An Adaptive Control Method and System for Medical Nebulizers Based on Real-Time Expiratory Volume Feedback

Abstract

This invention relates to the field of nebulizer technology, specifically disclosing a medical nebulizer adaptive control method and system based on real-time expiratory volume feedback. The system includes a control module, a nebulization execution module, and a respiratory feature analysis module. The control module is communicatively connected to both the nebulization execution module and the respiratory feature analysis module. The respiratory feature analysis module acquires a user's dynamic respiratory feature dataset in real time and sends it to the control module. The control module acquires a respiratory assessment index based on the user's dynamic respiratory feature dataset and acquires nebulizer control information in real time. It then obtains the user's respiratory-nebulization matching degree based on the respiratory assessment index and the nebulizer control information. This invention enables a deep match between nebulization parameters and the patient's respiratory state and individual characteristics, improving drug deposition efficiency and treatment accuracy. In terms of data monitoring and application, the nebulization execution module monitors drug release data in stages.

Description

Technical Field

[0001] This invention relates to the field of nebulizer technology, and in particular to an adaptive control method and system for medical nebulizers based on real-time expiratory volume feedback. Background Technology

[0002] Medical nebulizers are essential medical devices that atomize medications into tiny particles, allowing patients to inhale them through the respiratory tract for therapeutic purposes. With the development of medical technology, the application of adaptive control technology in medical nebulizers has gradually gained attention. Adaptive control in medical nebulizers aims to dynamically adjust nebulization parameters based on individual patient differences and real-time physiological states.

[0003] Current adaptive control technologies for medical nebulizers still have many shortcomings. Specifically, single-parameter monitoring can only obtain information on one dimension of respiratory characteristics, such as respiratory rate alone. It cannot take into account the low drug inhalation efficiency caused by insufficient expiratory flow rate, or the impact of tidal volume fluctuations on drug deposition. At the same time, these methods lack in-depth analysis of the dynamic correlation between multiple respiratory parameters, ignoring the coordinated changes in expiratory flow rate, respiratory rate, and tidal volume of different users during the respiratory process, and thus cannot accurately adaptively adjust. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive control method and system for medical nebulizers based on real-time expiratory volume feedback, so as to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An adaptive control system for a medical nebulizer based on real-time expiratory volume feedback, comprising:

[0007] The system includes a control module, a nebulization execution module, and a respiratory feature analysis module, wherein the control module is communicatively connected to the nebulization execution module and the respiratory feature analysis module, respectively.

[0008] The respiratory feature analysis module is used to acquire the user's dynamic respiratory feature dataset in real time and send the user's dynamic respiratory feature dataset to the control module;

[0009] The control module is used to obtain a breathing assessment index based on the user's dynamic breathing feature dataset, and to obtain the nebulizer control information in real time. The module also obtains the user's breathing-nebulization matching degree based on the breathing assessment index and the nebulizer control information.

[0010] The control module is used to determine whether the user's breathing-nebulization matching degree is within a preset threshold range. If it is, it is determined that the nebulizer is suitable for the user's breathing; if it is not, it is determined that the nebulizer is not suitable for the user's breathing, and then nebulization mode instruction information is generated according to the user's breathing-nebulization matching degree.

[0011] The nebulization execution module is used to perform nebulizer adjustment control according to the nebulization mode instruction information, and to obtain drug release data when the user controls the nebulizer;

[0012] The control module is also used to obtain drug release assessment data based on the drug release data, and to obtain nebulizer control information based on the drug release assessment data and the user's breathing-nebulization matching degree, and to correct the nebulization control information based on the nebulizer control information.

[0013] Preferably, the respiratory feature analysis module is used to acquire real-time airflow data of the nebulizer, acquire expiratory flow data set, respiratory flow data set and tidal volume data set based on the real-time airflow data, acquire expiratory flow deviation data based on the expiratory flow data set, acquire respiratory rate fluctuation data based on the respiratory flow data set, acquire tidal volume deviation data based on the tidal volume data set, and generate a user dynamic respiratory feature dataset based on the expiratory flow deviation data, respiratory rate fluctuation data and tidal volume deviation data;

[0014] The control module is further configured to extract expiratory flow data from the user's dynamic respiratory feature dataset according to a first preset time interval to obtain a high-frequency expiratory flow data set and a low-frequency expiratory flow data set; extract respiratory frequency data according to a second preset time interval to obtain a high-frequency respiratory frequency data set and a low-frequency respiratory frequency data set; extract 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 expiratory flow data set and the low-frequency expiratory flow data set, the high-frequency respiratory frequency data set and the low-frequency respiratory frequency data set, the high-tidal volume data set and the low-tidal volume data set into a respiratory state feature tensor; and obtain a respiratory assessment index based on the respiratory state feature tensor.

[0015] Preferably, the control module is further configured to acquire basic physiological data of the user, acquire expiratory flow correction coefficient, respiratory rate correction coefficient, and tidal volume correction coefficient based on the basic physiological data of the user, acquire expiratory flow fluctuation ratio based on the high-frequency expiratory flow data set and the low-frequency expiratory flow data set, acquire respiratory rate ratio based on the high-frequency respiratory rate data set and the low-frequency respiratory rate data set, acquire tidal volume ratio based on the tidal volume data set and the low tidal volume data set, and acquire a respiratory assessment index based on the expiratory flow correction coefficient and 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.

[0016] Preferably, the control module is used to acquire nebulization control information of the nebulizer operation, wherein the nebulization control information includes the real-time pulse frequency of the nebulizer, the real-time airflow velocity of the nebulizer, and the single dose of medication sprayed by the nebulizer. The nebulized particle size value is obtained based on the real-time pulse frequency, the airflow intensity is obtained based on the real-time airflow velocity of the nebulizer, the nebulized drug dosage per unit time is obtained based on the single dose of medication sprayed by the nebulizer, and the user's breathing-nebulization matching degree is obtained based on the nebulized particle size value, airflow intensity, nebulized drug dosage per unit time, and respiratory assessment index.

[0017] Preferably, the nebulization execution module is used to obtain a drug concentration fluctuation coefficient, a drug dosage deviation coefficient, and a drug particle distribution uniformity coefficient based on the user's breathing-nebulization matching degree; to obtain an airflow-drug concentration fluctuation value based on the drug concentration fluctuation coefficient and airflow intensity; to obtain a drug dosage deviation value based on the drug dosage deviation coefficient and the amount of drug nebulized per unit time; to obtain a drug particle distribution uniformity based on the drug particle distribution uniformity coefficient and the size of the nebulized particles; to generate an airflow control value based on the airflow-drug concentration fluctuation value; to obtain a nebulized drug dosage value based on the drug dosage deviation value; to obtain a pulse frequency value based on the drug particle distribution uniformity; and to generate nebulization mode instruction information based on the airflow control value, the nebulized drug dosage value, and the pulse frequency value.

[0018] Preferably, the control module is used to obtain actual drug concentration parameters, nebulized drug dosage parameters, and drug particle distribution parameters based on the drug release data; obtain drug concentration fluctuation coefficient, nebulized drug dosage fluctuation coefficient, and drug particle distribution fluctuation coefficient based on the user's breathing-nebulization 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 nebulized drug dosage fluctuation range and nebulized drug dosage adjustment direction per unit time based on the nebulized drug dosage parameters and nebulized drug dosage fluctuation coefficient; obtain nebulized drug dosage adjustment data based on the nebulized drug dosage fluctuation range and nebulized drug dosage adjustment direction per unit time; obtain pulse frequency fluctuation value and pulse frequency adjustment direction based on the drug particle distribution parameters and drug particle distribution fluctuation coefficient; generate pulse adjustment data based on the pulse frequency fluctuation value and pulse frequency adjustment direction; and combine the airflow adjustment data, nebulized drug dosage adjustment data, and pulse adjustment data information.

[0019] Preferably, the control module is further configured to construct a dynamic allocation model based on the nebulizer regulation information, obtain the user's real-time respiratory assessment index, drug release assessment data, and historical treatment response data based on the dynamic allocation model, and obtain correction information based on the user's real-time respiratory assessment index, drug release assessment data, and historical treatment response data. The correction information includes a nebulization pulse frequency correction value, a single-spray drug dose correction value, and an airflow velocity correction value. Based on the nebulization pulse frequency correction value, the nebulizer is controlled to match the patient's respiratory rate. Based on the single-spray drug dose correction value, the amount of nebulized drug in the nebulizer per unit time is controlled. Based on the airflow velocity correction value, the real-time gas flow rate of the nebulizer is controlled.

[0020] This invention also provides an adaptive control method for medical nebulizers based on real-time expiratory volume feedback, comprising:

[0021] The system acquires a real-time dataset of the user's dynamic breathing features and sends the dataset to the control module.

[0022] The breathing assessment index is obtained based on the user's dynamic breathing feature dataset, and the nebulizer operation nebulization control information is obtained in real time. The user's breathing-nebulization matching degree is obtained based on the breathing assessment index and the nebulizer control information.

[0023] Determine whether the user's breathing-nebulization matching degree is within a preset threshold range. If it is, determine that the nebulizer is suitable for the user's breathing and no adjustment is needed. If it is not, determine that the nebulizer is not suitable for the user's breathing and generate nebulization mode instruction information based on the user's breathing-nebulization matching degree.

[0024] The nebulizer adjustment control is executed according to the nebulization mode instruction information, and the drug release data of the user during nebulizer control is obtained;

[0025] Drug release assessment data is obtained based on the drug release data, and nebulizer control information is obtained based on the drug release assessment data and the user's breathing-nebulization matching degree. The nebulization control information is then corrected based on the nebulizer control information.

[0026] Preferably, the step of obtaining the user's dynamic breathing feature dataset includes:

[0027] Acquire real-time airflow data from the atomizer;

[0028] Based on the real-time airflow data, obtain the expiratory flow data set, the respiratory flow data set, and the tidal volume data set;

[0029] Expiratory flow deviation data are obtained based on the expiratory flow data set;

[0030] Respiratory rate fluctuation data are obtained from the respiratory flow data set;

[0031] Tidal volume deviation data are obtained based on the tidal volume data set;

[0032] A user dynamic respiratory feature dataset is generated based on expiratory flow deviation data, respiratory rate fluctuation data, and tidal volume deviation data.

[0033] Preferably, the step of obtaining the respiratory assessment index based on the user's dynamic respiratory feature dataset includes:

[0034] According to the first preset time interval, the expiratory flow data in the user dynamic breathing feature dataset is extracted to obtain a high-frequency expiratory flow data set and a low-frequency expiratory flow data set.

[0035] The respiratory rate data is extracted according to the second preset time interval to obtain a high-frequency respiratory rate data set and a low-frequency respiratory rate data set;

[0036] Tidal volume data are extracted according to the third preset time interval to obtain high tide volume data set and low tide volume data set;

[0037] The high-frequency expiratory flow rate data set, the low-frequency expiratory flow rate data set, the high-frequency respiratory rate data set, the high-tidal volume data set, and the low-tidal volume data set are mapped to respiratory state feature tensors.

[0038] The respiratory assessment index is obtained based on the respiratory state feature tensor.

[0039] The beneficial effects of this application are as follows: This invention uses a respiratory feature analysis module to collect data collaboratively from multiple sensors, and a control module employs data interception, tensor mapping, and other technologies to comprehensively integrate parameters such as expiratory flow rate, respiratory rate, and tidal volume, accurately obtaining the respiratory assessment index. This enables dynamic and multidimensional assessment of the patient's respiratory status. Regarding treatment parameter adjustment, the respiratory assessment index and drug release assessment data are used as the basis. The control module dynamically adjusts parameters such as nebulization pulse frequency and dosage, taking into account airflow fluctuations, dosage errors, and differences in patient lung function. This changes the traditional "one-size-fits-all" approach, allowing nebulization parameters to be deeply matched with the patient's respiratory status and individual characteristics, improving drug deposition efficiency and treatment accuracy. In terms of data monitoring and application, the nebulization execution module monitors drug release data in stages, and the control module deeply mines information such as concentration fluctuations and particle distribution, forming a closed-loop feedback mechanism. This provides a comprehensive basis for optimizing treatment plans, effectively reducing drug waste and lowering the risk of adverse reactions caused by parameter mismatch. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the system structure according to an embodiment of this application.

[0041] Figure 2 This is a schematic diagram of a method flow according to an embodiment of this application.

[0042] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0043] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0044] like Figure 1As shown, this application provides an adaptive control system for a medical nebulizer based on real-time expiratory volume feedback, including: a control module, a nebulization execution module, and a respiratory feature analysis module. The control module is communicatively connected to both the nebulization execution module and the respiratory feature analysis module. The respiratory feature analysis module is used to acquire a user's dynamic respiratory feature dataset in real time and send the dataset to the control module. The control module is used to acquire a respiratory assessment index based on the user's dynamic respiratory feature dataset and to acquire nebulization control information of the nebulizer in real time. It also acquires the user's breathing-nebulization matching degree based on the respiratory assessment index and the nebulization control information. The control module is used for... The system determines whether the user's breathing-nebulization matching degree is within a preset threshold range. If it is, the nebulizer is deemed suitable for the user's breathing and no adjustment is needed. If it is not, the nebulizer is deemed unsuitable for the user's breathing, and nebulization mode instruction information is generated based on the user's breathing-nebulization matching degree. The nebulization execution module is used to execute nebulizer adjustment control according to the nebulization mode instruction information and acquire drug release data when the user controls the nebulizer. The control module is also used to acquire drug release evaluation data based on the drug release data, acquire nebulizer control information based on the drug release evaluation data and the user's breathing-nebulization matching degree, and correct the nebulization control information based on the nebulizer control information.

[0045] As mentioned above, existing manual adjustment methods cannot accurately and in real time perceive changes in the patient's respiratory status, nor can they flexibly adjust nebulization parameters according to the specific characteristics of the medication. This makes it difficult for nebulizers to achieve optimal matching with the patient's respiratory status and the medication used in actual use. For example, when the patient is breathing rapidly and has a large expiratory flow rate, a fixed-level nebulizer may not be able to provide a sufficiently fine and appropriate amount of atomized particles in time, resulting in insufficient drug deposition in the effective respiratory tract and wasted medication; while when the patient's breathing is weak, there may be situations where the drug dosage is too high or the particles are too large to inhale. Based on this, this invention proposes an adaptive control system for medical nebulizers based on real-time expiratory volume feedback, aiming to overcome the shortcomings of these existing technologies and achieve more efficient and precise nebulization therapy. This invention mainly consists of a control module, a nebulization execution module, and a respiratory feature analysis module. The control module establishes communication connections with the nebulization execution module and the respiratory feature analysis module to achieve collaborative work between the modules. The respiratory feature analysis module acquires the user's dynamic respiratory feature dataset in real time, collecting real-time airflow data of the nebulizer using various sensors such as pressure sensors and flow sensors installed on the nebulizer. From these real-time airflow data, expiratory flow rate (EVF), respiratory flow rate (RVF), and tidal volume (TV) datasets are extracted. The EVF dataset records the flow rate variation during each exhalation; the RVF dataset covers the flow rate dynamics throughout the entire respiratory cycle; and the TV dataset reflects the volume of air inhaled or exhaled with each breath. Next, characteristic parameters are calculated for each dataset. Each data point in the EVF dataset is compared to a preset standard EVF model to obtain EVF deviation data, which measures the degree of difference between the user's EVF and the standard. Time series analysis algorithms are applied to the RVF dataset to calculate the frequency and amplitude of RVF changes at adjacent time points, obtaining respiratory rate fluctuation data to reflect the stability and regularity of respiratory rate. The TV dataset is compared with the user's baseline TV data and the average TV data of similar users to obtain TV deviation data, determining whether the TV is within the normal range. Finally, the EVF deviation data, respiratory rate fluctuation data, and TV deviation data are integrated into a dynamic respiratory feature dataset for the user and sent to the control module.

[0046] The control module receives the user's dynamic respiratory feature dataset from the respiratory feature analysis module. First, it extracts expiratory flow data according to a first preset time interval, calculating the mean, standard deviation, and other statistical characteristics of expiratory flow within each time period. The data is then divided into high-frequency and low-frequency expiratory flow datasets based on flow rate. Similarly, respiratory rate and tidal volume data are processed to obtain high-frequency, low-frequency, high-tidal volume, and low-tidal volume datasets. These six datasets are mapped to a respiratory state feature tensor. Based on this tensor, a multi-dimensional fit vector is generated, encompassing expiratory flow stability, respiratory rate regularity, and tidal volume consistency, thus obtaining the respiratory assessment index. The respiratory assessment index integrates the stability, regularity, and consistency of expiratory flow, respiratory rate, and tidal volume, visually reflecting the user's respiratory stability in numerical form. A higher index indicates a more stable respiratory state.

[0047] Next, the control module acquires real-time nebulizer control information, including the nebulizer's real-time pulse frequency, real-time airflow velocity, and single-dose drug delivery. The particle size is calculated based on the real-time pulse frequency, which determines the particle generation rate and size; higher frequencies result in finer particles. The airflow intensity is determined by the real-time airflow velocity, and the drug delivery rate per unit time is obtained from the single-dose drug delivery. Then, these values ​​are combined with a respiratory assessment index to obtain the user's breathing-nebulizer matching degree using a specific algorithm. This matching degree reflects the degree of adaptation between the nebulizer's operating parameters and the user's breathing state.

[0048] Next, the control module makes a decision based on the breath-nebulization matching degree. It determines whether the breath-nebulization matching degree is within a preset threshold range. If it is, the nebulizer is deemed suitable and no adjustment is needed; if not, nebulization mode instruction information is generated based on the matching degree.

[0049] After acquiring drug release data, the control module uses this data to obtain actual drug concentration parameters, nebulized drug dosage parameters, and drug particle distribution parameters. Combined with the respiration-nebulization matching degree, it obtains corresponding drug concentration fluctuation coefficients, nebulized drug dosage fluctuation coefficients, and drug particle distribution fluctuation coefficients. These coefficients and parameters are used to calculate data such as airflow fluctuation range, airflow adjustment direction, nebulized drug dosage fluctuation range per unit time, nebulized drug dosage adjustment direction, pulse frequency fluctuation value, and pulse frequency adjustment direction. This generates airflow adjustment data, nebulized drug dosage adjustment data, and pulse adjustment data, ultimately obtaining nebulizer control information and correcting the nebulization control information. The control module achieves precise quantitative assessment of respiratory status and dynamically adjusts the nebulization strategy based on the nebulizer's operating status, ensuring a deep match between nebulization parameters and the patient's respiratory status. By continuously optimizing nebulization control information, drug deposition efficiency and treatment accuracy are improved, drug waste is reduced, and the risk of adverse reactions is lowered.

[0050] Finally, the nebulization execution module is responsible for adjusting the nebulizer according to the nebulization mode command information generated by the control module, acquiring drug release data during the nebulization process, receiving nebulization mode command information sent by the control module, and adjusting the nebulizer's operating parameters, such as pulse frequency and single-spray drug dosage, according to the command. During nebulization treatment, the module extracts the user's early respiratory cycle set during the nebulization phase according to a first preset respiratory cycle number and the later respiratory cycle set according to a second preset respiratory cycle number, comparing them with preset standard drug release concentrations to obtain early and later concentration deviation data. Through data processing algorithms, these concentration deviation data are integrated with the corresponding respiratory cycle time, patient respiratory parameters, and other information to generate drug release data, which is then fed back to the control module. Simultaneously, based on the user's breathing-nebulization matching degree, the drug concentration fluctuation coefficient, drug dosage deviation coefficient, and drug particle distribution uniformity coefficient are obtained. The module calculates the airflow-drug concentration fluctuation value, drug dosage deviation value, and drug particle distribution uniformity, thereby generating airflow control value, nebulized drug dosage value, and pulse frequency value, and finally generating nebulization mode command information to form a closed-loop control with the control module. The drug release data includes information such as drug release concentration and release rate per unit time during nebulization therapy, used to evaluate drug release effectiveness. Drug concentration fluctuation coefficient, drug dose deviation coefficient, and drug particle distribution uniformity coefficient are used to measure drug concentration fluctuation, dose deviation, and particle distribution uniformity. The airflow-drug concentration fluctuation value reflects the magnitude of drug concentration fluctuation; the drug dose deviation value reflects the deviation between the actual drug dose and the standard dose; and the drug particle distribution uniformity measures the spatial uniformity of drug particle distribution. Airflow control values, nebulized drug dosage values, and pulse frequency values ​​are used to adjust the nebulizer's airflow, drug dosage, and pulse frequency. This module enables precise adjustment of the nebulizer, ensuring drug release with appropriate parameters, and monitors drug release data in stages, providing comprehensive and accurate feedback to the control module.

[0051] In one embodiment, the respiratory feature analysis module acquires real-time airflow data from the nebulizer, and obtains expiratory flow rate data sets, respiratory flow rate data sets, and tidal volume data sets based on the real-time airflow data. It also acquires expiratory flow rate deviation data, respiratory rate fluctuation data, and tidal volume deviation data based on the expiratory flow rate data set. Finally, it generates a user dynamic respiratory feature dataset based on the expiratory flow rate deviation data, respiratory rate fluctuation data, and tidal volume deviation data. The sensor array includes various types of sensors such as pressure sensors and flow sensors, acting like sensitive "sensory organs" capable of capturing subtle changes in airflow from multiple dimensions, providing a rich and accurate data foundation for subsequent analysis. After acquiring the real-time airflow data, the respiratory feature analysis module begins in-depth data deconstruction and analysis. First, using a specific data separation algorithm, it accurately extracts the expiratory flow rate data set, respiratory flow rate data set, and tidal volume data set from the real-time airflow data. This process is similar to separating and purifying a complex mixture. The expiratory flow data set records the flow changes during each exhalation, the respiratory flow data set covers the flow dynamics throughout the entire respiratory cycle, and the tidal volume data set reflects the volume of air inhaled or exhaled with each breath. Next, the module calculates characteristic parameters for each data set. For the expiratory flow data set, it compares it with a preset standard expiratory flow model, calculating the deviation value of each data point from the standard model, thus obtaining expiratory flow deviation data. This is analogous to comparing the user's expiratory flow performance with a "health template" to identify differences. Based on the respiratory flow data set, the module uses time series analysis algorithms to calculate the frequency and amplitude of respiratory flow changes at adjacent time points, thereby obtaining respiratory rate fluctuation data to reflect the stability and regularity of the user's respiratory rate. When processing the tidal volume dataset, the system compares the user's baseline tidal volume data with the average tidal volume data of users of the same type to calculate the tidal volume deviation data and determine whether the tidal volume is within the normal and reasonable range. Finally, the respiratory feature analysis module integrates the expiratory flow deviation data, respiratory rate fluctuation data, and tidal volume deviation data to generate a dynamic respiratory feature dataset for the user. (The respiratory feature analysis module first uses a filtering algorithm to remove noise from the expiratory flow deviation data, respiratory rate fluctuation data, and tidal volume deviation data. Then, it uses time synchronization technology to align the data collected at different frequencies. Next, it uses a weighted fusion algorithm to dynamically assign weights to each data point based on the disease type and respiratory stage. This effectively overcomes problems such as data clutter, time misalignment, and inability to reflect individual differences, ultimately generating a dynamic respiratory feature dataset that accurately reflects the user's real-time respiratory status and predicts respiratory change trends.)

[0052] In one embodiment, the control module extracts expiratory flow data from the user's dynamic respiratory feature dataset according to a first preset time interval to obtain a high-frequency expiratory flow data set and a low-frequency expiratory flow data set; extracts respiratory frequency data according to a second preset time interval to obtain a high-frequency respiratory frequency data set and a low-frequency respiratory frequency data set; and extracts 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. The high-frequency and low-frequency expiratory flow data sets, the high-frequency and low-frequency respiratory frequency data sets, and the high-tidal volume and low-tidal volume data sets are mapped to a respiratory state feature tensor. A multidimensional tensor containing expiratory flow stability, respiratory frequency regularity, and tidal volume consistency is generated based on the respiratory state feature tensor. The fit vector is used to obtain the respiratory assessment index. The control module first processes the user's dynamic respiratory feature dataset. It extracts expiratory flow data at a first preset time interval and obtains the mean and standard deviation statistical characteristics of expiratory flow in each time period. The data is divided into high-frequency expiratory flow data set and low-frequency expiratory flow data set according to the flow rate. The respiratory frequency data is processed at a second preset time interval and its changes at different time periods are observed. Data above the normal range are included in the high-frequency respiratory frequency data set, and those below the normal range are included in the low-frequency respiratory frequency data set. The tidal volume data is extracted at a third preset time interval and the mean tidal volume in each time period is compared with the standard value. Data greater than the standard value are included in the high tidal volume data set, and data less than the standard value are included in the low tidal volume data set. Then, the control module maps the above six sets of data into a respiratory state feature tensor. Tensors, as a multidimensional data structure, integrate expiratory flow rate, respiratory rate, and tidal volume data, systematically presenting the correlation between various parameters and achieving a multidimensional representation of respiratory status. Compared to traditional single-parameter analysis, it can more comprehensively reflect the complex characteristics of respiratory status. Subsequently, based on the respiratory status feature tensor, the control module generates a multidimensional fit vector containing expiratory flow rate stability, respiratory rate regularity, and tidal volume consistency. (The control module first extracts historical and real-time data of expiratory flow rate, respiratory rate, and tidal volume from the respiratory status feature tensor, uses a sliding window algorithm to calculate the standard deviation and coefficient of variation of each indicator at different time scales to quantify stability, regularity, and consistency, then normalizes the data to ensure uniformity of dimensions, and finally combines these quantification results into a multidimensional fit vector according to preset weights.) Among them, expiratory flow rate stability is calculated based on the fluctuation of high-frequency and low-frequency expiratory flow rate data sets; smaller fluctuations indicate higher stability. Respiratory rate regularity is determined by analyzing high-frequency and low-frequency respiratory rate data sets to determine whether respiratory rate changes are stable. Tidal volume consistency is assessed by comparing high-value and low-value tidal volume data sets to evaluate the degree of difference in tidal volume across different respiratory cycles.This multidimensional fit vector quantifies the characteristics and interrelationships of various respiratory parameters, forming a precise quantitative description of the respiratory state. Finally, the control module obtains the respiratory assessment index based on the multidimensional fit vector. (The control module first performs nonlinear mapping on the expiratory flow stability, respiratory rate regularity, and tidal volume consistency indicators in the multidimensional fit vector, converting them into confidence scores in the 0-1 interval. Then, it calculates the conditional probability distribution of each indicator through a dynamic Bayesian network, adjusts the weights by combining prior knowledge of disease type and treatment stage, introduces time-varying factors to capture the dynamic changes in respiratory state, and finally generates a respiratory assessment index that comprehensively reflects respiratory quality and treatment suitability through weighted aggregation.) This index comprehensively considers the stability, regularity, and consistency of expiratory 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. Multi-parameter collaborative analysis, which considers expiratory flow rate, respiratory rate, and tidal volume simultaneously, breaks through the limitations of single-parameter evaluation and comprehensively captures changes in respiratory state. Using tensor and multidimensional fit vector technology, it deeply mines the complex relationships between data to achieve precise quantification of respiratory state. Based on this index, nebulizer parameters and medication dosage can be adjusted to improve the user's respiratory state.

[0053] In one embodiment, the control module is further configured to acquire basic physiological data of the user, acquire expiratory flow correction coefficient, respiratory rate correction coefficient, and tidal volume correction coefficient based on the basic physiological data, acquire multiple high-frequency expiratory flow values ​​based on the high-frequency expiratory flow data set, acquire multiple low-frequency expiratory flow values ​​based on the low-frequency expiratory flow data set, acquire a first standard expiratory value corresponding to the high-frequency expiratory flow data set, acquire a second standard expiratory value corresponding to the low-frequency expiratory flow data set, and calculate an expiratory flow fluctuation ratio based on the multiple high-frequency expiratory flow values, the multiple low-frequency expiratory flow values, the first standard expiratory value, and the second standard expiratory value, wherein the calculation formula is:

[0054] ;

[0055] Where A represents the expiratory flow rate fluctuation ratio, m1 represents the first high-frequency expiratory flow rate value, m2 represents the second high-frequency expiratory flow rate value, and m... i Let n represent the i-th high-frequency expiratory flow rate value, k represent the first standard expiratory value, n1 represent the first low-frequency expiratory flow rate value, n2 represent the second low-frequency expiratory flow rate value, and n... u Let u represent the u-th low-frequency expiratory flow rate value, and I represent the second standard expiratory flow rate value.

[0056] The expiratory flow fluctuation ratio measures the fluctuation of expiratory flow, reflecting the degree of dispersion of expiratory flow relative to its respective standard value at different levels (high frequency and low frequency), where m1, m2...m iThis represents the individual expiratory flow measurements selected from the high-frequency expiratory flow dataset. These measurements record the specific flow rate values ​​during the higher phases of expiratory flow, reflecting the changes in expiratory flow rate during respiration when it is high. The difference between these values ​​and the first expiratory standard value k indicates the degree of deviation of the high-frequency expiratory flow rate from the standard value. n1, n2...n u This represents the individual expiratory flow measurements in the low-frequency expiratory flow data set, reflecting the specific flow changes during the lower stages of expiratory flow.

[0057] Multiple high-frequency respiratory rate values ​​are obtained from the high-frequency respiratory rate data set, and multiple low-frequency respiratory rate values ​​are obtained from the low-frequency respiratory rate data set. A first standard respiratory rate value corresponding to the high-frequency respiratory rate data set is obtained, and a second standard respiratory rate value corresponding to the low-frequency respiratory rate data set is obtained. A respiratory rate ratio is calculated based on the multiple high-frequency respiratory rate values, the multiple low-frequency respiratory rate values, the first standard respiratory rate value, and the second standard respiratory rate value. The calculation formula is as follows:

[0058] ;

[0059] Where B represents the respiratory rate ratio, q1 represents the first high-frequency respiratory rate value, q2 represents the second high-frequency respiratory rate value, and q o Let p represent the 0th high-frequency respiratory rate value, X represent the first standard respiratory rate value, p1 represent the first low-frequency respiratory rate value, p2 represent the second low-frequency respiratory rate value, and p... e Y represents the e-th low-frequency respiratory rate value, and Y represents the second respiratory rate standard value.

[0060] The respiratory rate ratio is used to quantify the degree of fluctuation of respiratory rate relative to its respective standard value in different frequency ranges (high and low frequencies). It integrates the characteristics of high-frequency and low-frequency respiratory rate datasets and is a key quantitative indicator for assessing the stability and regularity of respiratory rate. q1, q2...q o These are specific measurements obtained from the high-frequency respiratory rate dataset, reflecting the frequency changes during the higher phases of respiration. The difference is calculated with the first respiratory rate standard value X; the accumulated differences reflect the overall deviation of the high-frequency respiratory rate from the standard value. The low-frequency respiratory rate dataset represents specific measurements, reflecting the changes during the lower phases of respiration. p1, p2...p e Similar to high-frequency respiratory rate values, these are measurements taken during periods of lower respiratory rate. These values ​​are then subtracted from a second respiratory rate standard value Y. The sum of these differences reflects the deviation of the low-frequency respiratory rate from the standard value, illustrating the fluctuation characteristics of the low-frequency respiratory rate.

[0061] Multiple tidal volume values ​​are obtained from the tidal volume data set, multiple tidal volume values ​​are obtained from the tidal volume data set, a first standard tidal volume value corresponding to the high-frequency respiratory rate data set is obtained, and a second standard tidal volume value corresponding to the low-frequency respiratory rate data set is obtained. The tidal volume ratio is calculated based on the multiple tidal volume values, the multiple tidal volume values, the first standard tidal volume value, and the second standard tidal volume value. The calculation formula is as follows:

[0062] ;

[0063] Where C represents the tidal volume ratio, s1 represents the first tidal volume value, s2 represents the second tidal volume value, and s f Let h represent the f-th tidal volume value, G represent the standard value of the first tidal volume, h1 represent the first tidal volume value, h2 represent the second tidal volume value, and h... r Let R represent the r-th low tidal volume value, and F represent the second tidal volume standard value.

[0064] Tidal volume ratios measure the fluctuation of tidal volume, reflecting the degree of change in tidal volume during different respiratory cycles. They are specific values ​​obtained from the tidal volume data set, s1, s2...s... f These represent measurements taken when tidal volume is at a high level during respiration. These data reflect the specific situation where the volume of air inhaled or exhaled is large in each breath. By calculating the difference with the first tidal volume standard value G, the sum of multiple differences reflects the overall deviation of the tidal volume from the standard value, h1, h2...h... r These are the individual measurements in the low tidal volume data set, reflecting the specific situation when the tidal volume is at a low level during respiration. They are then compared with the second tidal volume standard value F, and the sum of these differences reflects the deviation of the low tidal volume from the standard value.

[0065] The respiratory assessment index is obtained based on the expiratory flow correction factor and expiratory flow fluctuation ratio, the respiratory rate correction factor and respiratory rate ratio, and the tidal volume correction factor and tidal volume ratio. The formula for calculating the respiratory assessment index is as follows:

[0066] ;

[0067] Where Z represents the respiratory assessment index, A represents the expiratory flow fluctuation ratio, α represents the expiratory flow correction factor, B represents the respiratory rate ratio, β represents the respiratory rate correction factor, C represents the tidal volume ratio, and γ represents the tidal volume correction factor.

[0068] In one embodiment, the control module is used to acquire nebulization control information of the nebulizer operation, wherein the nebulization control information includes the nebulizer's real-time pulse frequency, the nebulizer's real-time airflow velocity, and the nebulizer's single-dose drug delivery. The module acquires the nebulized particle size value based on the real-time pulse frequency, the airflow intensity based on the nebulizer's real-time airflow velocity, and the nebulized drug delivery per unit time based on the nebulizer's single-dose drug delivery. It also acquires the user's breathing-nebulization matching degree based on the nebulized particle size value, airflow intensity, nebulized drug delivery per unit time, and respiratory assessment index. The module normalizes the nebulized particle size value, airflow intensity, and nebulized drug delivery per unit time. For each nebulized particle size value, airflow intensity, and nebulized drug delivery per unit time, the module acquires a corresponding dynamic correction weight based on the normalization result, and acquires a breathing synchronization correction weight based on the dynamic correction weight. Finally, the module acquires the user's breathing-nebulization matching degree based on the breathing synchronization correction weight, the nebulized particle size value, airflow intensity, nebulized drug delivery per unit time, and the corresponding dynamic correction weight.

[0069] The real-time pulse frequency of a nebulizer is the frequency of the pulse signal generated when the nebulizer is working. It directly determines the generation rate and particle size of atomized particles. The higher the frequency, the more and finer the atomized particles are produced, which is beneficial for drug deposition deep into the lungs. The real-time airflow velocity of the nebulizer refers to the flow velocity of the airflow output by the nebulizer, measured in meters per second (m / s). It affects the transmission efficiency and uniformity of drug particles in the respiratory tract. The single-spray drug dose of the nebulizer, i.e., the amount of drug sprayed 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.

[0070] The control module, through a communication link with the nebulization execution module, collects three key parameters in real time: the nebulizer's real-time pulse frequency, real-time airflow velocity, and the single-spray drug dose. The nebulizer's real-time pulse frequency determines the generation and size of atomized particles, thus affecting the drug's deposition location and effectiveness in the lungs. The real-time airflow velocity ensures effective delivery and uniform distribution of drug particles within the respiratory tract. The single-spray drug dose is adjusted based on individual differences to ensure the patient inhales the appropriate amount of medication. The atomized particle size is closely related to the drug deposition site in the respiratory tract, helping to improve treatment targeting. Airflow intensity ensures better diffusion and deposition of drug particles. The nebulized drug dose per unit time, combined with the patient's respiratory rate and tidal volume, assesses whether the actual inhaled drug dose meets the treatment needs. The respiratory assessment index provides personalized guidance for nebulizer parameter adjustments; patients with unstable breathing require more precise parameter adjustments. The user's respiratory-nebulization fit is an important basis for judging the effectiveness of the current nebulization treatment plan.

[0071] In one embodiment, the nebulization execution module is used to obtain a drug concentration fluctuation coefficient, a drug dosage deviation coefficient, and a drug particle distribution uniformity coefficient based on the user's breathing-nebulization matching degree; to obtain an airflow-drug concentration fluctuation value based on the drug concentration fluctuation coefficient and airflow intensity; to obtain a drug dosage deviation value based on the drug dosage deviation coefficient and the amount of drug nebulized per unit time; to obtain a drug particle distribution uniformity based on the drug particle distribution uniformity coefficient and the size of the nebulized particles; to generate an airflow control value based on the airflow-drug concentration fluctuation value (by mapping the airflow-drug concentration fluctuation value to the airflow control value through fuzzy inference); to obtain a nebulized drug dosage value based on the drug dosage deviation value; to obtain a pulse frequency value based on the drug particle distribution uniformity; and to generate nebulization mode instruction information based on the airflow control value, the nebulized drug dosage value, and the pulse frequency value. Specifically, the adjustment process is as follows: by obtaining the threshold range of the airflow control value, when the airflow control value is in the low threshold range, the airflow speed can be appropriately increased, for example, by increasing it by 10%-20% based on the current airflow speed; when it is in the middle threshold range, the current airflow speed is maintained or fine-tuned, with the fine-tuning range controlled within 5%- 10%; when in the high threshold range, reduce the airflow speed, such as by 15% - 25%.

[0072] Based on the magnitude and sign of the drug dosage deviation, determine the corresponding nebulized drug dosage adjustment information. If the dosage deviation is positive and large (e.g., exceeding 15%), it indicates that the actual output drug dosage exceeds the target dosage, and the nebulized drug dosage needs to be reduced. The reduction amount can be adjusted according to the magnitude of the deviation; the larger the deviation, the larger the reduction. For example, when the deviation is 20%, reduce the current single nebulized drug dosage by 10% - 15%. If the dosage deviation is negative and large (e.g., exceeding -15%), increase the nebulized drug dosage, again adjusting the increase amount according to the deviation. For example, when the deviation is -20%, increase the current single nebulized drug dosage by 12% - 18%. When the dosage deviation is within a small range (e.g., between -5% and 5%), the current nebulized drug dosage is considered appropriate, and no adjustment is needed or only a very small adjustment is required (e.g., an adjustment range of 1% - 3%).

[0073] The direction and magnitude of pulse frequency adjustment are determined by quantifying the uniformity of drug particle distribution. When particle distribution uniformity is low, the pulse frequency needs adjustment to improve particle distribution. If the particle size is too large and the distribution is uneven, the pulse frequency should be appropriately increased to produce finer and more uniformly distributed atomized particles. The increase is determined by the difference between the quantified uniformity index and the ideal uniformity index; the larger the difference, the greater the increase. For example, if the quantified uniformity index is 20% lower than 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 the uniformity is very high, the pulse frequency can even be appropriately reduced to save energy and reduce equipment wear, but the reduction should not be too large (e.g., controlled within 5%-10%).

[0074] User breathing-nebulizer matching degree is a value derived by comprehensively considering factors such as nebulized particle size, airflow intensity, nebulized drug dosage per unit time, and respiratory assessment index. The higher the matching degree, the better the current nebulization treatment plan is suited to the patient's breathing, and the better the drug inhalation effect may be.

[0075] The drug concentration fluctuation coefficient, drug dose deviation coefficient, and drug particle distribution uniformity coefficient reflect the relevant characteristics of drugs in nebulized therapy from different perspectives. The drug concentration fluctuation coefficient quantifies the degree of fluctuation in drug concentration over time or a respiratory cycle. It is a dimensionless value, and its magnitude directly reflects the stability of the drug concentration; the larger the coefficient, the more drastic the fluctuation, and vice versa. The drug dose deviation coefficient measures the deviation between the actual nebulized drug dose and the expected set dose. Also a dimensionless value, the larger the coefficient, the greater the difference between the actual and expected doses, and the greater the potential impact on the therapeutic effect. The drug particle distribution uniformity coefficient assesses the spatial uniformity of the nebulized drug particles; the larger the coefficient, the more uniform the particle distribution.

[0076] The airflow-drug concentration fluctuation value is calculated by combining the drug concentration fluctuation coefficient and airflow intensity. It specifically quantifies the fluctuation range of drug concentration during actual nebulization and is a key reference value for subsequent adjustments to airflow parameters to stabilize drug concentration. The drug dosage deviation value is obtained by multiplying the drug dosage deviation coefficient by the amount of drug nebulized per unit time. It clarifies the specific deviation between the actual drug dosage and the expected dosage and plays an important role in accurately adjusting the nebulized drug dosage. The drug particle distribution uniformity is calculated by the drug particle distribution uniformity coefficient and the size of the nebulized particles. It visually shows the uniformity of drug particle distribution in space. The higher the uniformity, the better the drug deposition effect in the respiratory tract.

[0077] Airflow control value, nebulized drug dosage value, and pulse frequency value are key parameters directly used to control the operation of the nebulizer. The airflow control value is generated based on the airflow-drug concentration fluctuation and is used to adjust the nebulizer's airflow parameters, such as airflow velocity and pressure. A suitable airflow control value can stabilize drug concentration and improve drug delivery efficiency. The nebulized drug dosage value is determined based on the drug dosage deviation value, precisely controlling the amount of drug sprayed by the nebulizer each time, ensuring that the patient inhales the appropriate amount of drug with each breath. The pulse frequency value is determined by the uniformity of drug particle distribution and is used to adjust the nebulizer's pulse frequency. A suitable pulse frequency can make the nebulized particles more uniform in size, improving the deposition efficiency of the drug in the respiratory tract.

[0078] The nebulization execution module first receives the user's breath-nebulization matching degree from the control module, and obtains the drug concentration fluctuation coefficient, drug dosage deviation coefficient, and drug particle distribution uniformity coefficient. Then, it uses these coefficients to calculate the airflow-drug concentration fluctuation value, drug dosage deviation value, and drug particle distribution uniformity value using airflow intensity, drug dosage per unit time, and atomized particle size, respectively. The nebulization mode command information is then sent to the nebulizer, which adjusts its operating parameters accordingly.

[0079] User breathing-nebulization matching provides the basis for subsequent calculations, guiding the system to optimize treatment plans. Drug concentration fluctuation coefficient, drug dosage deviation coefficient, and drug particle distribution uniformity coefficient quantify drug-related characteristics, providing parameters for subsequent accurate calculations. Airflow-drug concentration fluctuation values, drug dosage deviation values, and drug particle distribution uniformity reflect actual conditions, helping the system identify and correct treatment deviations. Airflow control values, nebulized drug dosage values, and pulse frequency values ​​directly control nebulizer operation, ensuring stable drug delivery, accurate dosage, and uniform particle distribution. Nebulization mode command information integrates control parameters, providing operational guidance for the nebulizer.

[0080] In one embodiment, the control module is configured to acquire actual drug concentration parameters, nebulized drug dosage parameters, and drug particle distribution parameters based on the drug release data; acquire drug concentration fluctuation coefficient, nebulized drug dosage fluctuation coefficient, and drug particle distribution fluctuation coefficient based on the user's breathing-nebulization matching degree; acquire 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; acquire nebulized drug dosage fluctuation range and nebulized drug dosage adjustment direction per unit time based on the nebulized drug dosage parameters and nebulized drug dosage fluctuation coefficient; acquire nebulized drug dosage adjustment data based on the nebulized drug dosage fluctuation range and nebulized drug dosage adjustment direction per unit time; acquire pulse frequency fluctuation value and pulse frequency adjustment direction based on the drug particle distribution parameters and drug particle distribution fluctuation coefficient; generate pulse adjustment data based on the pulse frequency fluctuation value and pulse frequency adjustment direction; and acquire nebulizer control information based on the airflow adjustment data, nebulized drug dosage adjustment data, and pulse adjustment data.

[0081] Drug release data is a comprehensive collection of information on drug release during nebulization therapy, encompassing the release rate, time, and spatial distribution. Actual drug concentration parameters reflect the actual drug concentration within the nebulization environment (such as the nebulizer chamber or the patient's respiratory tract). Nebulized drug dosage parameters represent the total amount of drug delivered by the nebulizer within a specific time period, determining the patient's drug intake during treatment. Drug particle distribution parameters describe the spatial distribution characteristics of nebulized drug particles, including particle size distribution range and density distribution. A suitable distribution contributes to uniform drug deposition in the respiratory tract.

[0082] User-nebulizer fit is a value derived from factors such as nebulized particle size, airflow intensity, nebulized drug dosage per unit time, and respiratory assessment index. It measures the degree of fit between the nebulizer's operating parameters and the user's breathing state. A higher fit indicates a better match between the current nebulization treatment plan and the patient's breathing condition, potentially leading to better drug inhalation efficacy. The drug concentration fluctuation coefficient quantifies the degree of drug concentration fluctuation over time or the respiratory cycle during nebulization treatment. It is a dimensionless coefficient; a larger coefficient indicates more drastic drug concentration fluctuations and poorer stability. The nebulized drug dosage fluctuation coefficient measures the fluctuation of nebulized drug dosage during treatment. Also a dimensionless value, a larger coefficient indicates greater fluctuations in nebulized drug dosage, potentially affecting the stability of each inhalation. The drug particle distribution fluctuation coefficient describes the degree of fluctuation in drug particle distribution over time or space. A larger coefficient indicates poorer drug particle distribution stability, potentially leading to uneven drug deposition in the respiratory tract.

[0083] Airflow fluctuation range refers to the range of change of airflow parameters (such as velocity and pressure) within a certain time period. It clarifies the adjustable range of airflow and provides a numerical reference for airflow adjustment. Airflow adjustment direction indicates the direction of adjustment of airflow parameters—whether to increase or decrease. Combined with the airflow fluctuation range, it can precisely guide the adjustment of airflow parameters. Airflow adjustment data is specific data generated based on the airflow fluctuation range and adjustment direction, used to precisely adjust the airflow parameters of the nebulizer to ensure stable drug concentration. The nebulized drug dosage fluctuation range per unit time is the range of change of the nebulized drug dosage per unit time, reflecting the fluctuation range of the nebulized drug dosage and providing a basis for adjusting the nebulized drug dosage. The nebulized drug dosage adjustment direction clarifies whether the nebulized drug dosage is increased or decreased. Combined with the nebulized drug dosage fluctuation range per unit time, it enables precise control of the nebulized drug dosage. Nebulized drug dosage adjustment data is data generated based on the above two factors, used to guide the nebulizer to precisely adjust the nebulized drug dosage per unit time, ensuring that the patient inhales an appropriate dose of medication. Pulse frequency fluctuation value represents the numerical change of pulse frequency within a certain time period, reflecting its fluctuation and providing a quantitative basis for pulse frequency adjustment. The pulse frequency adjustment direction indicates whether the pulse frequency should be increased or decreased, achieving precise adjustment in conjunction with the fluctuation value. Pulse adjustment data, generated based on pulse frequency fluctuation and adjustment direction, guides the nebulizer in adjusting the pulse frequency and optimizing drug particle distribution. Nebulizer control information is a set of instructions generated by integrating airflow adjustment data, nebulized drug dosage adjustment data, and pulse adjustment data, providing comprehensive guidance for adjusting the nebulizer's operating parameters.

[0084] The control module first acquires drug release data in real time using various sensors (such as drug concentration sensors and flow sensors) installed in the nebulizer and related components. Simultaneously, the control module calculates the user's breathing-nebulization matching degree.

[0085] After acquiring the data, the control module performs in-depth analysis of the drug release data. From the time-series data of drug release, the cumulative amount of drug released per unit time is calculated to obtain the nebulized drug dosage parameter; the actual drug concentration parameter is directly obtained by using real-time monitoring data of drug concentration from sensors; and the drug particle distribution parameter is obtained by analyzing the collected drug particle samples and statistically analyzing the number and distribution of particles of different sizes.

[0086] The control module further determines the adjustment range and direction of each parameter. Taking drug concentration parameters and drug concentration fluctuation coefficients as examples, if the drug concentration parameter fluctuates frequently and the fluctuation coefficient is large over a period of time, 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 the drug concentration needs to be stabilized. At this time, it determines the airflow adjustment direction to be appropriately increased. Through the analysis of past data and experience, it determines the airflow fluctuation range, such as increasing the airflow speed by 2-5 m / s from the current level. Similarly, for nebulized drug dosage parameters and nebulized drug dosage fluctuation coefficients, if the nebulized drug dosage fluctuation coefficient shows a large fluctuation range and the actual nebulized drug dosage is lower than the expected value, the control module determines the nebulized drug dosage adjustment direction to be increased. By analyzing historical data, it determines the fluctuation range of nebulized drug dosage per unit time, such as increasing the nebulized drug dosage per unit time by 0.05-0.1 ml / min from the current level. For drug particle distribution parameters and 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 calculating the pulse frequency fluctuation value, for example, increasing the pulse frequency by 5-10 Hz based on the current basis.

[0087] Then, the control module generates corresponding adjustment data based on the determined adjustment range and direction. For airflow adjustment, specific airflow adjustment data is generated based on the determined airflow fluctuation range and airflow adjustment direction, including the specific values ​​of the adjusted airflow speed or pressure, and the adjustment time interval. For example, it might be set to gradually increase the airflow speed by 3 m / s over the next 30 seconds. For nebulized drug dosage adjustment, nebulized drug dosage adjustment data is generated based on the fluctuation range of the nebulized drug dosage per unit time and the nebulized drug dosage adjustment direction, accurate to the drug dosage change value and adjustment cycle for each adjustment. For example, the nebulized drug dosage per unit time is increased by 0.08 ml every 2 minutes. For pulse frequency adjustment, pulse adjustment data is generated based on the pulse frequency fluctuation value and pulse frequency adjustment direction, specifying the specific value and adjustment method of the pulse frequency adjustment. For example, the pulse frequency is increased by 2 Hz every 10 seconds over the next minute.

[0088] Finally, the control module integrates the generated airflow adjustment data, nebulized drug dosage adjustment data, and pulse adjustment data to form nebulizer control information. This control information is then sent to the nebulizer via a communication link. Upon receiving this information, the nebulizer automatically adjusts its own airflow parameters, nebulized drug dosage output, pulse frequency, and other operating parameters based on this information.

[0089] From the perspective of the beneficial effects achieved, this complete data processing and control process, through the precise analysis of drug release data and accurate adjustment of various parameters by the control module, enables the drug to act more accurately on the respiratory tract lesions. Simultaneously, personalized adjustment data and control information generated based on the user's breathing-nebulization matching degree fully consider the respiratory status of different patients, tailoring a nebulization treatment plan for each patient. Furthermore, precise control of drug concentration, nebulized drug dosage, and drug particle distribution effectively avoids drug overdose or underdose. By optimizing airflow parameters, nebulized drug dosage, and pulse frequency, the deposition efficiency of the drug in the respiratory tract is improved, reducing drug waste.

[0090] In one embodiment, the control module is further configured to construct a dynamic allocation model based on the nebulizer regulation information, obtain the user's real-time respiratory assessment index, drug release assessment data, and historical treatment response data based on the dynamic allocation model, and obtain correction information based on the user's real-time respiratory assessment index, drug release assessment data, and historical treatment response data. The correction information includes a nebulization pulse frequency correction value, a single-spray drug dose correction value, and an airflow velocity correction value. Based on the nebulization pulse frequency correction value, the module controls the nebulizer to match the patient's respiratory rate; based on the single-spray drug dose correction value, the module controls the amount of nebulized drug per unit time; and based on the airflow velocity correction value, the module controls the real-time gas flow rate of the nebulizer.

[0091] The dynamic allocation model constructed by the control module achieves real-time optimization of nebulization therapy parameters by integrating multi-source data: based on nebulizer control information, combined with the user's real-time respiratory assessment index (including expiratory flow rate, respiratory rate, and tidal volume fluctuation ratio), drug release assessment data (drug output, particle distribution, and concentration), and historical treatment response data (parameter adjustment records, respiratory change trends, and onset time), the correction information is calculated using reinforcement learning algorithms; among them, the frequency correction value is synchronized with the respiratory phase through PID control, the dose correction value is dynamically adjusted based on tidal volume stability, and the flow rate correction value is optimized and matched with the flow ratio and particle size. The three are connected in a closed loop through PWM modulation, stepper motor drive, and PID fan control.

[0092] like Figure 2 As shown, the present invention also provides an adaptive control method for a medical nebulizer based on real-time expiratory volume feedback, comprising:

[0093] S1. Acquire the user's dynamic breathing feature dataset in real time and send the user's dynamic breathing feature dataset to the control module;

[0094] S2. Obtain the breathing assessment index based on the user's dynamic breathing feature dataset, and obtain the nebulizer operation nebulization control information in real time. Obtain the user's breathing-nebulization matching degree based on the breathing assessment index and nebulizer control information.

[0095] S3. Determine whether the user's breathing-nebulization matching degree is within the preset threshold range. If it is, determine that the nebulizer is suitable for the user's breathing and no adjustment is needed. If it is not, determine that the nebulizer is not suitable for the user's breathing and generate nebulization mode instruction information based on the user's breathing-nebulization matching degree.

[0096] S4. Perform nebulizer adjustment control according to the nebulization mode instruction information, and obtain the drug release data when the user controls the nebulizer;

[0097] S5. Obtain drug release assessment data based on the drug release data, and obtain nebulizer control information based on the drug release assessment data and the user's breathing-nebulization matching degree, and correct the nebulization control information based on the nebulizer control information.

[0098] In one embodiment, the step of obtaining the user's dynamic breathing feature dataset includes:

[0099] S101. Obtain real-time airflow data from the atomizer;

[0100] S102. Obtain the expiratory flow data set, respiratory flow data set, and tidal volume data set based on the real-time airflow data;

[0101] S103. Obtain expiratory flow deviation data based on the expiratory flow data set;

[0102] S104. Obtain respiratory rate fluctuation data based on the respiratory flow data set;

[0103] S105. Obtain tidal volume deviation data based on the tidal volume data set;

[0104] S106. Generate a user dynamic respiratory feature dataset based on expiratory flow deviation data, respiratory rate fluctuation data, and tidal volume deviation data.

[0105] In one embodiment, the step of obtaining the respiratory assessment index based on the user's dynamic respiratory feature dataset includes:

[0106] S201. Extract expiratory flow data from the user's dynamic respiratory feature dataset according to the first preset time interval to obtain a high-frequency expiratory flow data set and a low-frequency expiratory flow data set.

[0107] S202. Extract respiratory rate data according to the second preset time interval to obtain a high-frequency respiratory rate data set and a low-frequency respiratory rate data set.

[0108] S203. Tidal volume data are extracted according to the third preset time interval to obtain high tide volume data set and low tide volume data set;

[0109] S204. Map the high-frequency expiratory flow data set and the low-frequency expiratory flow data set, the high-frequency respiratory rate data set and the low-frequency respiratory rate data set, the tidal volume data set and the tidal volume data set into respiratory state feature tensors.

[0110] S205. Obtain the respiratory assessment index based on the respiratory state feature tensor.

[0111] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. 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 external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0112] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0113] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent results or equivalent process transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An adaptive control system for a medical nebulizer based on real-time expiratory volume feedback, characterized in that, include: The system includes a control module, a nebulization execution module, and a respiratory feature analysis module, wherein the control module is communicatively connected to the nebulization execution module and the respiratory feature analysis module, respectively. The respiratory feature analysis module is used to acquire the 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 breathing assessment index based on the user's dynamic breathing feature dataset, and to obtain the nebulizer control information in real time. The module also obtains the user's breathing-nebulization matching degree based on the breathing assessment index and the nebulizer control information. The control module is used to determine whether the user's breathing-nebulization matching degree is within a preset threshold range. If it is, it is determined that the nebulizer is suitable for the user's breathing; if it is not, it is determined that the nebulizer is not suitable for the user's breathing, and then nebulization mode instruction information is generated according to the user's breathing-nebulization matching degree. The nebulization execution module is used to perform nebulizer adjustment control according to the nebulization mode instruction information, and to obtain drug release data when the user controls the nebulizer; The control module is also used to obtain drug release assessment data based on the drug release data, and to obtain nebulizer control information based on the drug release assessment data and the user's breathing-nebulization matching degree, and to correct the nebulization control information based on the nebulizer control information. The respiratory feature analysis module is also used to acquire real-time airflow data of the nebulizer, acquire expiratory flow data set, respiratory flow data set and tidal volume data set based on the real-time airflow data, acquire expiratory flow deviation data based on the expiratory flow data set, acquire respiratory rate fluctuation data based on the respiratory flow data set, acquire tidal volume deviation data based on the tidal volume data set, and generate a user dynamic respiratory feature dataset based on the expiratory flow deviation data, respiratory rate fluctuation data and tidal volume deviation data. The control module is further configured to extract expiratory flow data from the user's dynamic respiratory feature dataset according to a first preset time interval to obtain a high-frequency expiratory flow data set and a low-frequency expiratory flow data set; extract respiratory frequency data according to a second preset time interval to obtain a high-frequency respiratory frequency data set and a low-frequency respiratory frequency data set; extract 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 expiratory flow data set and the low-frequency expiratory flow data set, the high-frequency respiratory frequency data set and the low-frequency respiratory frequency data set, the high-tidal volume data set and the low-tidal volume data set into a respiratory state feature tensor; and obtain a respiratory assessment index based on the respiratory state feature tensor. The control module is further configured to acquire actual drug concentration parameters, nebulized drug dosage parameters, and drug particle distribution parameters based on the drug release data; acquire drug concentration fluctuation coefficient, nebulized drug dosage fluctuation coefficient, and drug particle distribution fluctuation coefficient based on the user's breathing-nebulization matching degree; acquire 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; acquire nebulized drug dosage fluctuation range and nebulized drug dosage adjustment direction per unit time based on the nebulized drug dosage parameters and nebulized drug dosage fluctuation coefficient; acquire nebulized drug dosage adjustment data based on the nebulized drug dosage fluctuation range and nebulized drug dosage adjustment direction per unit time; acquire pulse frequency fluctuation value and pulse frequency adjustment direction based on the drug particle distribution parameters and drug particle distribution fluctuation coefficient; generate pulse adjustment data based on the pulse frequency fluctuation value and pulse frequency adjustment direction; and acquire nebulizer control information based on the airflow adjustment data, nebulized drug dosage adjustment data, and pulse adjustment data.

2. The adaptive control system for a medical nebulizer based on real-time expiratory volume feedback according to claim 1, characterized in that, The control module is also used to acquire basic physiological data of the user, and to acquire expiratory flow correction coefficient, respiratory rate correction coefficient, and tidal volume correction coefficient based on the basic physiological data of the user, to acquire expiratory flow fluctuation ratio based on the high-frequency expiratory flow data set and the low-frequency expiratory flow data set, to acquire respiratory rate ratio based on the high-frequency respiratory rate data set and the low-frequency respiratory rate data set, to acquire tidal volume ratio based on the tidal volume data set and the low tidal volume data set, and to acquire respiratory assessment index based on the expiratory flow correction coefficient and 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.

3. The adaptive control system for a medical nebulizer based on real-time expiratory volume feedback according to claim 1, characterized in that, The control module is used to acquire nebulization control information for the nebulizer operation. The nebulization control information includes the nebulizer's real-time pulse frequency, real-time airflow velocity, and single-dose drug delivery from the nebulizer. The module obtains the atomized particle size value based on the real-time pulse frequency, the airflow intensity based on the real-time airflow velocity, the atomized drug delivery rate per unit time based on the single-dose drug delivery from the nebulizer, and the user's breathing-nebulization matching degree based on the atomized particle size value, airflow intensity, atomized drug delivery rate per unit time, and a breathing assessment index.

4. The adaptive control system for a medical nebulizer based on real-time expiratory volume feedback according to claim 3, characterized in that, The nebulization execution module is used to obtain the drug concentration fluctuation coefficient, drug dosage deviation coefficient, and drug particle distribution uniformity coefficient based on the user's breathing-nebulization matching degree; to obtain the airflow-drug concentration fluctuation value based on the drug concentration fluctuation coefficient and airflow intensity; to obtain the drug dosage deviation value based on the drug dosage deviation coefficient and the amount of drug nebulized per unit time; to obtain the drug particle distribution uniformity based on the drug particle distribution uniformity coefficient and the size of the nebulized particles; to generate an airflow control value based on the airflow-drug concentration fluctuation value; to obtain the nebulized drug dosage value based on the drug dosage deviation value; to obtain a pulse frequency value based on the drug particle distribution uniformity; and to generate nebulization mode instruction information based on the airflow control value, the nebulized drug dosage value, and the pulse frequency value.

5. The adaptive control system for a medical nebulizer based on real-time expiratory volume feedback according to claim 1, characterized in that, The control module is also used to construct a dynamic allocation model based on the nebulizer regulation information, obtain the user's real-time respiratory assessment index, drug release assessment data, and historical treatment response data based on the dynamic allocation model, and obtain correction information based on the user's real-time respiratory assessment index, drug release assessment data, and historical treatment response data. The correction information includes a nebulization pulse frequency correction value, a single-spray drug dose correction value, and an airflow velocity correction value. Based on the nebulization pulse frequency correction value, the nebulizer is controlled to match the patient's respiratory rate. Based on the single-spray drug dose correction value, the amount of nebulized drug in the nebulizer per unit time is controlled. Based on the airflow velocity correction value, the real-time gas flow rate of the nebulizer is controlled.

6. An adaptive control method for a medical nebulizer based on real-time expiratory volume feedback, characterized in that, include: The system acquires a real-time dataset of the user's dynamic breathing features and sends the dataset to the control module. The step of acquiring the user's dynamic respiratory feature dataset in real time includes: acquiring real-time airflow data from the nebulizer; acquiring an expiratory flow data set, a respiratory flow data set, and a tidal volume data set based on the real-time airflow data; acquiring expiratory flow deviation data based on the expiratory flow data set; acquiring respiratory rate fluctuation data based on the respiratory flow data set; acquiring tidal volume deviation data based on the tidal volume data set; and generating the user's dynamic respiratory feature dataset based on the expiratory flow deviation data, respiratory rate fluctuation data, and tidal volume deviation data. The breathing assessment index is obtained based on the user's dynamic breathing feature dataset, and the nebulizer operation nebulization control information is obtained in real time. The user's breathing-nebulization matching degree is obtained based on the breathing assessment index and the nebulizer control information. The step of obtaining a respiratory assessment index based on the user dynamic respiratory feature dataset includes: extracting expiratory flow data from the user dynamic respiratory feature dataset according to a first preset time interval to obtain a high-frequency expiratory flow data set and a low-frequency expiratory flow data set; extracting respiratory frequency data according to a second preset time interval to obtain a high-frequency respiratory frequency data set and a low-frequency respiratory frequency data set; extracting 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; mapping the high-frequency expiratory flow data set and the low-frequency expiratory flow data set, the high-frequency respiratory frequency data set and the low-frequency respiratory frequency data set, the high-tidal volume data set and the low-tidal volume data set into a respiratory state feature tensor; and obtaining a respiratory assessment index based on the respiratory state feature tensor. Determine whether the user's breathing-nebulization matching degree is within a preset threshold range. If it is, determine that the nebulizer is suitable for the user's breathing and no adjustment is needed. If it is not, determine that the nebulizer is not suitable for the user's breathing and generate nebulization mode instruction information based on the user's breathing-nebulization matching degree. The nebulizer adjustment control is executed according to the nebulization mode instruction information, and the drug release data of the user during nebulizer control is obtained; Drug release assessment data is obtained based on the drug release data, and nebulizer control information is obtained based on the drug release assessment data and the user's breathing-nebulization matching degree. The nebulization control information is then corrected based on the nebulizer control information. The steps of obtaining drug release assessment data based on the drug release data and obtaining nebulizer control information based on the drug release assessment data and the user's breathing-nebulization matching degree include: obtaining actual drug concentration parameters, nebulized drug dosage parameters, and drug particle distribution parameters based on the drug release data; obtaining drug concentration fluctuation coefficient, nebulized drug dosage fluctuation coefficient, and drug particle distribution fluctuation coefficient based on the user's breathing-nebulization matching degree; obtaining airflow fluctuation range and airflow adjustment direction based on the drug concentration parameters and drug concentration fluctuation coefficient; generating airflow adjustment data based on the airflow fluctuation range and airflow adjustment direction; obtaining nebulized drug dosage fluctuation range and nebulized drug dosage adjustment direction per unit time based on the nebulized drug dosage parameters and nebulized drug dosage fluctuation coefficient; obtaining nebulized drug dosage adjustment data based on the nebulized drug dosage fluctuation range and nebulized drug dosage adjustment direction per unit time; obtaining pulse frequency fluctuation value and pulse frequency adjustment direction based on the drug particle distribution parameters and drug particle distribution fluctuation coefficient; generating pulse adjustment data based on the pulse frequency fluctuation value and pulse frequency adjustment direction; and obtaining nebulizer control information based on the airflow adjustment data, nebulized drug dosage adjustment data, and pulse adjustment data.

Images