the disease type and respiratory stage, effectively overcoming problems such as messy data, time misalignment, and inability to reflect individual differences, and finally generating a dynamic respiratory feature data set that can accurately reflect the user's real-time respiratory state and predict the respiratory change trend).

[0028] In one embodiment, the control module intercepts the expiratory flow rate data in the user's dynamic breathing characteristic data set according to a first preset time interval to obtain a high-frequency expiratory flow rate data set and a low-frequency expiratory flow rate data set, intercepts the breathing frequency data according to a second preset time interval to obtain a high-frequency breathing frequency data set and a low-frequency breathing frequency data set, intercepts the tidal volume data according to a third preset time interval to obtain a high tidal volume data set and a low tidal volume data set, maps the high-frequency expiratory flow rate data set and the low-frequency expiratory flow rate data set, the high-frequency breathing frequency data set and the low-frequency breathing frequency data set, and the high tidal volume data set and the low tidal volume data set into a breathing state feature tensor, generates a multi-dimensional fitness vector including expiratory flow rate stability, breathing frequency regularity, and tidal volume consistency according to the breathing state feature tensor, and obtains a breathing evaluation index according to the multi-dimensional fitness vector. The control module first processes the user's dynamic breathing characteristic data set, intercepts the expiratory flow rate data according to the first preset time interval, obtains the mean value and standard deviation statistical characteristics of the expiratory flow rate in each time period, and divides it into a high-frequency expiratory flow rate data set and a low-frequency expiratory flow rate data set according to the flow rate level; processes the breathing frequency data at the second preset time interval, observes its changes in different time periods, includes those higher than the normal range in the high-frequency breathing frequency data set, and vice versa in the low-frequency breathing frequency data set; intercepts the tidal volume data through the third preset time interval, compares the mean value of the tidal volume in each time period with the standard value, those greater than the standard value are classified into the high tidal volume data set, and those less than the standard value are the low tidal volume data set. Then, the control module maps the above six groups of data into a breathing state feature tensor. As a multi-dimensional data structure, the tensor integrates the expiratory flow rate, breathing frequency, and tidal volume data, systematically presents the correlation relationships between the parameters, realizes the multi-dimensional representation of the breathing state, and can more comprehensively reflect the complex characteristics of the breathing state compared with the traditional single-parameter analysis. Subsequently, based on the breathing state feature tensor, the control module generates a multi-dimensional fitness vector including expiratory flow rate stability, breathing frequency regularity, and tidal volume consistency (the control module first extracts the historical data and real-time data of the expiratory flow rate, breathing frequency, and tidal volume from the breathing state feature tensor, uses the sliding window algorithm to calculate the standard deviation and coefficient of variation of each index at different time scales to quantify stability, regularity, and consistency, then normalizes the data to make it in the same dimension, and finally combines these quantization results into a multi-dimensional fitness vector according to the preset weights). Among them, the expiratory flow rate stability is calculated based on the fluctuation degree of the high-frequency and low-frequency expiratory flow rate data sets, and a small fluctuation means high stability; the breathing frequency regularity is judged by analyzing the high-frequency and low-frequency breathing frequency data sets to determine whether the change in the breathing frequency is stable; the tidal volume consistency compares the high-value and low tidal volume data sets to evaluate the difference degree of the tidal volume in different breathing cycles.The multi-dimensional fitness vector quantifies the characteristics and mutual relationships of various respiratory parameters, forming an accurate quantitative description of the respiratory state. Finally, the control module obtains a respiratory assessment index based on the multi-dimensional fitness vector (the control module first performs a non-linear mapping on the exhaled flow rate stability, respiratory rate regularity, and tidal volume consistency indicators in the multi-dimensional fitness vector to convert them into confidence scores in the 0-1 interval, then calculates the conditional probability distribution of each indicator through a dynamic Bayesian network, adjusts the weights by combining prior knowledge of the disease type and treatment stage, then introduces a time-varying factor to capture the dynamic changes of the respiratory state, and finally generates a respiratory assessment index that can comprehensively reflect the respiratory quality and treatment suitability through weighted aggregation). This index comprehensively considers the stability, regularity, and consistency of the exhaled flow rate, respiratory rate, and tidal volume, and intuitively reflects the user's respiratory stability in numerical form. The higher the index, the more stable the respiratory state. Through multi-parameter collaborative analysis, it simultaneously considers the exhaled flow rate, respiratory rate, and tidal volume, breaking through the limitation of single-parameter assessment and comprehensively capturing the changes in the respiratory state; by using tensor and multi-dimensional fitness vector technologies, it deeply explores the complex relationships between data and realizes the accurate quantification of the respiratory state. Based on this index, the parameters of the nebulizer and the dosage of medication are adjusted to improve the user's respiratory state.

[0029] In one embodiment, the control module is further configured to obtain the user's basic physiological data, obtain an exhaled flow rate correction coefficient, a respiratory rate correction coefficient, and a tidal volume correction coefficient according to the user's basic physiological data, obtain a plurality of high-frequency exhaled flow rate values according to the high-frequency exhaled flow rate data set, obtain a plurality of low-frequency exhaled flow rate values according to the low-frequency exhaled flow rate data set, obtain a first exhaled standard value corresponding to the high-frequency exhaled flow rate data set, obtain a second exhaled standard value corresponding to the low-frequency exhaled flow rate data set, and calculate an exhaled flow rate fluctuation ratio according to the plurality of high-frequency exhaled flow rate values, the plurality of low-frequency exhaled flow rate values, the first exhaled standard value, and the second exhaled standard value. The calculation formula is as follows: ; where A represents the exhaled flow rate fluctuation ratio, m1 represents the first high-frequency exhaled flow rate value, m2 represents the second high-frequency exhaled flow rate value, m i represents the i-th high-frequency exhaled flow rate value, k represents the first exhaled standard value, n1 represents the first low-frequency exhaled flow rate value, n2 represents the second low-frequency exhaled flow rate value, n u represents the u-th low-frequency exhaled flow rate value, and I represents the second exhaled standard value.

[0030] The exhaled flow rate fluctuation ratio is used to measure the fluctuation of the exhaled flow rate and reflects the degree of dispersion of the exhaled flow rate relative to its respective standard value at different levels (high frequency and low frequency). m1, m2...m irepresenting respective expiratory flow rate measurements selected from the high-frequency expiratory flow rate data set, which record specific flow rate values during the high expiratory flow rate phase, reflect the changes in expiratory flow rate during the larger expiratory flow rate in the breathing process, and through the difference operation with the first expiratory standard value k, reflect the deviation degree of the high-frequency expiratory flow rate relative to the standard value, n1, n2...n u representing respective expiratory flow rate measurements in the low-frequency expiratory flow rate data set, reflecting the specific flow rate changes during the low expiratory flow rate phase.

[0031] Obtain multiple high-frequency breathing frequency values according to the high-frequency breathing frequency data set, obtain multiple low-frequency breathing frequency values according to the low-frequency breathing frequency data set, obtain the first breathing frequency standard value corresponding to the high-frequency breathing frequency data set, obtain the second breathing frequency standard value corresponding to the low-frequency breathing frequency data set, and calculate the breathing frequency ratio according to the multiple high-frequency breathing frequency values, multiple low-frequency breathing frequency values, the first breathing frequency standard value, and the second breathing frequency standard value, where the calculation formula is: ; where B represents the breathing frequency ratio, q1 represents the 1st high-frequency breathing frequency value, q2 represents the 2nd high-frequency breathing frequency value, q o represents the oth high-frequency breathing frequency value, X represents the first breathing frequency standard value, p1 represents the 1st low-frequency breathing frequency value, p2 represents the 2nd low-frequency breathing frequency value, p e represents the eth low-frequency breathing frequency value, and Y represents the second breathing frequency standard value.

[0032] The breathing frequency ratio is used to quantify the fluctuation degree of the breathing frequency in different frequency intervals (high-frequency and low-frequency) relative to their respective standard values. It synthesizes the characteristics of the high-frequency and low-frequency breathing frequency data sets and is a key quantitative index for evaluating the stability and regularity of the breathing frequency. q1, q2...q o are specific measurement values obtained from the high-frequency breathing frequency data set, reflecting the frequency changes during the high breathing frequency phase in the breathing process. The cumulative sum of the differences after the difference operation with the first breathing frequency standard value X reflects the overall deviation degree of the high-frequency breathing frequency relative to the standard value. p1, p2...p represent specific measurement values in the low-frequency breathing frequency data set, reflecting the changes during the low breathing frequency phase. e Similar to the high-frequency breathing frequency values, they are measurement data obtained during the low breathing frequency time period. These values are subjected to a difference operation with the second breathing frequency standard value Y, and the cumulative sum of the differences reflects the deviation of the low-frequency breathing frequency relative to the standard value, reflecting the fluctuation characteristics of the low-frequency breathing frequency.

[0033] Obtain multiple high tidal volume values according to the high tidal volume data set, obtain multiple low tidal volume values according to the low tidal volume data set, obtain a first tidal volume standard value corresponding to the high breathing frequency data set, obtain a second tidal volume standard value corresponding to the low breathing frequency data set, and calculate a tidal volume ratio according to the multiple high tidal volume values, multiple low tidal volume values, the first tidal volume standard value, and the second tidal volume standard value. The calculation formula is as follows: ; where C represents the tidal volume ratio, s1 represents the first high tidal volume value, s2 represents the second high tidal volume value, s f represents the f-th high tidal volume value, G represents the first tidal volume standard value, h1 represents the first low tidal volume value, h2 represents the second low tidal volume value, h r represents the r-th low tidal volume value, and F represents the second tidal volume standard value.

[0034] The tidal volume ratio is used to measure the fluctuation of the tidal volume, reflect the change degree of the tidal volume in different breathing cycles, and is a specific value obtained from the high tidal volume data set. s1, s2... s f represent the measurement values when the tidal volume is at a relatively high level during the breathing process. These data reflect the specific situation when the volume of inhaled or exhaled gas is relatively large during each breath. By performing a difference operation with the first tidal volume standard value G, the accumulation of multiple differences reflects the overall deviation degree of the high tidal volume relative to the standard value. h1, h2... h r are the respective measurement values in the low tidal volume data set, reflecting the specific situation when the tidal volume is at a relatively low level during the breathing process. They perform a difference operation with the second tidal volume standard value F, and the accumulation of the differences reflects the deviation of the low tidal volume relative to the standard value.

[0035] Obtain a respiratory assessment index according to the expiratory flow correction coefficient, expiratory flow fluctuation ratio, respiratory frequency correction coefficient, respiratory frequency ratio, tidal volume correction coefficient, and tidal volume ratio. The calculation formula for the respiratory assessment index is as follows: ; where Z represents the respiratory assessment index, A represents the expiratory flow fluctuation ratio, α represents the expiratory flow correction coefficient, B represents the respiratory frequency ratio, β represents the respiratory frequency correction coefficient, C represents the tidal volume ratio, and γ represents the tidal volume correction coefficient.

[0036] In one embodiment, the control module is configured to obtain atomization control information of the atomizer during operation. The atomization control information includes the real-time pulse frequency of the atomizer, the real-time air flow velocity of the atomizer, and the drug dose ejected by the atomizer each time. The atomization particle size value is obtained according to the real-time pulse frequency, the air flow intensity is obtained according to the real-time air flow velocity of the atomizer, the drug amount atomized per unit time is obtained according to the drug dose ejected by the atomizer each time, and the user's respiration-atomization matching degree is obtained according to the atomization particle size value, the air flow intensity, the drug amount atomized per unit time, and the respiration evaluation index. The atomization particle size value, the air flow intensity, and the drug amount atomized per unit time are normalized. The corresponding dynamic correction weights are obtained for the normalized results of each atomization particle size value, air flow intensity, and drug amount atomized per unit time, and the respiration synchronization correction weight is obtained based on the state correction weight. The user's respiration-atomization matching degree is obtained according to the respiration synchronization correction weight, the normalized results of the atomization particle size value, the air flow intensity, the drug amount atomized per unit time, and the corresponding dynamic correction weights.

[0037] The real-time pulse frequency of the atomizer is the frequency of the pulse signal generated when the atomizer is working. It directly determines the generation rate and particle size of the atomization particles. The higher the frequency, the more and finer the atomization particles generated, which is beneficial for the drug to be deposited deep in the lungs. The real-time air flow velocity of the atomizer refers to the flow velocity of the air flow output by the atomizer, measured in meters per second (m / s). It affects the transmission efficiency and distribution uniformity of the drug particles in the respiratory tract. The drug dose ejected by the atomizer each time, that is, the amount of drug ejected each time, is usually measured in milliliters (mL) or micrograms (μg), and directly determines the total amount of drug inhaled by the patient each time.

[0038] The control module collects the three key parameters of the real-time pulse frequency of the atomizer, the real-time air flow velocity of the atomizer, and the drug dose ejected by the atomizer each time in real time through the communication link with the atomization execution module. After that, the real-time pulse frequency of the atomizer determines the generation and particle size of the atomization particles, which in turn affects the deposition position and effect of the drug in the lungs. The real-time air flow velocity of the atomizer ensures the effective transmission and uniform distribution of the drug particles in the respiratory tract. The drug dose ejected by the atomizer each time is adjusted according to individual differences to ensure that the patient inhales an appropriate amount of drug. The atomization particle size value is closely related to the deposition site of the drug in the respiratory tract, which helps to improve the treatment targeting. The air flow intensity ensures that the drug particles can be better diffused and deposited. The drug amount atomized per unit time combines the patient's respiration frequency and tidal volume to evaluate whether the actual inhaled drug amount of the patient meets the treatment requirements. The respiration evaluation index provides a personalized basis for the adjustment of the atomizer parameters. Patients with unstable respiration require more refined parameter adjustment. The user's respiration-atomization matching degree is an important basis for judging the effectiveness of the current atomization treatment plan.

[0039] In one embodiment, the atomization execution module is used to obtain a drug concentration fluctuation coefficient, a drug dose deviation coefficient, and a drug particle distribution uniformity coefficient according to the user's breathing-atomization matching degree, obtain an air flow-drug concentration fluctuation value according to the drug concentration fluctuation coefficient and the air flow intensity, obtain a drug dose deviation value according to the drug dose deviation coefficient and the atomization drug amount per unit time, obtain a drug particle distribution uniformity according to the drug particle distribution uniformity coefficient and the atomization particle size value, generate an air flow control value according to the air flow-drug concentration fluctuation value (mapping the air flow-drug concentration fluctuation value to the air flow control value through fuzzy inference), obtain an atomization drug amount value according to the drug dose deviation value, obtain a pulse frequency value according to the drug particle distribution uniformity, and generate atomization mode instruction information according to the air flow control value, the atomization drug amount value, and the pulse frequency value (wherein, the specific adjustment process is as follows. By obtaining the threshold range where the air flow control value is located, when the air flow control value is in the low threshold range, the air flow speed can be appropriately increased. For example, increase it by 10% - 20% based on the current air flow speed; when it is in the medium threshold range, maintain the current air flow speed or make a fine adjustment, and the fine adjustment range is controlled within 5% - 10%; when it is in the high threshold range, reduce the air flow speed, such as reducing it by 15% - 25%.

[0040] According to the magnitude and sign of the drug dose deviation value, determine the corresponding atomization drug amount adjustment information. If the dose deviation value is positive and large (such as exceeding 15%), it means that the actual output drug amount exceeds the target drug amount. At this time, the atomization drug amount needs to be reduced. The reduction amplitude can be adjusted according to the magnitude of the deviation value. The larger the deviation value, the greater the reduction amplitude. For example, when the deviation value is 20%, reduce the current single-time atomization drug amount by 10% - 15%. If the dose deviation value is negative and large (such as exceeding -15%), increase the atomization drug amount, and also adjust the increase amplitude according to the deviation value. For example, when the deviation value is -20%, increase the current single-time atomization drug amount by 12% - 18%. When the dose deviation value is within a small range (such as between -5% and 5%), it is considered that the current atomization drug amount is relatively appropriate, and no adjustment or only a very small adjustment (such as an adjustment range of 1% - 3%) can be made.

[0041] The direction and magnitude of pulse frequency adjustments are determined by the quantified results of drug particle distribution uniformity. Low particle distribution uniformity indicates the need for pulse frequency adjustment to improve distribution. If the particle size is large and the distribution is uneven, the pulse frequency should be appropriately increased to produce finer and more evenly distributed atomized particles. The magnitude of this increase is determined by the difference between the quantified uniformity index and the ideal uniformity index. The greater the difference, the greater the increase. For example, if the quantified uniformity index is 20% below the ideal value, the pulse frequency should be increased by 15%-20%. When particle distribution uniformity is high, the current pulse frequency can be maintained or fine-tuned. If uniformity is very high, the pulse frequency can even be appropriately reduced to save energy and reduce equipment wear, but the reduction should be limited to, for example, 5%-10%.

[0042] The user's breathing-nebulization matching degree is a value obtained by comprehensively considering factors such as the atomized particle size, airflow intensity, atomized drug volume per unit time, and respiratory assessment index. The higher the matching degree, the more adaptable the current atomization treatment plan is to the patient's breathing, and the better the drug inhalation effect may be.

[0043] The drug concentration fluctuation coefficient, drug dose deviation coefficient and drug particle distribution uniformity coefficient reflect the relevant characteristics of drugs in nebulization therapy from different aspects. The drug concentration fluctuation coefficient is used to quantify the degree of fluctuation of drug concentration in time or respiratory cycle. It is a dimensionless value. Its size directly reflects the stability of drug concentration. The larger the coefficient, the more violent the fluctuation, and vice versa. The drug dose deviation coefficient measures the degree of deviation between the actual nebulized output drug dose and the expected set dose. It is also a dimensionless value. The larger the coefficient, the greater the gap between the actual dose and the expected dose, and the greater the impact on the treatment effect. The drug particle distribution uniformity coefficient is an indicator to evaluate the uniformity of the spatial distribution of nebulized drug particles. The larger the coefficient, the more uniform the particle distribution.

[0044] The airflow-drug concentration fluctuation value is calculated by combining the drug concentration fluctuation coefficient and the airflow intensity. It specifically quantifies the fluctuation amplitude of the drug concentration during the actual atomization process and is a key reference value for subsequent adjustment of the airflow parameters to stabilize the drug concentration. The drug dose deviation value is obtained by multiplying the drug dose deviation coefficient and the atomized drug volume per unit time. It clarifies the specific deviation between the actual drug dose and the expected dose, and plays an important role in accurately adjusting the atomized drug volume. The drug particle distribution uniformity is calculated using the drug particle distribution uniformity coefficient and the atomized particle size value, which intuitively shows the uniformity of the drug particle distribution in space. The higher the uniformity, the better the drug deposition effect in the respiratory tract.

[0045] The air flow control value, the atomization drug amount value, and the pulse frequency value are key parameters directly used to control the operation of the atomizer. The air flow control value is generated based on the air flow-drug concentration fluctuation value and is used to adjust the air flow parameters of the atomizer, such as air flow speed, pressure, etc. A suitable air flow control value can stabilize the drug concentration and improve the drug delivery efficiency. The atomization drug amount value is determined according to the drug dose deviation value to accurately control the drug dose ejected by the atomizer each time, ensuring that the patient can inhale an appropriate amount of drug with each breath. The pulse frequency value is determined by the uniformity of the drug particle distribution and is used to adjust the pulse frequency of the atomizer. A suitable pulse frequency can make the atomized particle size more uniform and improve the deposition efficiency of the drug in the respiratory tract.

[0046] The atomization execution module first receives the user's breathing-atomization matching degree from the control module, and obtains the drug concentration fluctuation coefficient, the drug dose deviation coefficient, and the drug particle distribution uniformity coefficient. Then, these coefficients are used to calculate with the air flow intensity, the atomization drug amount per unit time, and the atomized particle size value respectively, to obtain the air flow-drug concentration fluctuation value, the drug dose deviation value, and the drug particle distribution uniformity. The atomization mode instruction information is sent to the atomizer, and the atomizer adjusts the working parameters according to the instruction.

[0047] The user's breathing-atomization matching degree provides a basic basis for subsequent calculations and guides the system to optimize the treatment plan. The drug concentration fluctuation coefficient, the drug dose deviation coefficient, and the drug particle distribution uniformity coefficient quantify the drug-related characteristics and provide parameters for subsequent accurate calculations. The air flow-drug concentration fluctuation value, the drug dose deviation value, and the drug particle distribution uniformity reflect the actual situation and help the system discover and correct treatment deviations. The air flow control value, the atomization drug amount value, and the pulse frequency value directly control the operation of the atomizer, ensuring stable drug delivery, accurate dosage, and uniform particle distribution. The atomization mode instruction information integrates the control parameters and provides operation guidance for the atomizer.

[0048] In one embodiment, the control module is used to obtain actual drug concentration parameters, atomized drug dose parameters, and drug particle distribution parameters based on the drug release data, obtain drug concentration fluctuation coefficient, atomized drug dose fluctuation coefficient, and drug particle distribution fluctuation coefficient based on the user's breathing-atomization matching degree, obtain airflow fluctuation range and airflow adjustment direction based on the drug concentration parameters and drug concentration fluctuation coefficient, generate airflow adjustment data based on the airflow fluctuation range and airflow adjustment direction, obtain atomized drug dose fluctuation range and atomized drug dose adjustment direction per unit time based on the atomized drug dose parameters and the atomized drug dose fluctuation coefficient, obtain atomized drug dose adjustment data based on the atomized drug dose fluctuation range and atomized drug dose adjustment direction per unit time, obtain pulse frequency fluctuation value and pulse frequency adjustment direction based on the drug particle distribution parameters and the drug particle distribution fluctuation coefficient, generate pulse adjustment data based on the pulse frequency fluctuation value and pulse frequency adjustment direction, and obtain nebulizer control information based on the airflow adjustment data, atomized drug dose adjustment data, and pulse adjustment data.

[0049] Drug release data is a comprehensive collection of information on drug release during nebulized therapy, covering multiple aspects such as the rate, timing, and spatial distribution of drug release. Actual drug concentration parameters reflect the actual concentration of the drug in the nebulized environment (such as the nebulizer chamber or the patient's respiratory tract). Nebulized drug volume parameters represent the total amount of drug delivered by the nebulizer within a specific timeframe and determine the amount of drug a patient receives during treatment. Drug particle distribution parameters describe the spatial distribution characteristics of nebulized drug particles, including the particle size distribution range and density distribution. Proper distribution facilitates uniform drug deposition in the respiratory tract.

[0050] The user's breathing-nebulization matching degree is a value derived from comprehensive factors such as the atomized particle size, airflow intensity, atomized drug volume per unit time, and respiratory assessment index. It measures the degree of adaptation of the nebulizer operating parameters to the user's respiratory status. The higher the matching degree, the more compatible the current atomization treatment plan is with the patient's respiratory condition, and the better the drug inhalation effect may be. The drug concentration fluctuation coefficient is used to quantify the degree of fluctuation of drug concentration over time or respiratory cycle during atomization treatment. It is a dimensionless coefficient. The larger the coefficient, the more drastic the fluctuation of drug concentration and the worse the stability. The atomized drug volume fluctuation coefficient measures the fluctuation of atomized drug volume during treatment. It is also a dimensionless value. The larger the coefficient, the greater the fluctuation amplitude of atomized drug volume, which may affect the stability of the patient's inhaled drug each time. The drug particle distribution fluctuation coefficient describes the degree of fluctuation of drug particle distribution in time or space. The larger the coefficient, the worse the stability of drug particle distribution, which may lead to uneven deposition of drugs in the respiratory tract.

[0051] The airflow fluctuation range refers to the variation interval of airflow parameters (such as velocity, pressure, etc.) within a certain period of time. It defines the adjustable range of the airflow and provides a numerical reference for airflow adjustment. The airflow adjustment direction indicates the direction of airflow parameter adjustment, whether it is increasing or decreasing. Combined with the airflow fluctuation range, it can accurately guide the adjustment of airflow parameters. The airflow adjustment data are the specific data generated based on the airflow fluctuation range and the adjustment direction, which are used to precisely adjust the airflow parameters of the nebulizer to ensure the stability of the drug concentration. The fluctuation range of the atomization drug amount per unit time is the variation range of the atomization drug amount within a unit time, which reflects the fluctuation amplitude of the atomization drug amount and provides a basis for adjusting the atomization drug amount. The atomization drug amount adjustment direction clarifies the adjustment direction of whether the atomization drug amount is increasing or decreasing. Combined with the fluctuation range of the atomization drug amount per unit time, it realizes the precise control of the atomization drug amount. The atomization drug amount adjustment data are the data generated based on the above two, which are used to guide the nebulizer to precisely adjust the atomization drug amount per unit time to ensure that the patient inhales an appropriate dose of the drug. The pulse frequency fluctuation value represents the change value of the pulse frequency within a certain period of time, which reflects its fluctuation situation and provides a quantitative basis for pulse frequency adjustment. The pulse frequency adjustment direction indicates whether the pulse frequency is increasing or decreasing. Combined with the fluctuation value, it realizes precise adjustment. The pulse adjustment data are the data generated based on the pulse frequency fluctuation value and the adjustment direction, which are used to guide the nebulizer to adjust the pulse frequency and optimize the drug particle distribution. The nebulizer control information is a set of instructions generated by integrating the airflow adjustment data, the atomization drug amount adjustment data, and the pulse adjustment data, which are used to comprehensively guide the nebulizer to adjust the working parameters.

[0052] The control module first obtains the drug release data in real time by means of various sensors (such as drug concentration sensors, flow sensors, etc.) installed on the nebulizer and related parts. At the same time, the control module calculates the user's breathing - atomization matching degree.

[0053] After obtaining the data, the control module deeply analyzes the drug release data. From the time - series data of drug release, by calculating the accumulation of the drug release amount per unit time, the atomization drug amount parameter is obtained; using the real - time monitoring data of the drug concentration by the sensor, the actual drug concentration parameter is directly obtained; by analyzing the collected drug particle samples, counting the number and distribution of particles with different particle sizes, the drug particle distribution parameter is obtained.

[0054] The control module further determines the adjustment ranges and directions of each parameter. Taking the drug concentration parameter and the drug concentration fluctuation coefficient as examples, if within a period of time, the drug concentration parameter fluctuates frequently and the fluctuation coefficient is large, the control module will analyze its fluctuation trend. If the drug concentration shows an unstable state of first increasing and then rapidly decreasing, the control module determines that it is necessary to stabilize the drug concentration. At this time, it is determined that the air flow adjustment direction is to increase appropriately. Through the analysis of past data and empirical judgment, the air flow fluctuation range is determined. For example, the air flow speed is increased by 2 - 5 m / s on the current basis. Similarly, for the atomization drug amount parameter and the atomization drug amount fluctuation coefficient, if the atomization drug amount fluctuation coefficient shows a large fluctuation amplitude and the actual atomization drug amount is lower than the expected value, the control module determines that the adjustment direction of the atomization drug amount is to increase. By analyzing historical data, the atomization drug amount fluctuation range per unit time is determined. For example, the atomization drug amount per unit time is increased by 0.05 - 0.1 ml / min on the current basis. For the drug particle distribution parameter and the drug particle distribution fluctuation coefficient, if the drug particle distribution fluctuation coefficient is large and the distribution is uneven, the control module will determine the pulse frequency adjustment direction according to the specific distribution situation, such as increasing the pulse frequency, and at the same time calculate the pulse frequency fluctuation value. For example, the pulse frequency is increased by 5 - 10 Hz on the current basis.

[0055] Then, the control module generates corresponding adjustment data according to the determined adjustment ranges and directions. For air flow adjustment, according to the determined air flow fluctuation range and air flow adjustment direction, specific air flow adjustment data is generated, including the specific values for adjusting the air flow speed or pressure, the adjustment time interval, etc. For example, it is set that within the next 30 seconds, the air flow speed is gradually increased by 3 m / s. For atomization drug amount adjustment, based on the atomization drug amount fluctuation range per unit time and the atomization drug amount adjustment direction, atomization drug amount adjustment data is generated, accurate to the drug amount change value and adjustment period for each adjustment. For example, the atomization drug amount per unit time is increased by 0.08 ml every 2 minutes. For pulse frequency adjustment, according to the pulse frequency fluctuation value and the pulse frequency adjustment direction, pulse adjustment data is generated, specifying the specific value and adjustment method for pulse frequency adjustment. For example, within the next 1 minute, the pulse frequency is increased by 2 Hz every 10 seconds.

[0056] Finally, the control module integrates the generated air flow adjustment data, atomization drug amount adjustment data, and pulse adjustment data to form atomizer control information. And this control information is sent to the atomizer through the communication link. After receiving it, the atomizer automatically adjusts its working parameters such as air flow parameters, atomization drug amount output, and pulse frequency according to this information.

[0057] From the perspective of the beneficial effects achieved, through such a complete data processing and regulation process, the control module precisely analyzes the drug release data and accurately adjusts various parameters, enabling the drug to act more accurately on the respiratory lesion site. At the same time, the personalized adjustment data and regulation information generated based on the user's breathing-atomization matching degree fully consider the breathing states of different patients, and customize the atomization treatment plan for each patient. In addition, the drug concentration, atomization drug amount, and drug particle distribution are precisely controlled, effectively avoiding the situations of drug overdose or insufficiency. By optimizing the airflow parameters, atomization drug amount, and pulse frequency, the deposition efficiency of the drug in the respiratory tract is improved, and drug waste is reduced.

[0058] In one embodiment, the control module is further configured to construct a dynamic allocation model according to the atomizer regulation information, obtain the user's real-time breathing evaluation index, drug release evaluation data, and historical treatment response data based on the dynamic allocation model, obtain correction information according to the user's real-time breathing evaluation index, drug release evaluation data, and historical treatment response data, where the correction information includes an atomization pulse frequency correction value, a single-dose drug ejection correction value, and an air flow velocity correction value, control the atomizer to match the patient's breathing frequency based on the atomization pulse frequency correction value, control the atomization drug amount of the atomizer per unit time based on the single-dose drug ejection correction value, and control the real-time gas flow velocity of the atomizer based on the air flow velocity correction value.

[0059] The dynamic allocation model constructed by the control module realizes the real-time optimization of atomization treatment parameters by fusing multi-source data: taking the atomizer regulation information as a benchmark, combining the user's real-time breathing evaluation index (including expiratory flow rate, breathing frequency, tidal volume fluctuation ratio), drug release evaluation data (drug output, particle distribution, concentration), and historical treatment response data (parameter adjustment records, breathing change trends, onset time), and calculating the correction information using the reinforcement learning algorithm; among them, the frequency correction value is synchronized with the breathing phase through PID control, the dose correction value is dynamically adjusted based on the tidal volume stability, the flow velocity correction value is optimized and matched by combining the flow ratio and particle size, and the three form a closed loop through PWM modulation, stepper motor drive, and PID fan control.

[0060] As Figure 2 shown, the present invention also provides a method for adaptively controlling a medical atomizer based on real-time expiratory volume feedback, including: S1. Obtain the user's dynamic breathing feature data set in real time, and send the user's dynamic breathing feature data set to the control module; S2. Obtain a breathing evaluation index according to the user's dynamic breathing feature data set, obtain the atomization control information of the atomizer in real time, and obtain the user's breathing-atomization matching degree according to the breathing evaluation index and the atomization control information; S3. Determine whether the user's breathing - atomization matching degree is within a preset threshold range. If it is, determine that the atomizer is suitable for the user's breathing and no adjustment is required. If it is not, determine that the atomizer is not suitable for the user's breathing and generate atomization mode instruction information based on the user's breathing - atomization matching degree; S4. Execute atomizer adjustment control according to the atomization mode instruction information and obtain the drug release data of the user during atomizer control; S5. Obtain drug release evaluation data based on the drug release data, obtain atomizer regulation information based on the drug release evaluation data and the user's breathing - atomization matching degree, and correct the atomization control information according to the atomizer regulation information.

[0061] In one embodiment, the step of obtaining the user's dynamic breathing feature data set includes: S101. Obtain the real - time airflow data of the atomizer; S102. Obtain an exhalation flow rate data set, a breathing flow rate data set, and a tidal volume data set based on the real - time airflow data; S103. Obtain exhalation flow rate deviation data based on the exhalation flow rate data set; S104. Obtain breathing frequency fluctuation data based on the breathing flow rate data set; S105. Obtain tidal volume deviation data based on the tidal volume data set; S106. Generate a user's dynamic breathing feature data set based on the exhalation flow rate deviation data, the breathing frequency fluctuation data, and the tidal volume deviation data.

[0062] In one embodiment, the step of obtaining a breathing evaluation index based on the user's dynamic breathing feature data set includes: S201. Intercept the exhalation flow rate data in the user's dynamic breathing feature data set according to a first preset time interval to obtain a high - frequency exhalation flow rate data set and a low - frequency exhalation flow rate data set; S202. Intercept the breathing frequency data according to a second preset time interval to obtain a high - frequency breathing frequency data set and a low - frequency breathing frequency data set; S203. Intercept the tidal volume data according to a third preset time interval to obtain a high - tidal volume data set and a low - tidal volume data set; S204. Map the high - frequency exhalation flow rate data set and the low - frequency exhalation flow rate data set, the high - frequency breathing frequency data set and the low - frequency breathing frequency data set, and the high - tidal volume data set and the low - tidal volume data set to a breathing state feature tensor; S205. Obtain a breathing evaluation index based on the breathing state feature tensor.

[0063] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, value library, or other medium provided in this application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0064] It should be noted that in this article, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, device, article, or method including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such a process, device, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, device, article, or method including that element.

[0065] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent result or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. An adaptive control system for a medical nebulizer based on real-time exhaled volume feedback, characterized in that, Comprising: A control module, an atomization execution module, and a breathing feature analysis module, where the control module is communicatively connected to the atomization execution module and the breathing feature analysis module respectively; The breathing feature analysis module is configured to obtain a user's dynamic breathing feature data set in real time and send the user's dynamic breathing feature data set to the control module; The control module is configured to obtain a breathing evaluation index based on the user's dynamic breathing feature data set, and obtain atomization control information of the atomizer in real time, and obtain a user's breathing-atomization matching degree based on the breathing evaluation index and the atomization control information; The control module is configured to determine whether the user's breathing-atomization matching degree is within a preset threshold range. If it is, it is determined that the atomizer is suitable for the user's breathing; if not, it is determined that the atomizer is not suitable for the user's breathing, and then an atomization mode instruction information is generated based on the user's breathing-atomization matching degree; The atomization execution module is configured to perform atomizer adjustment control according to the atomization mode instruction information and obtain drug release data of the user during atomizer control; The control module is further configured to obtain drug release evaluation data based on the drug release data, and obtain atomizer regulation information based on the drug release evaluation data and the user's breathing-atomization matching degree, and correct the atomization control information according to the atomizer regulation information.

2. The adaptive control system of a medical nebulizer based on real-time exhaled volume feedback according to claim 1, wherein, The breathing feature analysis module is configured to obtain real-time air flow data of the atomizer, obtain an exhalation flow rate data set, a breathing flow rate data set, and a tidal volume data set based on the real-time air flow data, obtain exhalation flow rate deviation data based on the exhalation flow rate data set, obtain breathing frequency fluctuation data based on the breathing flow rate data set, obtain tidal volume deviation data based on the tidal volume data set, and generate a user's dynamic breathing feature data set based on the exhalation flow rate deviation data, the breathing frequency fluctuation data, and the tidal volume deviation data; The control module is further configured to intercept the exhalation flow rate data in the user's dynamic breathing feature data set at a first preset time interval to obtain a high-frequency exhalation flow rate data set and a low-frequency exhalation flow rate data set, intercept the breathing frequency data at a second preset time interval to obtain a high-frequency breathing frequency data set and a low-frequency breathing frequency data set, intercept the tidal volume data at a third preset time interval to obtain a high tidal volume data set and a low tidal volume data set, map the high-frequency exhalation flow rate data set and the low-frequency exhalation flow rate data set, the high-frequency breathing frequency data set and the low-frequency breathing frequency data set, the high tidal volume data set and the low tidal volume data set to a breathing state feature tensor, and obtain a breathing evaluation index based on the breathing state feature tensor.

3. The adaptive control system of a medical nebulizer based on real-time exhaled volume feedback according to claim 2, wherein The control module is further configured to obtain the user's basic physiological data, obtain an expiratory flow correction coefficient, a respiratory rate correction coefficient, and a tidal volume correction coefficient based on the user's basic physiological data, obtain an expiratory flow fluctuation ratio based on the high-frequency expiratory flow data set and the low-frequency expiratory flow data set, obtain a respiratory rate ratio based on the high-frequency respiratory rate data set and the low-frequency respiratory rate data set, obtain a tidal volume ratio based on the high tidal volume data set and the low tidal volume data set, and obtain a respiratory assessment index based on the expiratory flow correction coefficient and the expiratory flow fluctuation ratio, the respiratory rate correction coefficient and the respiratory rate ratio, and the tidal volume correction coefficient and the tidal volume ratio.

4. The adaptive control system of a medical nebulizer based on real-time expiratory volume feedback according to claim 1, wherein The control module is configured to obtain atomization control information for the operation of the nebulizer. Among them, the atomization control information includes the real-time pulse frequency of the nebulizer, the real-time air flow velocity of the nebulizer, and the drug dose ejected by the nebulizer per injection. Obtain the atomized particle size value according to the real-time pulse frequency, obtain the air flow intensity according to the real-time air flow velocity of the nebulizer, obtain the drug amount atomized per unit time value according to the drug dose ejected by the nebulizer per injection, and obtain the user's respiration-nebulization matching degree according to the atomized particle size value, the air flow intensity, the drug amount atomized per unit time, and the respiratory assessment index.

5. The adaptive control system of a medical nebulizer based on real-time expiratory volume feedback according to claim 4, wherein The atomization execution module is configured to obtain a drug concentration fluctuation coefficient, a drug dose deviation coefficient, and a drug particle distribution uniformity coefficient according to the user's respiration-nebulization matching degree, obtain an air flow-drug concentration fluctuation value according to the drug concentration fluctuation coefficient and the air flow intensity, obtain a drug dose deviation value according to the drug dose deviation coefficient and the drug amount atomized per unit time, obtain the drug particle distribution uniformity according to the drug particle distribution uniformity coefficient and the atomized particle size value, generate an air flow control value according to the air flow-drug concentration fluctuation value, obtain an atomized drug amount value according to the drug dose deviation value, obtain a pulse frequency value according to the drug particle distribution uniformity, and generate atomization mode instruction information according to the air flow control value, the atomized drug amount value, and the pulse frequency value.

6. The adaptive control system of a medical nebulizer based on real-time exhaled volume feedback according to claim 1, wherein The control module is configured to obtain the actual drug concentration parameter, atomized drug amount parameter, and drug particle distribution parameter according to the drug release data, obtain the drug concentration fluctuation coefficient, atomized drug amount fluctuation coefficient, and drug particle distribution fluctuation coefficient according to the user's respiration-nebulization matching degree, obtain the air flow fluctuation range and air flow adjustment direction according to the drug concentration parameter and the drug concentration fluctuation coefficient, generate air flow adjustment data according to the air flow fluctuation range and the air flow adjustment direction, obtain the drug amount atomized per unit time fluctuation range and atomized drug amount adjustment direction according to the atomized drug amount parameter and the atomized drug amount fluctuation coefficient, obtain atomized drug amount adjustment data according to the drug amount atomized per unit time fluctuation range and the atomized drug amount adjustment direction, obtain the pulse frequency fluctuation value and pulse frequency adjustment direction according to the drug particle distribution parameter and the drug particle distribution fluctuation coefficient, generate pulse adjustment data according to the pulse frequency fluctuation value and the pulse frequency adjustment direction, and generate air flow adjustment data, atomized drug amount adjustment data, and pulse adjustment data information according to the air flow adjustment data, the atomized drug amount adjustment data, and the pulse adjustment data.

7. An adaptive control system for a medical nebulizer based on real-time exhaled volume feedback according to claim 1, characterized in that, The control module is further configured to construct a dynamic allocation model according to the atomizer regulation information, obtain a user's real-time breathing evaluation index, drug release evaluation data, and historical treatment response data based on the dynamic allocation model, obtain correction information according to the user's real-time breathing evaluation index, drug release evaluation data, and historical treatment response data, where the correction information includes an atomization pulse frequency correction value, a single drug ejection dose correction value, and an air flow velocity correction value, control the atomizer to match the patient's breathing frequency based on the atomization pulse frequency correction value, control the atomization drug amount of the atomizer per unit time based on the single drug ejection dose correction value, and control the real-time gas flow velocity of the atomizer based on the air flow velocity correction value.

8. An adaptive control method for a medical nebulizer based on real-time exhaled volume feedback, characterized in that, It includes: Obtain a user's dynamic breathing feature data set in real time and send the user's dynamic breathing feature data set to the control module; Obtain a breathing evaluation index according to the user's dynamic breathing feature data set, obtain atomization control information of the atomizer in real time, and obtain a user's breathing-atomization matching degree according to the breathing evaluation index and the atomization control information; Judge whether the user's breathing-atomization matching degree is within a preset threshold range. If it is, judge that the atomizer is suitable for the user's breathing and no adjustment is required. If it is not, judge that the atomizer is not suitable for the user's breathing, and generate atomization mode instruction information according to the user's breathing-atomization matching degree; Execute atomizer adjustment control according to the atomization mode instruction information and obtain drug release data of the user during atomizer control; Obtain drug release evaluation data according to the drug release data, obtain atomizer regulation information according to the drug release evaluation data and the user's breathing-atomization matching degree, and correct the atomization control information according to the atomizer regulation information.

9. The adaptive control method of a medical nebulizer based on real-time exhaled volume feedback according to claim 7, characterized in that, The step of obtaining the user's dynamic breathing feature data set includes: Obtain real-time air flow data of the atomizer; Obtain an exhalation flow data set, a breathing flow data set, and a tidal volume data set according to the real-time air flow data; Obtain exhalation flow deviation data according to the exhalation flow data set; Obtain breathing frequency fluctuation data according to the breathing flow data set; Obtain tidal volume deviation data according to the tidal volume data set; Generate a user's dynamic breathing feature data set according to the exhalation flow deviation data, the breathing frequency fluctuation data, and the tidal volume deviation data.

10. The adaptive control method for a medical nebulizer based on real-time exhaled volume feedback according to claim 7, characterized in that, The step of obtaining the breathing evaluation index according to the user's dynamic breathing feature data set includes: Intercept the exhalation flow data in the user's dynamic breathing feature data set according to a first preset time interval to obtain a high-frequency exhalation flow data set and a low-frequency exhalation flow data set; Intercept the breathing frequency data according to a second preset time interval to obtain a high-frequency breathing frequency data set and a low-frequency breathing frequency data set; Intercept the tidal volume data according to a third preset time interval to obtain a high tidal volume data set and a low tidal volume data set; Map the high-frequency exhalation flow data set and the low-frequency exhalation flow data set, the high-frequency breathing frequency data set and the low-frequency breathing frequency data set, and the high tidal volume data set and the low tidal volume data set to a breathing state feature tensor; Obtain a respiration assessment index based on the respiration state feature tensor.

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