Micro liquid atomization method and device based on piezoelectric oscillation

Abstract

The invention relates to the technical field of piezoelectric oscillation, and discloses a micro liquid atomization method and device based on piezoelectric oscillation, and the method comprises the steps: carrying out the collection and analysis of atomization characteristic data of different liquid medicines in a piezoelectric net type atomizer under different temperature conditions, and obtaining multi-parameter mapping data; performing function fitting on the multi-parameter mapping data, and establishing a micro-liquid atomization parameter model; designing a piezoelectric oscillation controller based on the micro-liquid atomization parameter model, and constructing a time causal sequence equation formed by liquid drops; carrying out extended Kalman filtering processing based on a time causal sequence equation formed by the liquid drops to obtain a real-time parameter estimation value; and the piezoelectric oscillation control parameter adjustment amount is calculated according to the real-time parameter estimation value, dynamic adjustment of the piezoelectric oscillation frequency and amplitude is achieved according to the piezoelectric oscillation control parameter adjustment amount, and stable atomized liquid drops are output. Therefore, accurate prediction of the atomization process is realized, and the atomization control precision is improved.

Description

Technical Field

[0001] The present application relates to the technical field of piezoelectric oscillation, and particularly relates to a method and device for atomizing trace liquid based on piezoelectric oscillation. Background Art

[0002] As an important device for modern respiratory therapy, piezoelectric oscillation nebulizers are widely used in the field of clinical medicine, providing a means of drug delivery for the treatment of various respiratory diseases. Compared with traditional compressed nebulizers and ultrasonic nebulizers, piezoelectric mesh nebulizers have the advantages of uniform and tiny droplets, low drug residue, and easy portability, and can more effectively deliver drugs to the deep part of the respiratory tract and the lungs. However, current piezoelectric mesh nebulizers face many challenges in clinical applications. Especially under the changes of different liquid medicine characteristics and environmental conditions, it is difficult to ensure the consistency and stability of the atomization effect, which brings difficulties to precise medication.

[0003] The process of atomizing trace liquid involves complex problems of fluid mechanics and interfacial science. Especially, there is a time coupling constraint and a relationship of multi-parameter interaction between piezoelectric oscillation and droplet formation. Traditional piezoelectric atomization control methods are mainly based on static models, ignoring the dynamic process from droplet formation to detachment, resulting in significant fluctuations in atomization particle size and atomization rate under actual application environments such as changes in liquid medicine viscosity and room temperature fluctuations. In addition, the existing atomization control system lacks theoretical guidance for parameter adjustment of the piezoelectric drive part, and mostly uses empirical adjustment, making it difficult to cope with complex and variable liquid medicine characteristics, especially the atomization control problems of high-viscosity liquid medicine and protein drugs are particularly prominent. Summary of the Invention

[0004] The present application provides a method and device for atomizing trace liquid based on piezoelectric oscillation, thereby realizing precise prediction of the atomization process and improving the atomization control accuracy.

[0005] The first aspect of the present application provides a method for atomizing trace liquid based on piezoelectric oscillation, and the method for atomizing trace liquid based on piezoelectric oscillation includes:

[0006] Collect and analyze the atomization characteristic data of different liquid medicines in a piezoelectric mesh nebulizer under different temperature conditions to obtain multi-parameter mapping data;

[0007] Perform function fitting on the multi-parameter mapping data to establish a parameter model for atomizing trace liquid;

[0008] Design a piezoelectric oscillation controller based on the parameter model for atomizing trace liquid, and construct a time causal sequence equation for droplet formation;

[0009] Perform extended Kalman filtering processing based on the time causal sequence equation for droplet formation to obtain real-time parameter estimation values;

[0010] Calculate the adjustment amount of the piezoelectric oscillation control parameter according to the real-time parameter estimation value, and realize the dynamic adjustment of the piezoelectric oscillation frequency and amplitude according to the adjustment amount of the piezoelectric oscillation control parameter, and output stable atomized droplets.

[0011] The second aspect of the present application provides a micro liquid atomization device based on piezoelectric oscillation, and the micro liquid atomization device based on piezoelectric oscillation includes:

[0012] An acquisition module, configured to collect and analyze the atomization characteristic data of different liquid medicines in a piezoelectric mesh atomizer under different temperature conditions to obtain multi-parameter mapping data;

[0013] A function fitting module, configured to perform function fitting on the multi-parameter mapping data to establish a micro liquid atomization parameter model;

[0014] A construction module, configured to design a piezoelectric oscillation controller based on the micro liquid atomization parameter model and construct a time-causal sequence equation for droplet formation;

[0015] A filtering processing module, configured to perform extended Kalman filtering processing based on the time-causal sequence equation for droplet formation to obtain a real-time parameter estimation value;

[0016] An output module, configured to calculate the adjustment amount of the piezoelectric oscillation control parameter according to the real-time parameter estimation value, and realize the dynamic adjustment of the piezoelectric oscillation frequency and amplitude according to the adjustment amount of the piezoelectric oscillation control parameter, and output stable atomized droplets.

[0017] Compared with the prior art, the present application has the following beneficial effects: A multi-parameter mapping model of a piezoelectric oscillation atomizer is established, which accurately describes the relationship between the piezoelectric oscillation frequency, amplitude and atomization efficiency, droplet formation rate, realizes the accurate prediction of the atomization process, improves the atomization control accuracy, and ensures the accuracy of drug delivery. By constructing a time-causal aggregated atomized droplet dynamics model, the present invention introduces an environmental temperature compensation factor and a humidity influence factor, significantly reduces the influence of environmental condition fluctuations on the atomization effect, and significantly enhances the adaptability. Using extended Kalman filtering for real-time parameter estimation and update can dynamically track the changes in the characteristics of the liquid medicine, automatically adjust the control parameters, solve the problem of reduced control performance caused by parameter mismatch in traditional atomization control methods, and enable different viscosity liquid medicines to maintain a consistent atomization effect. By designing a super-local oscillation model and a hierarchical control structure, the complex non-linear control problem is transformed into multiple local linear problems, significantly reducing the computational complexity, enabling the control algorithm to run in real time on an embedded system, and the present invention realizes a dynamic switching mechanism for three modes of single-frequency oscillation, multi-frequency oscillation and sweep-frequency oscillation, and can select the best atomization mode according to different drug characteristics and treatment requirements. Description of the Drawings

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0019] The structures, ratios, sizes, etc. shown in the drawings of this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention. Therefore, they do not have technical substantive significance. Any modification of the structure, change of the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope that can be covered by the technical content disclosed in the present invention.

[0020] Figure 1 It is a schematic flowchart of a micro - liquid atomization method based on piezoelectric oscillation provided by an embodiment of the present invention;

[0021] Figure 2 It is a schematic block diagram of the structure of a micro - liquid atomization device based on piezoelectric oscillation provided by an embodiment of the present invention. Detailed implementation manners

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0023] The flowchart shown in the drawings is only an example illustration, and does not necessarily include all contents and operations / steps, nor does it necessarily execute in the described order. For example, some operations / steps can also be decomposed, combined, or partially merged. Therefore, the actual execution order may change according to the actual situation.

[0024] It should also be understood that the terms used in this specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0025] It should be further understood that the term "and / or" used in this specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations. Please refer toFigure 1 , one embodiment of the micro - liquid atomization method based on piezoelectric oscillation in the embodiments of the present application includes:

[0026] Step 100, collect and analyze the atomization characteristic data of different liquid medicines in a piezoelectric mesh nebulizer under different temperature conditions to obtain multi - parameter mapping data;

[0027] It can be understood that the execution subject of the present application can be a micro - liquid atomization device based on piezoelectric oscillation, or a terminal or a server. Specifically, it is not limited here. In the embodiments of the present application, the server is taken as an example of the execution subject for illustration.

[0028] Specifically, physical parameter measurements are carried out on the atomization characteristics of different liquid medicines in a piezoelectric mesh atomizer under different temperature conditions. Measure the basic physical properties of different liquid medicines, such as viscosity, surface tension coefficient, and density. For the measurement of different liquid medicines, ensure that it is carried out under a variety of temperature conditions, and these temperature conditions should cover the temperature range that appears in actual use, such as between 10°C and 40°C, and data collection is carried out every 5°C. Through the measurement of these physical parameters, a numerical set of the liquid medicine at different temperatures is obtained. Based on the numerical set, the droplet formation process of the liquid medicine on the piezoelectric oscillation mesh plate is recorded. Use a high-speed imaging system to accurately capture the droplet formation and detachment process of the liquid medicine at different oscillation frequencies. Through the recording of the high-speed camera, time-series image data of the droplets from the initial formation to the complete detachment from the oscillation mesh plate are obtained. These image data are the key to understanding the atomization characteristics of the liquid medicine and provide important information such as the shape, size, and detachment time of the droplets. In the image processing stage, image recognition technology is used to extract the key features of the droplets from each frame of the image, such as the volume, detachment time, and movement trajectory of the droplets. These feature data help the system further understand how the dynamic characteristics of the droplets are related to the atomization efficiency at different oscillation frequencies. By analyzing these image data, a quantitative relationship between droplet formation and oscillation frequency is obtained. After image processing and feature extraction, the corresponding relationship between different oscillation frequencies and the volume, detachment time, and movement trajectory of the atomized droplets helps to reveal the internal connection between the oscillation frequency and the atomization effect. On this basis, using these corresponding relationships, a functional relationship between the piezoelectric oscillation driving frequency and the atomization efficiency is established to obtain an initial atomization efficiency model. This functional model is based on the obtained experimental data and accurately describes the influence of factors such as oscillation frequency and amplitude on the atomization efficiency through mathematical expressions. Perform least-squares fitting on the initial atomization efficiency model, and adjust the coefficients in the model by comparing with the experimental data to make the prediction results of the model more consistent with the actual situation. After least-squares fitting, a calibrated atomization efficiency model is obtained, which more accurately reflects the actual atomization effect at different oscillation frequencies. According to the calibrated atomization efficiency model, a mapping relationship between the oscillation frequency and the atomization particle diameter is established. Consider parameters such as the surface tension and density of the liquid medicine, and obtain an accurate corresponding relationship between the oscillation frequency and the atomization particle diameter through experimental data. This mapping relationship helps the system understand how to optimize the size of the atomization particles by adjusting the oscillation frequency, thereby improving the atomization efficiency. In this process, ensure the accuracy of the model to ensure that the diameter of the atomization particles can be accurately controlled by adjusting the frequency, and finally achieve stable atomization of the droplets. Through the above steps, multi-parameter mapping data is finally formed, including multi-dimensional data sets such as oscillation frequency, amplitude, physical properties of the liquid medicine, droplet formation process, atomization efficiency, and atomization particle diameter.

[0029] Step 200: Perform functional fitting on the multi-parameter mapping data to establish a micro-liquid atomization parameter model;

[0030] Specifically, function fitting is performed on the multi-parameter mapping data. Based on the multi-parameter mapping data, the droplet formation process is divided into four stages: initial formation, growth, neck contraction, and detachment. These four stages represent the whole process of the droplet from formation to complete detachment from the oscillating mesh plate. The characteristics and influencing factors of each stage are different, so detailed analysis and modeling are carried out on them. The initial formation stage is the moment when the droplet starts to form from the liquid surface. In this stage, the morphological changes of the droplet are mainly affected by the surface tension, viscosity, and oscillation frequency of the liquid medicine. The growth stage is the stage when the droplet volume increases rapidly. The energy input of the liquid medicine under the oscillation action causes the droplet to continuously increase in size. In the neck contraction stage, the growth rate of the droplet volume slows down, and the droplet gradually forms a constricted neck until it finally detaches. In the detachment stage, the droplet detaches from the oscillating mesh plate and enters the air through the final contraction of the neck. For the four stages of droplet formation, differential equation systems are established respectively to quantitatively describe the deformation of the liquid medicine, the change of surface energy, and the conversion of piezoelectric oscillation energy into droplet kinetic energy in each stage. The differential equations for each stage will contain multiple variables, such as droplet volume, surface tension, droplet displacement, oscillation frequency, etc. These equations can describe in detail the variation rules of the droplet in different stages. In the initial formation stage, the relationship between the droplet volume and surface tension is particularly important because the surface tension determines the stability of the droplet and the persistence of the formation process. The equations for the growth stage focus more on describing how the liquid medicine continuously expands with the input of oscillation energy, especially under the action of oscillation frequency and amplitude, the influence of the viscosity and surface tension of the liquid medicine on the droplet volume. As for the neck contraction stage, the differential equation needs to combine the internal and external pressure differences of the droplet and the hydrodynamic characteristics of the fluid, analyze the contraction process of the droplet, and deduce the relationship between the detachment time of the droplet and the oscillation frequency and the properties of the liquid medicine. Based on these stage-based droplet formation kinetic equations, an analysis of the droplet momentum transfer efficiency is performed. The goal of this analysis is to quantify how effectively the oscillation energy is converted into the kinetic energy of the droplet. To achieve this, the relationship between the momentum change of the droplet and the oscillation characteristics of the piezoelectric element is considered. The conversion efficiency model of the droplet kinetic energy will be based on parameters such as piezoelectric oscillation frequency, amplitude, and the density and viscosity of the liquid medicine, and describe how the oscillation energy is converted into the actual kinetic energy of the droplet in each stage. The accuracy of this model is crucial for understanding the flight trajectory and stability of the droplet, especially when considering how the droplet continues to move after detaching from the mesh plate. The efficiency of momentum transfer determines the final flight speed and distribution of the droplet. Based on the efficiency model of the droplet kinetic energy and combined with the relationship of the droplet formation time, a droplet kinematic model considering multi-droplet interaction is constructed. During the atomization process, especially under high-frequency oscillation, the interaction between droplets cannot be ignored. The mutual influence between different droplets leads to droplet coalescence, collision, or airflow disturbance, and these factors will affect the atomization effect. Therefore, the droplet kinematic model not only needs to consider the dynamic behavior of a single droplet but also needs to introduce correction terms for multi-droplet interaction.These correction terms will describe the changes brought about by the interaction between droplets, such as the distance between droplets, relative velocity, collision probability, etc., so as to provide a more refined control basis for improving the atomization efficiency. After completing the construction of the droplet kinematic model, numerical calculation optimization of the model is carried out. During the optimization process, the influence of external factors such as environmental temperature and humidity on the formation and movement of droplets is considered. These environmental factors are combined with the model through additional correction factors, affecting the viscosity, surface tension of the liquid medicine and the stability of droplets. According to the numerical calculation results, the parameters of the model are adjusted to ensure that the model can accurately reflect the behavior in the actual atomization process. Through the optimization and correction of this model, a complete parameter model for micro-liquid atomization is finally established, which can accurately describe the formation, movement, interaction of droplets and the influence of environmental factors on the atomization process.

[0031] Step 300: Design a piezoelectric oscillation controller based on the micro-liquid atomization parameter model and construct a time causal sequence equation for droplet formation;

[0032] It should be noted that the calculation of PID and feedforward control parameters is performed according to the micro liquid atomization parameter model. The micro liquid atomization parameter model has provided a theoretical basis for subsequent control strategies. By combining PID (Proportional-Integral-Derivative) control and feedforward control, the control parameters of piezoelectric oscillation are precisely adjusted. PID control is used to adjust the control input according to the error between the current oscillation state and the set target, while feedforward control directly adjusts the system response based on known input conditions and model predictions. Through these control strategies, the initial parameter configuration of the piezoelectric oscillation controller is calculated to ensure that the piezoelectric element can operate within the expected frequency range and amplitude conditions. The piezoelectric grid plate is driven to oscillate according to the initial parameter configuration information, and the data of the droplet formation process is collected. The oscillation process is monitored in real time, and the dynamic characteristics of droplet formation are analyzed through the collected data. With the initial parameter configuration provided by the control system, the piezoelectric grid plate will start at different oscillation frequencies and amplitudes to observe the droplet formation process under various conditions. In this process, the collected data includes characteristic parameters such as the formation time, volume, shape, and detachment time of the droplets. Through high-precision sensors and data acquisition systems, the dynamic response characteristic data during the droplet formation process is obtained. The dynamic response characteristic data is subjected to time series analysis and feature extraction. The goal of time series analysis is to understand the whole process of droplet formation to detachment from the grid plate, including the time changes and response characteristics at each stage. Through the processing of these time series data, key features such as the droplet formation time, growth rate, and detachment time are extracted. These feature data reveal the dynamic behavior law of the droplets. During the feature extraction process, signal processing techniques such as filtering, denoising, and smoothing are used to ensure the accuracy and stability of the data, thus providing high-quality input data for subsequent analysis. Based on the time series data extracted from the time series analysis, a mathematical model is established for the relationship between the droplet formation time and the oscillation frequency. The droplet formation time is the time required for the droplet to form from the grid plate to complete detachment. This process is affected by factors such as oscillation frequency, amplitude, physical properties of the liquid medicine (such as viscosity, surface tension, etc.), and environmental temperature. By systematically analyzing these influencing factors, a mathematical relationship between the droplet formation time and the oscillation frequency is established. This relationship is achieved through regression analysis or fitting methods to obtain the functional relationship between the droplet formation time and the oscillation frequency. According to this mathematical model, the droplet formation time is predicted under different oscillation conditions. Based on the mathematical model of the relationship between the droplet formation time and the oscillation frequency, a time causal sequence equation for droplet formation is constructed. This equation is not only a simple functional relationship between the droplet formation time and the oscillation frequency, but also includes each stage and interaction in the droplet formation process, such as the acceleration of the droplet in the initial formation stage, the expansion in the growth stage, and the stability in the neck contraction stage. These stage characteristics make the droplet formation process exhibit complex dynamic behavior, and the time causal sequence equation can describe this complex dynamic relationship.By optimizing this equation, the accuracy and stability of the model are improved.

[0033] Perform image sequence segmentation on the droplet formation process on the surface of the piezoelectric oscillating mesh plate, and extract the droplet contours in each frame of the continuous image sequence. Through image processing techniques, such as edge detection and threshold segmentation methods, separate the droplet morphology from the background to obtain the binary dataset of the droplet in each frame of image, describing the process of the droplet contour changing over time. Perform inter-frame calculations of the droplet area, volume, and centroid position based on the binary dataset. The calculation of the droplet area is obtained by counting the pixel points within the droplet contour in each frame of binary image to get the actual area of the droplet at each time point. The calculation of the droplet volume depends on the two-dimensional projection of the droplet and corresponding geometric assumptions, and estimates the droplet volume through the relationship between the droplet area and its thickness. The calculation of the centroid position is to obtain the geometric center position of the droplet by weighted averaging the pixel weights within the droplet region in each frame of image. Through the inter-frame calculations of these geometric parameters, obtain the time series of geometric parameters describing the droplet growth process, which contains dynamic information such as the area, volume, and centroid position of the droplet changing over time. Perform multi-scale analysis on the time series of geometric parameters to identify the time nodes of the four stages of droplet formation, namely initial formation, growth, neck contraction, and detachment. Combine the changes in the geometric parameters of the droplet, and find the key time nodes in the droplet formation process by detecting the change trends of the droplet area, volume, and centroid position. For example, in the initial formation stage of the droplet, it is manifested as a rapid increase in the droplet volume and area, while in the growth stage, the droplet volume will continue to increase, but the growth rate will change. The neck contraction stage is that after the droplet volume growth slows down, the neck of the droplet begins to contract until the detachment stage, when the droplet finally detaches from the oscillating mesh plate. Through the variation laws of these geometric parameters, accurately identify the start and end times of each stage to obtain the segmented time feature vector, describing each stage of the droplet formation process. According to the segmented time feature vector, calculate the time duration of each stage and establish a correlation with the control parameters of the piezoelectric oscillation (such as oscillation frequency, amplitude, etc.) to construct a stage time prediction model. The stage time prediction model can predict the duration of each stage of the droplet based on the input piezoelectric oscillation parameters. Through this model, in the control process, the formation time of the droplet can be predicted in real time, and then the control strategy of the piezoelectric oscillation can be adjusted to achieve precise control of the atomization effect. Based on this stage time prediction model, model the balance relationship between the neck contraction rate and the surface tension at the moment when the droplet detaches from the oscillating mesh plate. The balance relationship between the neck contraction rate and the surface tension of the droplet is the key factor for the droplet to detach from the oscillating mesh plate. The detachment of the droplet is not only affected by the droplet volume and oscillation frequency, but also closely related to physical parameters such as the surface tension and viscosity of the droplet. At this stage, establish a mathematical model to describe the mechanical behavior during the droplet neck contraction process to obtain the fracture critical condition equation for droplet detachment. This equation provides a quantitative description of the stability of the droplet detachment process, ensuring that the detachment timing of the droplet can be accurately predicted under different oscillation parameters.Combine the fracture critical condition equation with data such as droplet volume, formation time, and initial velocity to construct time series data containing three-dimensional information of time, space, and momentum.

[0034] Step 400: Perform extended Kalman filtering on the time causal sequence equation of droplet formation to obtain real-time parameter estimation values;

[0035] Specifically, a state observer is designed based on the time causal sequence equation of droplet formation, and the dynamic process of droplet formation is transformed into a state space model. Factors such as the oscillation frequency, amplitude of the piezoelectric grid plate, viscosity of the liquid medicine, surface tension, and environmental temperature are used as state vectors to describe the state of the system. The state space equation constructs an observation system model that can reflect the dynamic behavior of the system by transforming these physical quantities and their relationships into equations. Through the design of the state observer, measured values of these key physical parameters are obtained in real time from the system, providing reliable input data for the filtering process. According to the designed observation system model, an extended Kalman filter is constructed. The extended Kalman filter is an improved form of the Kalman filter and is applicable to dealing with nonlinear systems. By combining the nonlinear characteristics of the system with the state space model, corresponding state equations and observation equations are constructed. The state equation describes how the state of the system changes over time, while the observation equation establishes the relationship between the actually measured observation data and the state variables. Through the combination of these two, prediction and estimation of the system are achieved, and the state information of the system at each moment is obtained. Since the process of droplet formation is affected by multiple factors, such as oscillation frequency, physical properties of the liquid, and environmental conditions, the changes in these factors will lead to the nonlinear behavior of the system dynamics. Therefore, an extended Kalman filter is used to process it. The state equation and the observation equation are discretized, and the continuous-time filtering process is transformed into a discrete-time form. In the discretization process, the continuous-time model is transformed into a discrete-time model through the time step, ensuring that the system has corresponding state and observation data at each discrete time point. At the same time, the discretization process considers the noise characteristics of the system, providing a basis for subsequent filtering. To ensure that the filter adapts to changes in environmental noise, a dynamic update algorithm for the adaptive covariance matrix is executed. The adaptive covariance matrix is used to represent the uncertainty in the state estimation process and is continuously adjusted as the observation data is updated to ensure that the filter can adapt to different noise levels and dynamic changes. Based on the filter structure with noise adaptability, the five-step recursive operation of the extended Kalman filter is performed. First is the state prediction step. In this stage, based on the current state and control input, the state of the system at the next moment is predicted. Then is the observation prediction step. Based on the predicted state, the observation value at the next moment is predicted. The Kalman gain is calculated. The Kalman gain is an important parameter used to balance the difference between the predicted value and the actual observation value. Through the calculation of the Kalman gain, an optimal weighted combination is obtained to reduce the prediction error. Next is the state update step. By combining the observation data with the predicted state, the state estimate value of the system is updated. Then comes the covariance update step. According to the observation data and the state update, the covariance matrix is corrected to reflect the uncertainty of the current state estimate. Through these five-step recursive operations, the optimal state estimation result at each moment is obtained, and an accurate prediction of the dynamic behavior of the system is made.Online correct the coefficients in the micro liquid atomization parameter model according to the optimal state estimation result. By adjusting the model coefficients in real time, the atomization model can better fit the actual atomization process, thereby improving the control accuracy of the system. The real-time parameter estimation value can reflect the changes in physical properties such as the viscosity and surface tension of the liquid medicine, enabling the atomization process to be adaptively adjusted according to environmental changes.

[0036] Step 500: Calculate the adjustment amount of the piezoelectric oscillation control parameter according to the real-time parameter estimation value, and dynamically adjust the piezoelectric oscillation frequency and amplitude according to the adjustment amount of the piezoelectric oscillation control parameter, and output stable atomized droplets.

[0037] Specifically, according to the real-time parameter estimation values, the entire control system is divided into three levels: the target layer, the strategy layer, and the execution layer, forming a hierarchical control architecture. In the hierarchical control architecture, the target layer is mainly responsible for setting the target parameters of the atomization effect, such as droplet size, atomization rate, etc.; the strategy layer calculates specific control strategies based on the targets set by the target layer and combines with the real-time state information of the system; the execution layer is responsible for converting the control instructions of the strategy layer into actual physical control signals, and then adjusting the working state of the piezoelectric oscillator. Through this clearly defined control structure, precise regulation of the entire atomization process is achieved, enabling the system to maintain a stable atomization effect under different working conditions. Based on the hierarchical control architecture, the oscillation frequency space is divided into multiple local intervals, and a linear model is established within each local interval to obtain a continuous oscillation control model within the full frequency range. The division of the oscillation frequency space is to better adapt to the characteristics of piezoelectric oscillation under different working conditions, because within different frequency intervals, the behavior of piezoelectric elements will be different. The linear model within each local interval simplifies the dynamic characteristics of the system, making the adjustment of oscillation control more precise. After establishing linear models in different frequency intervals, a continuous oscillation control model within the full frequency range is obtained. Through this oscillation control model, the oscillation frequency and amplitude are adjusted in real time to ensure the best atomization effect under different conditions. Based on the oscillation control model, the initial adjustment amount of the piezoelectric oscillation control parameters is calculated. The initial adjustment amount u(k) of the control parameters is represented by the formula u(k) = K(k)·e(k) + ΔuFF(k), where u(k) represents the adjustment amount of the control parameters, e(k) is the deviation vector between the target atomization parameters and the actual atomization parameters, K(k) is the adaptive gain matrix, and ΔuFF(k) is the feedforward compensation term. The deviation vector e(k) reflects the difference between the target and actual atomization effects, and the gain matrix K(k) is dynamically adjusted according to the real-time state of the system to achieve precise control of the atomization effect. The feedforward compensation term ΔuFF(k) considers the direct impact of changes in the system input on the atomization effect, can predict and compensate the system response in advance, and avoid control errors caused by the lag effect. Through these calculations, the initial adjustment amount of the piezoelectric oscillation control parameters is obtained. To optimize the control effect, a gain scheduling mechanism is applied to the calculated initial adjustment amount. The role of the gain scheduling mechanism is to dynamically adjust the gain according to the working state of the system under different working conditions to obtain the optimal control parameter adjustment amount. Through the gain scheduling mechanism, it is ensured that the system can adjust the control strategy in real time in the face of different operating environments and parameter changes to achieve the best atomization effect. Whether under low temperature, high viscosity or other extreme working conditions, the gain scheduling mechanism can enable the control system to adjust according to real-time parameters, thereby ensuring the stability and efficiency of the system. A piezoelectric oscillation drive signal is generated according to the optimal control parameter adjustment amount. This signal includes three working modes: single-frequency oscillation mode, multi-frequency oscillation mode, and sweep-frequency oscillation mode.The single - frequency oscillation mode is suitable for scenarios that require a stable atomization effect; the multi - frequency oscillation mode works at multiple frequencies to optimize the droplet distribution; the swept - frequency oscillation mode adapts to the variable characteristics of droplets and different working requirements by changing the frequency within a certain range. According to the actual working conditions and requirements, the appropriate oscillation mode is automatically selected, and the corresponding piezoelectric oscillation drive signal is generated. The piezoelectric oscillation drive signal is input into the optical monitoring feedback system for real - time evaluation. The optical monitoring feedback system obtains parameters such as the size, speed, and distribution of droplets by monitoring the characteristics of droplets in real - time and feeds this information back to the control system. Based on the monitoring results of droplet characteristics, the system calculates the control correction amount and makes further adjustments according to these correction amounts. Through closed - loop control, the dynamic adjustment of the piezoelectric oscillation frequency and amplitude is achieved to ensure the stability and consistency of the atomized droplets. Whenever a deviation in droplet characteristics is detected, the control system responds quickly to adjust the oscillation frequency and amplitude to ensure that the finally output droplets meet the target atomization parameters.

[0038] In the embodiments of this application, a multi - parameter mapping model of the piezoelectric oscillation atomizer is established, which accurately describes the relationship between the piezoelectric oscillation frequency, amplitude and atomization efficiency, droplet formation rate, realizes the accurate prediction of the atomization process, improves the atomization control accuracy, and ensures the accuracy of drug delivery. By constructing a time - causal aggregation atomized droplet dynamics model, the present invention introduces an environmental temperature compensation factor and a humidity influence factor, significantly reducing the influence of environmental condition fluctuations on the atomization effect and enhancing the adaptability significantly. Using the extended Kalman filter for real - time parameter estimation and update can dynamically track the changes in the characteristics of the liquid medicine, automatically adjust the control parameters, solve the problem of reduced control performance caused by parameter mismatch in traditional atomization control methods, and enable different viscosity liquid medicines to maintain a consistent atomization effect. By designing a super - local oscillation model and a hierarchical control structure, the complex non - linear control problem is transformed into multiple local linear problems, significantly reducing the computational complexity and enabling the control algorithm to run in real - time on an embedded system. The present invention realizes a dynamic switching mechanism for the single - frequency oscillation, multi - frequency oscillation and swept - frequency oscillation modes, and can select the best atomization mode according to different drug characteristics and treatment requirements.

[0039] In a specific embodiment, the process of executing step 100 may specifically include the following steps:

[0040] Measure the physical parameters of different liquid medicines in the piezoelectric mesh atomizer under different temperature conditions to obtain a numerical set of the liquid medicine viscosity, surface tension coefficient, and density;

[0041] Based on the numerical set, record the droplet formation process of the liquid medicine on the piezoelectric oscillation mesh plate to obtain the time - series image data of the droplets from formation to detachment from the oscillation mesh plate;

[0042] Perform image processing and feature extraction on time series image data to obtain the corresponding relationships between different oscillation frequencies and the volume of atomized droplets, detachment time, and movement trajectories;

[0043] Establish a functional relationship between the piezoelectric oscillation driving frequency and the atomization efficiency according to the corresponding relationships, obtain the initial atomization efficiency model, and perform least squares fitting on the initial atomization efficiency model to obtain the calibrated atomization efficiency model;

[0044] Based on the calibrated atomization efficiency model, establish a mapping relationship between the oscillation frequency and the atomization particle diameter to form multi-parameter mapping data.

[0045] Specifically, in the laboratory, precise measurements of the physical parameters of various liquid medicines are carried out, including viscosity, surface tension coefficient, and density. The viscosity of the liquid medicine represents the frictional force inside the fluid and determines the resistance to the flow of the liquid; the surface tension coefficient reflects the intensity of the mutual attraction between liquid molecules and affects the droplet formation process; the density determines the mass per unit volume of the liquid and affects the dynamic behavior of the droplets. At different temperatures, these physical properties of the liquid medicine will change. Therefore, the experiment needs to be carried out at multiple temperature points (such as 10°C, 20°C, 30°C, 40°C, etc.) to ensure that the working environment range is covered. A stable temperature is maintained through a temperature control system, and standard measuring instruments (such as viscometers, surface tension measuring instruments, and densitometers) are used to record the numerical sets of different liquid medicines at different temperatures. Based on these numerical sets, the droplet formation process of the liquid medicine on the piezoelectric oscillating mesh plate is analyzed. The recording of this process is achieved through a high-speed photography system, which can capture the whole process from the start of droplet formation until it completely detaches from the mesh plate. In each frame of the image, the morphological changes of the droplet can be clearly seen, and then the changes in droplet volume, the measurement of detachment time, and the movement trajectory of the droplet can be obtained. These image data form a complete time series, and the data at each moment from the just-formed droplet to the final detachment are recorded, providing detailed dynamic information about droplet growth and movement. During the droplet formation process, from the initial tiny bulge to gradually expanding into a larger droplet, it detaches from the oscillating mesh plate through the balance of neck contraction and surface tension, and this process can be clearly captured by the image. For the time-series image data, image processing and feature extraction are carried out. Image processing includes steps such as noise removal, edge detection, and morphological analysis. Through these steps, feature data such as the precise contour, size, and speed of the droplet are obtained. By calculating features such as the droplet area and circularity in each frame of the image, the relationship between droplet volume and detachment time is obtained. At the same time, different liquid medicines are tested at multiple oscillation frequencies, and the relationships between features such as droplet volume, detachment time, and movement trajectory and frequency changes are obtained. Through the accumulation of multiple experimental data, the corresponding relationship between these features of the droplet and the piezoelectric oscillation driving frequency is extracted. According to the corresponding relationship, a functional relationship between the piezoelectric oscillation driving frequency and the atomization efficiency is established to obtain the initial atomization efficiency model. This process is modeled by the following formula:

[0046] E(f,A,μ,T)=α·f β ·A γ ·e -δ·μ / T ;

[0047] In this formula, E is the atomization efficiency, representing the distribution uniformity and efficiency of droplets during the atomization process; f is the oscillation frequency, controlling the oscillation rate of the piezoelectric element; A is the oscillation amplitude, representing the amplitude size of the piezoelectric element; μ is the viscosity of the liquid medicine, affecting the speed of droplet formation and detachment; T is the ambient temperature, affecting the fluidity and surface tension of the liquid medicine; α, β, γ, δ are coefficients obtained by experimental fitting, reflecting the influence of different factors on the atomization efficiency. This formula indicates that there is a complex non-linear relationship between the atomization efficiency and the oscillation frequency, amplitude, viscosity of the liquid medicine, and temperature. The specific values of these coefficients are obtained by fitting experimental data to obtain the atomization efficiency model. To improve the accuracy of the model, the least squares method is used to fit the initial atomization efficiency model. By minimizing the sum of squared errors and adjusting the model parameters, the difference between the experimental data and the theoretical predicted values is minimized to obtain the calibrated atomization efficiency model. This process is represented by the following formula:

[0048]

[0049] where E i is the experimentally measured atomization efficiency, E(f i ,A i ,μ i ,T i ) is the atomization efficiency calculated by the model, and n is the number of experimental data points. Through this process, a more accurate atomization efficiency model is obtained and adapted to different liquid medicine and temperature conditions. Based on the calibrated atomization efficiency model, a mapping relationship between the oscillation frequency and the atomization particle diameter is established. The diameter of the droplets directly affects the stability and uniformity of the atomization effect, so it needs to be precisely controlled. The change in the oscillation frequency will affect the droplet formation method and thus affect its diameter. Based on experience and experimental data, the mapping relationship between the oscillation frequency and the atomization particle diameter is established through the following formula:

[0050]

[0051] In this formula, d is the diameter of the atomization particles, f is the oscillation frequency, σ is the surface tension of the liquid medicine, ρ is the density of the liquid medicine, and κ, θ, φ are coefficients obtained through experiments, reflecting the influence of different parameters on the droplet diameter. This formula reveals how the oscillation frequency, surface tension, and density act together on the droplet diameter. After obtaining this relationship, the droplet diameter is precisely controlled by adjusting the oscillation frequency, thereby optimizing the atomization effect. Through the above process, an accurate mapping relationship between the atomization efficiency and the droplet diameter and the oscillation frequency is established, and these relationships are continuously optimized according to experimental data to form a multi-parameter mapping data set.

[0052] In a specific embodiment, the process of performing step 200 may specifically include the following steps:

[0053] Based on multi-parameter mapping data, the droplet formation process is divided into four stages, including initial formation, growth, neck contraction, and detachment.

[0054] Differential equation systems are established for the four stages of droplet formation respectively to quantitatively describe the deformation of the liquid medicine, the change of surface energy, and the piezoelectric energy transfer in each stage, and a piecewise droplet formation kinetic equation is obtained.

[0055] Based on the piecewise droplet formation kinetic equation, the droplet momentum transfer efficiency analysis is performed to obtain an efficiency model for the conversion of oscillating energy into droplet kinetic energy.

[0056] Combining the efficiency model for the conversion of oscillating energy into droplet kinetic energy with the relationship between the droplet formation time, a droplet kinematic model considering multi-droplet interaction is constructed.

[0057] The droplet kinematic model is numerically calculated and optimized, and combined with the influence factors of environmental temperature and humidity, a micro-liquid atomization parameter model is constructed.

[0058] Specifically, based on multi-parameter mapping data, the droplet formation process is divided into four stages, including initial formation, growth, neck contraction, and detachment. The dynamic characteristics of the droplet in each stage are analyzed through experimental data. In the initial formation stage, the droplet gradually bulges from the liquid surface under the drive of piezoelectric oscillation. At this time, it is mainly affected by the surface tension (σ) and viscous force (μ), and the morphological change of the droplet can be described by the balance relationship between the surface energy and piezoelectric energy. The kinetic equation in the initial formation stage is expressed as:

[0059]

[0060] Among them, V represents the volume of the droplet, A is the oscillation amplitude, f is the oscillation frequency, μ is the viscosity of the liquid medicine, T is the temperature, and C1 is the experimental fitting coefficient. This equation shows that in the initial stage, the volume growth rate of the droplet is positively correlated with the oscillation frequency and amplitude, while the viscosity of the liquid medicine inhibits the droplet formation speed through an exponential relationship. The higher the temperature, the smaller the influence of the viscosity, which is beneficial to the formation of droplets. After entering the growth stage, the volume of the droplet continues to increase under the continuous action of the oscillating energy. However, due to the gravity (g) and the inertial effect of the liquid, the volume growth rate tends to be stable. The conversion relationship between the surface energy (Eσ) and piezoelectric energy (Ep) of the droplet becomes important. The kinetic equation in the growth stage is expressed as:

[0061]

[0062] Among them, k is the stiffness coefficient of the system, S is the surface area of the droplet, and C2 is the proportionality coefficient. This system of equations describes the conversion of piezoelectric energy into the surface energy of the droplet through oscillatory driving. The growth rate of the surface area of the droplet depends on the difference between the oscillatory energy and the surface energy of the droplet. When the oscillatory energy is higher than the required surface energy of the droplet, the surface area of the droplet increases rapidly; otherwise, it tends to be stable. As the droplet approaches its maximum volume, it enters the neck contraction stage. In this stage, the volume of the droplet no longer increases significantly, but instead, the slender region of the neck gradually tapers, eventually causing the droplet to detach from the oscillating mesh plate. The process of neck contraction is affected by the combined action of surface tension and the gravity of the droplet, and the kinetic equation is expressed as:

[0063]

[0064] Among them, r is the radius of the droplet neck, r0 is the critical radius of neck contraction, and C3 is the contraction rate coefficient. This equation reveals the relationship between the neck contraction rate of the droplet, surface tension, and viscosity. When the neck radius approaches the critical value, the contraction rate accelerates, and the droplet is about to detach. Finally, in the detachment stage, the droplet detaches from the oscillating mesh plate under the action of inertial force and surface tension and enters the air. The dynamics of the detachment stage are described by the momentum transfer efficiency model. The momentum transfer efficiency (n) represents the efficiency of converting oscillatory energy (Ep) into the kinetic energy (Ek) of the droplet, and the specific model is:

[0065]

[0066] Among them, is the kinetic energy of the droplet, m is the mass of the droplet, v is the droplet detachment velocity, and θ and φ are empirical coefficients. This model shows that the momentum transfer efficiency decreases with the increase in oscillation frequency, and the high viscosity of the liquid medicine also reduces the energy conversion efficiency, but a high-temperature environment helps to improve the efficiency. By integrating the kinetic equations of the above four stages, a complete droplet kinematic model is constructed. This model not only considers the formation process of a single droplet but also includes the interaction between multiple droplets. When multiple droplets are formed at the same frequency and amplitude, their interactions (such as air flow disturbance, coalescence effect) will affect the overall atomization effect. To describe this phenomenon of multi-droplet interaction, a droplet interference coefficient (ξ) is introduced, and the kinematic model is extended to:

[0067] V(t)=∫0 t η·C5·f α ·A β ·(1 - ξN)dt;

[0068] Among them, N is the number of droplets formed per unit time, ξ is the mutual influence coefficient between droplets, and C5, α, and β are fitting coefficients. This model reveals that during the formation of multiple droplets, an increase in the number of droplets leads to enhanced interactions, thereby inhibiting the volume growth and movement speed of individual droplets. To improve the accuracy of the model, the droplet kinematic model is combined with the influencing factors of environmental temperature and humidity. Temperature (T) and humidity (H) affect the evaporation rate of the liquid medicine, the surface tension of the droplets, and the viscous resistance of the air. The environmental influence is introduced into the model through a correction factor (Ω), and the form of the correction factor is:

[0069]

[0070] Among them, ψ and χ are constants related to the characteristics of the liquid medicine, and Ω is used to correct the movement behavior of the droplets under different environmental conditions. Combining all the kinetic equations and the environmental correction model, a micro-liquid atomization parameter model that can comprehensively describe the processes of droplet formation, growth, contraction, and detachment is obtained.

[0071] In a specific embodiment, the process of executing step 300 may specifically include the following steps:

[0072] Execute PID and feedforward control parameter calculations according to the micro-liquid atomization parameter model to obtain the initial parameter configuration information of the piezoelectric oscillation controller;

[0073] Perform oscillation drive on the piezoelectric grid board according to the initial parameter configuration information and collect the data of the droplet formation process to obtain the dynamic response characteristic data;

[0074] Perform time series analysis and feature extraction on the dynamic response characteristic data to obtain the time series data of the whole process of droplets from formation to detachment from the oscillation grid board;

[0075] Perform mathematical modeling on the relationship between the droplet formation time and the oscillation frequency according to the time series data to obtain the time causal sequence equation of droplet formation.

[0076] Specifically, extract the key control variables closely related to the atomization effect from the micro-liquid atomization parameter model. These key variables include the oscillation frequency f, the oscillation amplitude A, the viscosity μ of the liquid medicine, the surface tension σ, and the environmental temperature T. These parameters affect the droplet formation rate, atomization efficiency, and droplet particle size during the atomization process respectively. In PID (Proportional-Integral-Derivative) control, the set control objectives are usually the atomization efficiency E and the droplet diameter d, and the specific control strategy is realized through the following control model:

[0077]

[0078] where, u(k) is the control output, representing the adjustment amount of the piezoelectric oscillation control parameter; e(k) is the control error at the current moment, which is equal to the difference between the target atomization parameter and the actual atomization parameter; K p 、K i 、K d are the proportional, integral, and differential gain coefficients in the PID control respectively; Δu FF (k) is the feedforward control compensation amount, which adjusts the control parameter in advance by predicting the system response. By combining the advantages of PID and feedforward control, this formula enables the system to not only respond quickly to error changes but also cope with external disturbances in advance, obtaining the initial parameter configuration information of the piezoelectric oscillation controller. Oscillate and drive the piezoelectric grid board according to the initial parameter configuration information and collect the data of the droplet formation process. In the experiment, a high-speed camera and an optical sensor are used to synchronously monitor the droplet formation process, recording every critical moment from the start of droplet generation to the final detachment from the oscillating grid board. These data include not only the volume and morphological changes of the droplets but also the trajectory and velocity information of the droplet movement. By performing time series analysis on these dynamic response characteristic data, the characteristic data of each stage of the droplet during the whole process are obtained, such as the initial formation time, growth rate, neck contraction rate, and detachment time. These data can help the system describe the droplet formation process more precisely. During the time series analysis process, a multi-scale analysis method is adopted to decompose the droplet formation process into multiple time nodes and extract the key feature vectors from them. For example, by analyzing the curve of the droplet volume change, the formation speed and stability of the droplets at different oscillation frequencies are identified. Using these data, a mathematical relationship between the droplet formation time and the oscillation frequency is established. For this purpose, a time causal sequence equation is introduced to accurately describe the relationship between the droplet formation time (T f ) and the oscillation frequency (f), amplitude (A), and liquid medicine characteristics:

[0079]

[0080] In this equation, T f represents the total time from droplet formation to detachment; η, α, β, γ, δ are the coefficients obtained by fitting experimental data, reflecting the influence degree of each parameter on the droplet formation time; f and A are the frequency and amplitude of the piezoelectric oscillation respectively, μ is the viscosity of the liquid medicine, σ is the surface tension of the liquid medicine, and T is the ambient temperature. This formula reveals that the increase in oscillation frequency and amplitude will shorten the droplet formation time, while higher liquid medicine viscosity and lower temperature will prolong the droplet formation process.

[0081] In a specific embodiment, the process of performing time series analysis and feature extraction on the dynamic response characteristic data to obtain the time series data of the whole process of droplet formation to detachment from the oscillating grid board may specifically include the following steps:

[0082] Segment the image sequence of the droplet formation process on the surface of the piezoelectric oscillating mesh plate to obtain a binary dataset of the droplet contour changing with time;

[0083] Perform inter-frame calculations of the droplet area, volume, and centroid position based on the binary dataset to obtain a time series of geometric parameters describing the droplet growth process;

[0084] Conduct multi-scale analysis on the time series of geometric parameters to identify the time nodes of the four stages of initial formation, growth, neck contraction, and detachment of droplet formation, and obtain a segmented time feature vector;

[0085] Calculate the stage time prediction model of the time duration of each stage and the piezoelectric oscillation parameters according to the segmented time feature vector;

[0086] Based on the stage time prediction model, model the balance relationship between the neck contraction rate and the surface tension at the moment when the droplet detaches from the oscillating mesh plate to obtain the fracture critical condition equation;

[0087] Combine the fracture critical condition equation with the droplet volume, formation time, and initial velocity data to construct a time series data containing three-dimensional information of time, space, and momentum.

[0088] Specifically, use a high-speed imaging system to capture the entire process of the droplet from initial formation to final detachment from the oscillating mesh plate in real time. During this process, thousands of high-resolution images are recorded per second, and the dynamic changes of the droplet are accurately recorded through the optical system. In image preprocessing, image enhancement and denoising techniques, such as Gaussian filtering and morphological operations, are used to ensure that the droplet contour is clear and the background interference is minimized. In the image segmentation step, a threshold segmentation-based method is used to convert the image into a binary form. In this binary image, the droplet region is shown as white (pixel value is 1), while the background is black (pixel value is 0). Through this processing, a binary dataset of the droplet contour changing with time is obtained. Based on the binary dataset, calculate the area, volume, and centroid position of the droplet. In each frame of the image, the area of the droplet is obtained by counting the number of white pixels, and this area directly reflects the cross-sectional area of the droplet. The volume of the droplet needs to be estimated based on the area and the assumption of the droplet shape (such as spherical or ellipsoidal). For example, assuming the droplet is spherical, the droplet volume V is expressed as:

[0089]

[0090] where A is the cross-sectional area of the droplet and π is the pi. Through this method, the droplet volume in each frame of the image is calculated. The centroid position of the droplet is obtained by weighted averaging of the pixel coordinates within the droplet region, and the specific calculation formula is:

[0091]

[0092] Among them, (x i , y i ) are pixel coordinates, I(x i , y i ) is the value (1 or 0) of the corresponding pixel in the binarized image, and (x c , y c ) is the centroid coordinate of the droplet. Through this process, a geometric parameter time series describing the droplet growth process is obtained, including the changes in droplet area, volume, and centroid position over time. Multiscale analysis is performed on the geometric parameter time series to identify the time nodes of the four stages of initial formation, growth, neck contraction, and detachment during the droplet formation process. Multiscale analysis effectively identifies the boundaries of different stages by decomposing the change trends of the data at different time scales. For example, the curve of droplet area change usually shows linear growth in the initial formation stage, tends to saturate in the growth stage, and shows a significant decrease in the neck contraction stage until the droplet volume instantaneously becomes zero in the detachment stage. To mathematize these change processes, wavelet transform is used to decompose the time series, and the change conditions of specific frequency components are extracted from it to obtain the segmented time feature vectors of each stage. Based on the segmented time feature vectors, the relationship between the time duration of each stage and the piezoelectric oscillation parameters (frequency f, amplitude A) is calculated, and a stage time prediction model is constructed. The core of the prediction model lies in establishing the mapping relationship between the oscillation parameters and the time of each stage. For example, the relationship between the time T1 of the droplet in the initial formation stage and the oscillation frequency and amplitude is described by the following formula:

[0093] T1 = C1f -α A -β ;

[0094] Among them, C1 is the fitting coefficient, and α and β are the exponential coefficients obtained by experimental fitting. This model shows that increasing the oscillation frequency and amplitude significantly shortens the initial formation time of the droplet. Similar models are used to predict the time durations of the growth stage, neck contraction stage, and detachment stage. Through these prediction models, precise control of the entire droplet formation process is achieved. After obtaining the stage time prediction model, the neck contraction rate at the moment when the droplet detaches from the oscillating mesh plate is analyzed in relation to the surface tension balance to establish a fracture critical condition equation. When the droplet detaches, the neck contraction rate is mainly affected by the surface tension (σ) and the droplet viscosity (μ), and is modeled by the following critical condition equation:

[0095]

[0096] Among them, r is the radius of the droplet neck, r cis the critical radius at breakage, and C2 is the contraction rate coefficient. This equation reveals the relationship between the contraction speed of the droplet at the instant of detachment and the physical properties of the liquid. When the neck radius approaches the critical value, the contraction speed increases sharply, resulting in the droplet breaking off. Combining the critical condition equation for breakage with the droplet volume, formation time, and initial velocity data, a time series data model containing three-dimensional information of time, space, and momentum is constructed. This model can not only accurately describe the dynamic behavior of the droplet on the oscillating mesh plate but also predict the trajectory and velocity of the droplet flying in the air. For example, by combining the initial velocity (v0) at the moment of droplet detachment and the effect of air resistance (F d = C d v 2 ), the motion equation of the droplet is calculated:

[0097]

[0098] where x(t) is the droplet position, a is the acceleration, C d is the drag coefficient, and m is the droplet mass. Through this model, the dynamic prediction and control of the atomization process are realized, enabling the system to adjust the oscillation frequency and amplitude according to real-time feedback, thereby maintaining the stability and consistency of the droplet atomization effect.

[0099] In a specific embodiment, the process of executing step 400 may specifically include the following steps:

[0100] Design a state observer based on the time causal sequence equation of droplet formation, construct a state space equation with the oscillation frequency, amplitude, liquid medicine viscosity, surface tension, and temperature of the piezoelectric mesh plate as the state vector, and obtain the observation system model;

[0101] Construct an extended Kalman filter according to the observation system model, and establish a state equation and an observation equation;

[0102] Discretize the state equation and the observation equation, and execute the dynamic update algorithm of the adaptive covariance matrix to obtain a filter structure with noise adaptability;

[0103] Perform the five-step recursive operation of the extended Kalman filter based on the filter structure with noise adaptability, including state prediction, observation prediction, Kalman gain calculation, state update, and covariance update, to obtain the optimal state estimation result;

[0104] Online correct the coefficients in the micro-liquid atomization parameter model according to the optimal state estimation result to obtain the real-time parameter estimation value.

[0105] Specifically, a state-space model is established that can accurately reflect the relationship between the oscillation process of the piezoelectric plate and the dynamics of droplet formation. In this model, the oscillation frequency \(f\), amplitude \(A\), liquid medicine viscosity \(\mu\), surface tension \(\sigma\), and temperature \(T\) are used as elements of the state vector \(x\), and the state-space equation is constructed to describe the dynamic behavior of the system. The state-space model consists of a state equation and an observation equation. The state equation is used to predict the change of the system state, and the observation equation relates the measurement data to the state vector. In this system, the state vector is expressed as:

[0106] \(x = [f, A, \mu, \sigma, T]\) T ;

[0107] The state equation describes the variation of these parameters over time and is expressed in a non-linear form:

[0108] \(x\) k+1 \(=\ F(x\) k , u k ) + w k ;

[0109] where \(x\) k+1 is the state at the next moment, \(F\) is the state transition function, \(u\) k is the control input (such as the signal for adjusting the oscillation frequency and amplitude), and \(w\) k is the process noise, which is assumed to be Gaussian noise with zero mean and covariance \(Q\). The observation equation relates the information such as the droplet formation time, volume, and motion trajectory obtained from the measurement to the state vector and is in the form:

[0110] \(z\) k \(=\ H(x\) k ) + v k ;

[0111] where \(z\) k is the observed value, \(H\) is the observation function, and \(v\) k is the observation noise with covariance \(R\). This observation system model can not only predict the system state but also be corrected by the actual measurement data. After constructing the observation system model, an extended Kalman filter is established to achieve real-time estimation of the system state. The extended Kalman filter is applicable to non-linear systems. Therefore, during the filtering process, the state equation and the observation equation are linearized. The linear approximation is obtained by performing Taylor expansion on the state transition function \(F\) and the observation function \(H\). The linearization process involves calculating the Jacobian matrix:

[0112]

[0113] where \(F\) x and \(H\) xThey are the Jacobian matrices of the state equation and the observation equation respectively, which are used to replace the state transition matrix and the observation matrix in the linear system in nonlinear filtering. During the discretization process, the state equation and the observation equation are transformed from the continuous time domain to the discrete time domain to adapt to the computing mode of the digital signal processor. For example, the state equation is discretized as follows:

[0114] x k+1 = x k + F x (x k , u k )Δt + w k ;

[0115] where Δt is the sampling time interval, and the discrete modeling of the system state change is achieved in this way. At the same time, in order to adapt to the environmental changes and the dynamic characteristics of the measurement noise, an adaptive covariance matrix dynamic update algorithm is executed, so that the process noise covariance Q and the observation noise covariance R can be adaptively adjusted according to the real-time data. The dynamic update of the covariance matrix is achieved through the following formula:

[0116]

[0117] R k = βR k-1 + (1 - β)(z k - H(x k )0(z k - H(x k )0 T ;

[0118] where e k is the state prediction error, α and β are smoothing coefficients, and through adaptive adjustment, the filter can maintain stable estimation performance in different noise environments. After completing the discretization and covariance dynamic update, the extended Kalman filter enters the five-step recursive operation stage. These five steps include state prediction, observation prediction, Kalman gain calculation, state update, and covariance update. In the state prediction step, the system state at the next moment is predicted based on the current state and the control input:

[0119]

[0120] The observation prediction step calculates the expected observation value of the system according to the predicted state:

[0121]

[0122] The calculation of the Kalman gain is the core of the filter. By weighting the prediction error and the observation error, the optimal state estimation correction amount is obtained. The calculation formula of the Kalman gain is:

[0123]

[0124] Among them, P k|k-1 is the predicted state covariance matrix, and K k is the Kalman gain matrix, which effectively balances the error between the prediction model and the actual observation data. In the state update step, the predicted state is corrected using the Kalman gain:

[0125]

[0126] In the covariance update step, the uncertainty of the state estimate is corrected, further enhancing the adaptive ability of the filter:

[0127] P k =(I - K k H x )P k|k-1 ;

[0128] Through the above five-step recursive operation, the extended Kalman filter can provide high-precision real-time state estimation in a non-linear and non-steady droplet atomization system. Combining these optimal state estimation results, the coefficients in the micro-liquid atomization parameter model are corrected online. For example, the predicted changes in viscosity μ and surface tension σ can dynamically update the physical parameters in the model, so that the model always remains consistent with the actual system. The online correction mechanism can significantly improve the response speed and control accuracy of the system under different environmental and operating conditions, enabling the piezoelectric oscillating mesh plate to always output stable atomized droplets and ensuring the efficiency and reliability of the atomization process.

[0129] In a specific embodiment, the process of executing step 500 may specifically include the following steps:

[0130] Based on the real-time parameter estimation values, the control system is divided into three levels: the target layer, the strategy layer, and the execution layer, obtaining a hierarchical control architecture;

[0131] According to the hierarchical control architecture, the oscillation frequency space is divided into multiple local intervals, and a linear model is established within each local interval to obtain a continuous oscillation control model within the full frequency range;

[0132] Based on the oscillation control model, the initial adjustment amount of the piezoelectric oscillation control parameter u(k)=K(k)·e(k)+ΔuFF(k) is calculated, where u(k) is the initial adjustment amount of the piezoelectric oscillation control parameter, e(k) is the deviation vector between the target atomization parameter and the actual atomization parameter, K(k) is the adaptive gain matrix, and ΔuFF(k) is the feedforward compensation term;

[0133] Apply the gain scheduling mechanism to the initial adjustment amount of the piezoelectric oscillation control parameter to obtain the optimal control parameter adjustment amount under different working conditions;

[0134] Generate a piezoelectric oscillation driving signal according to the optimal control parameter adjustment amount. The piezoelectric oscillation driving signal includes a single-frequency oscillation mode, a multi-frequency oscillation mode, and a swept-frequency oscillation mode.

[0135] Input the piezoelectric oscillation driving signal into the optical monitoring feedback system for real-time evaluation, calculate the control correction amount based on the monitoring results of droplet characteristics, complete the closed-loop dynamic adjustment of the piezoelectric oscillation frequency and amplitude, and output stable atomized droplets.

[0136] Specifically, determine the overall structure of the control system according to the real-time parameter estimation values in the micro-liquid atomization parameter model. The target layer defines the final control objectives of the system, including key performance indicators such as atomization efficiency, droplet particle size, and atomization rate. The strategy layer formulates specific control strategies according to the control objectives set by the target layer, including calculating the required oscillation frequency, amplitude, and control mode (such as single-frequency, multi-frequency, or swept-frequency mode). The execution layer then converts the control strategies formulated by the strategy layer into actual physical control signals to drive the piezoelectric oscillator to achieve the expected atomization effect. This three-level hierarchical control architecture can decompose complex control tasks and enable the system to operate efficiently and stably under different working conditions. According to the hierarchical control architecture, divide the oscillation frequency space into multiple local intervals, and establish a linear model in each interval to achieve a continuous oscillation control model within the full frequency range. This method of dividing the frequency space stems from the non-linear characteristics of the piezoelectric oscillator. In different frequency ranges, its oscillation characteristics and the response curve of the droplet formation process will be significantly different. For example, divide the frequency range from 100 Hz to 500 kHz into several sub-intervals, and use the method of linear approximation to describe the relationship between the oscillation frequency and the droplet formation characteristics in each interval. In each local interval, the control model of the system is expressed by a linear equation as:

[0137] y i =a i f + b i , f ∈ [f i-1 , f i ;

[0138] where, y i represents atomization characteristics such as atomization efficiency or droplet diameter, f is the oscillation frequency, a i and b i are linear model coefficients obtained by fitting experimental data. When the frequency crosses different intervals, ensure the continuity of the model through a smooth transition function (such as the Sigmoid function or polynomial interpolation method) to avoid jumps in the control signal. Based on the above oscillation control model, calculate the initial adjustment amount of the piezoelectric oscillation control parameter. The calculation formula for the initial adjustment amount is:

[0139] u(k) = K(k)·e(k) + ΔuFF (k);

[0140] where u(k) is the control parameter adjustment amount at the current moment, e(k) is the deviation vector between the target atomization parameter and the actual atomization parameter, such as the difference between the target atomization efficiency and the actually measured efficiency. K(k) is an adaptive gain matrix that can dynamically adjust the control gain according to the change of the system state, and Δu FF (k) is the feedforward compensation term, which adjusts the control signal in advance by predicting the dynamic behavior of the system. In practical applications, the deviation vector e(k) is calculated by real-time monitoring the feedback signal of the system. For example, the error between the droplet particle size distribution data obtained by using the optical monitoring feedback system and the target distribution. The gain matrix K(k) is updated in real time through an adaptive algorithm (such as the LMS algorithm or the adaptive adjustment algorithm) to ensure the response speed and stability of the system under different working conditions. After obtaining the initial control parameter adjustment amount, the control parameters are further optimized through the gain scheduling mechanism to adapt to different working conditions. The gain scheduling mechanism is a method for dynamically adjusting the control gain, which automatically adjusts the gain parameters of the controller when the working point of the system changes, so that the system can maintain the best control effect in the full frequency range. The nonlinear relationship between the control gain K(k) and the oscillation frequency f and amplitude A is mapped through the gain scheduling function G(f,A):

[0141] K(k) = G(f,A) = C1f -α A β ;

[0142] Among them, C1, α, and β are fitting coefficients obtained by fitting experimental data. When the frequency or amplitude changes, the gain scheduling mechanism can update the control gain in real time, enabling the control system to quickly respond to external disturbances, thereby ensuring the stability of the atomization process. After obtaining the optimal control parameter adjustment amount, a piezoelectric oscillation drive signal is generated based on these parameters. The mode of the drive signal is selected as a single-frequency oscillation, multi-frequency oscillation, or sweep-frequency oscillation mode according to specific application requirements. In the single-frequency mode, the drive signal is a sine wave with a constant frequency, suitable for scenarios with stable atomization requirements. In the multi-frequency mode, the system simultaneously applies oscillation signals of multiple frequencies, which can increase the droplet distribution uniformity and is suitable for handling liquid medicines with different viscosities. In the sweep-frequency mode, the frequency of the drive signal continuously changes within a certain range, which can effectively prevent the standing wave effect during the droplet formation process and improve the atomization efficiency. These drive signals are generated by a digital signal processor and output to the piezoelectric oscillator through a high-precision power amplifier to drive the mesh plate to generate the required mechanical oscillation. The generated piezoelectric oscillation drive signal is input into the optical monitoring feedback system for real-time evaluation. The feedback system monitors the droplet formation process through a high-speed camera and image processing algorithm, including the size, shape, velocity, and distribution characteristics of the droplets. Based on these monitoring data, the deviation between the actual atomization effect and the target atomization effect is calculated to form a control correction amount:

[0143] Δu(k) = K c *y ref -y(k));

[0144] Among them, y ref is the target atomization characteristic value, y(k) is the actual measured value, and K c is the control correction gain. By feeding back the control correction amount to the controller, the piezoelectric oscillation frequency and amplitude are adjusted in real time to achieve closed-loop control. During this closed-loop control process, the system continuously monitors the output state of the atomized droplets and dynamically adjusts the control signal to keep the size and distribution of the droplets within the set target range, ensuring the stability and consistency of the atomization process. The multi-level control method based on the hierarchical control architecture, gain scheduling mechanism, and closed-loop feedback control enables the piezoelectric oscillation atomization system to operate efficiently and stably under different operating conditions.

[0145] The above describes the micro liquid atomization method based on piezoelectric oscillation in the embodiments of the present application. Next, the micro liquid atomization device 10 based on piezoelectric oscillation in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the micro liquid atomization device 10 based on piezoelectric oscillation in the embodiments of the present application includes:

[0146] The acquisition module 11 is used to collect and analyze the atomization characteristic data of different liquid medicines in the piezoelectric mesh nebulizer under different temperature conditions to obtain multi-parameter mapping data;

[0147] The function fitting module 12 is used to perform function fitting on the multi-parameter mapping data to establish a micro liquid atomization parameter model;

[0148] The construction module 13 is used to design a piezoelectric oscillation controller based on the micro liquid atomization parameter model and construct a time causal sequence equation for droplet formation;

[0149] The filtering processing module 14 is used to perform extended Kalman filtering processing based on the time causal sequence equation for droplet formation to obtain real-time parameter estimation values;

[0150] The output module 15 is used to calculate the adjustment amount of the piezoelectric oscillation control parameters according to the real-time parameter estimation values, and realize the dynamic adjustment of the piezoelectric oscillation frequency and amplitude according to the adjustment amount of the piezoelectric oscillation control parameters, and output stable atomized droplets.

[0151] Through the collaborative cooperation of the above-mentioned various components, a multi-parameter mapping model of the piezoelectric oscillation nebulizer is established, which accurately describes the relationship between the piezoelectric oscillation frequency, amplitude and atomization efficiency, droplet formation rate, realizes the accurate prediction of the atomization process, improves the atomization control accuracy, and ensures the accuracy of drug delivery. By constructing a kinetic model of atomized droplets with time causal aggregation, the present invention introduces an environmental temperature compensation factor and a humidity influence factor, significantly reduces the influence of environmental condition fluctuations on the atomization effect, and significantly enhances the adaptability. Using extended Kalman filtering for real-time parameter estimation and update can dynamically track the changes in the characteristics of the liquid medicine and automatically adjust the control parameters, solving the problem of decreased control performance caused by parameter mismatch in traditional atomization control methods, so that different viscosity liquid medicines can maintain a consistent atomization effect. By designing a super-local oscillation model and a hierarchical control structure, the complex non-linear control problem is transformed into multiple local linear problems, significantly reducing the computational complexity, enabling the control algorithm to run in real time on an embedded system. The present invention realizes a dynamic switching mechanism for three modes of single-frequency oscillation, multi-frequency oscillation and sweep-frequency oscillation, and can select the best atomization mode according to different drug characteristics and treatment requirements.

[0152] The embodiment of the present application also provides an electronic device, which includes a processor and a memory. The processor and the memory are connected through a device bus. Among them, the memory may include a non-volatile storage medium and an internal memory.

[0153] The non-volatile storage medium can store a computer program. The computer program includes program instructions, and when the program instructions are executed by the processor, the processor can execute any one of the above-mentioned micro liquid atomization methods based on piezoelectric oscillation.

[0154] The processor is used to provide computing and control capabilities to support the operation of the entire electronic device.

[0155] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can be caused to execute any of the above-mentioned micro-liquid atomization methods based on piezoelectric oscillation.

[0156] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0157] It should be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described electronic device can refer to the corresponding process of the foregoing micro-liquid atomization method based on piezoelectric oscillation, and will not be elaborated herein.

[0158] The embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by one or more processors, the one or more processors are caused to implement the micro-liquid atomization method based on piezoelectric oscillation provided by the embodiment of the present application.

[0159] Among them, the computer-readable storage medium may be an internal storage unit of the electronic device in the foregoing embodiment, such as the hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk equipped with the electronic device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.

[0160] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiment, and will not be elaborated herein.

[0161] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing an electronic device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0162] As described above, the above embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of this application.

Claims

1. A method for atomizing trace liquid based on piezoelectric oscillation, characterized in that, Including: Collect and analyze the atomization characteristic data of different liquid medicines in a piezoelectric mesh nebulizer under different temperature conditions to obtain multi-parameter mapping data; Perform function fitting on the multi-parameter mapping data to establish a micro-liquid atomization parameter model; Design a piezoelectric oscillation controller based on the micro-liquid atomization parameter model and construct a time causal sequence equation for droplet formation; Perform extended Kalman filtering on the basis of the time causal sequence equation for droplet formation to obtain real-time parameter estimation values; Calculate the adjustment amount of piezoelectric oscillation control parameters according to the real-time parameter estimation values, and realize the dynamic adjustment of piezoelectric oscillation frequency and amplitude according to the adjustment amount of piezoelectric oscillation control parameters, and output stable atomized droplets.

2. The method for atomizing trace liquid based on piezoelectric oscillation according to claim 1, characterized in that, The collecting and analyzing the atomization characteristic data of different liquid medicines in a piezoelectric mesh nebulizer under different temperature conditions to obtain multi-parameter mapping data includes: Measure the physical parameters of different liquid medicines in a piezoelectric mesh nebulizer under different temperature conditions to obtain a numerical set of liquid medicine viscosity, surface tension coefficient, and density; Based on the numerical set, record the droplet formation process of the liquid medicine on the piezoelectric oscillation mesh plate to obtain time series image data of droplets from formation to detachment from the oscillation mesh plate; Perform image processing and feature extraction on the time series image data to obtain the corresponding relationship between different oscillation frequencies and the volume, detachment time, and movement trajectory of atomized droplets; Establish a functional relationship between the piezoelectric oscillation driving frequency and the atomization efficiency according to the corresponding relationship to obtain an initial atomization efficiency model, and perform least squares fitting on the initial atomization efficiency model to obtain a calibrated atomization efficiency model; Based on the calibrated atomization efficiency model, establish a mapping relationship between the oscillation frequency and the atomization particle diameter to form multi-parameter mapping data.

3. The micro liquid atomization method based on piezoelectric oscillation according to claim 1, characterized in that, The performing function fitting on the multi-parameter mapping data to establish a micro-liquid atomization parameter model includes: Based on the multi-parameter mapping data, divide the droplet formation process into four stages, where the four stages include initial formation, growth, neck contraction, and detachment; Establish differential equation groups for the four stages of droplet formation respectively, quantitatively describe the deformation of the liquid medicine, the change of surface energy, and the piezoelectric energy transfer in each stage to obtain a segmented droplet formation kinetic equation; Perform droplet momentum transfer efficiency analysis based on the segmented droplet formation kinetic equation to obtain an efficiency model for converting oscillation energy into droplet kinetic energy; Combine the efficiency model for converting oscillation energy into droplet kinetic energy with the droplet formation time relationship to construct a droplet kinematic model considering the interaction of multiple droplets; Perform numerical calculation optimization on the droplet kinematic model and combine it with environmental temperature and humidity influence factors to construct a micro-liquid atomization parameter model.

4. The micro liquid atomization method based on piezoelectric oscillation according to claim 1, wherein The designing a piezoelectric oscillation controller based on the micro-liquid atomization parameter model and constructing a time causal sequence equation for droplet formation includes: Calculate the PID and feedforward control parameters according to the micro-liquid atomization parameter model to obtain the initial parameter configuration information of the piezoelectric oscillation controller; Perform oscillation drive on the piezoelectric mesh plate according to the initial parameter configuration information and collect the droplet formation process data to obtain dynamic response characteristic data; Perform time series analysis and feature extraction on the dynamic response characteristic data to obtain time series data of the entire process of droplet formation to detachment from the oscillating mesh plate; Based on the time series data, perform mathematical modeling on the relationship between droplet formation time and oscillation frequency to obtain a time causal sequence equation for droplet formation.

5. The method for atomizing trace liquid based on piezoelectric oscillation according to claim 4, wherein The performing time series analysis and feature extraction on the dynamic response characteristic data to obtain time series data of the entire process of droplet formation to detachment from the oscillating mesh plate includes: Perform image sequence segmentation on the droplet formation process on the surface of the piezoelectric oscillating mesh plate to obtain a binary data set of the droplet contour changing with time; Based on the binary data set, perform inter-frame calculations of droplet area, volume, and centroid position to obtain a time series of geometric parameters describing the droplet growth process; Perform multi-scale analysis on the time series of geometric parameters to identify the time nodes of the four stages of initial formation, growth, neck contraction, and detachment of droplet formation, and obtain a segmented time feature vector; Based on the segmented time feature vector, calculate a stage time prediction model of the time duration of each stage and the piezoelectric oscillation parameters; Based on the stage time prediction model, model the relationship between the neck contraction rate and surface tension balance at the moment when the droplet detaches from the oscillating mesh plate to obtain a fracture critical condition equation; Combine the fracture critical condition equation with droplet volume, formation time, and initial velocity data to construct time series data containing three-dimensional information of time, space, and momentum.

6. The method for atomizing trace liquid based on piezoelectric oscillation according to claim 1, wherein The performing extended Kalman filter processing based on the time causal sequence equation for droplet formation to obtain real-time parameter estimation values includes: Based on the time causal sequence equation for droplet formation, design a state observer, use the oscillation frequency, amplitude, liquid medicine viscosity, surface tension, and temperature of the piezoelectric mesh plate as state vectors to construct a state space equation, and obtain an observation system model; Based on the observation system model, construct an extended Kalman filter, and establish a state equation and an observation equation; Perform discretization processing on the state equation and the observation equation, and execute a dynamic update algorithm for the adaptive covariance matrix to obtain a filter structure with noise adaptability; Based on the filter structure with noise adaptability, perform five-step recursive operations of extended Kalman filter, including state prediction, observation prediction, Kalman gain calculation, state update, and covariance update, to obtain an optimal state estimation result; Based on the optimal state estimation result, online correct the coefficients in the micro-liquid atomization parameter model to obtain real-time parameter estimation values.

7. The method for atomizing trace liquid based on piezoelectric oscillation according to claim 1, wherein The calculating the adjustment amount of piezoelectric oscillation control parameters according to the real-time parameter estimation values, and realizing the dynamic adjustment of piezoelectric oscillation frequency and amplitude according to the adjustment amount of piezoelectric oscillation control parameters, and outputting stable atomized droplets includes: Based on the real-time parameter estimation values, divide the control system into three levels: the target layer, the strategy layer, and the execution layer, to obtain a hierarchical control architecture; According to the hierarchical control architecture, divide the oscillation frequency space into multiple local intervals, and establish a linear model in each local interval to obtain a continuous oscillation control model within the full frequency range; Calculate the initial adjustment amount u(k) of the piezoelectric oscillation control parameter based on the oscillation control model: u(k)=K(k)·e(k)+ΔuFF(k), where u(k) is the initial adjustment amount of the piezoelectric oscillation control parameter, e(k) is the deviation vector between the target atomization parameter and the actual atomization parameter, K(k) is the adaptive gain matrix, and ΔuFF(k) is the feedforward compensation term; Apply the gain scheduling mechanism to the initial adjustment amount of the piezoelectric oscillation control parameter to obtain the optimal control parameter adjustment amount under different working conditions; Generate a piezoelectric oscillation driving signal according to the optimal control parameter adjustment amount, and the piezoelectric oscillation driving signal includes a single-frequency oscillation mode, a multi-frequency oscillation mode, and a frequency-sweeping oscillation mode; Input the piezoelectric oscillation driving signal into the optical monitoring feedback system for real-time evaluation, calculate the control correction amount based on the monitoring result of the droplet characteristics, complete the closed-loop dynamic adjustment of the piezoelectric oscillation frequency and amplitude, and output stable atomized droplets.

8. A micro liquid atomization device based on piezoelectric oscillation, characterized in that, For implementing the micro liquid atomization method based on piezoelectric oscillation according to any one of claims 1-7, the micro liquid atomization device based on piezoelectric oscillation includes: An acquisition module, configured to acquire and analyze the atomization characteristic data of different liquid medicines in the piezoelectric mesh atomizer under different temperature conditions to obtain multi-parameter mapping data; A function fitting module, configured to perform function fitting on the multi-parameter mapping data to establish a micro liquid atomization parameter model; A construction module, configured to design a piezoelectric oscillation controller based on the micro liquid atomization parameter model and construct a time causal sequence equation for droplet formation; A filtering processing module, configured to perform extended Kalman filtering processing based on the time causal sequence equation for droplet formation to obtain a real-time parameter estimation value; An output module, configured to calculate the adjustment amount of the piezoelectric oscillation control parameter according to the real-time parameter estimation value, and realize the dynamic adjustment of the piezoelectric oscillation frequency and amplitude according to the adjustment amount of the piezoelectric oscillation control parameter, and output stable atomized droplets.

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