A multi-band airway vibration adaptive optimization system for expectoration

Abstract

The present invention belongs to the field of respiratory therapy equipment control technology. The present invention discloses a multi-band airway vibration expectoration adaptive optimization system. The acoustic impedance distribution data of each micro-segment of the patient's airway inner wall is obtained through acoustic impedance imaging technology, the abnormal tissue interface area is accurately identified, and the reflection enhancement coefficient and transmission attenuation coefficient of the vibration wave in these areas are calculated. Based on the multi-path interference model and the expectoration demand priority matrix, a multi-band vibration wave composite field with phase modulation characteristics is generated to achieve effective compensation for the energy transfer efficiency of the abnormal tissue interface area. By real-time monitoring of the spectral characteristics of the pressure pulsation signal in the airway, the characteristic resonance peak is identified and the vibration wave parameters are dynamically adjusted accordingly to form a closed-loop feedback control system. The present invention can optimize the expectoration parameters in real time according to the individual differences of the patients and the characteristics of the sputum, significantly improve the expectoration efficiency, and reduce the discomfort of the patients.

Description

Technical Field

[0001] The present invention relates to the technical field of respiratory therapy equipment control, and more particularly to a multi-band airway vibration and expectoration adaptive optimization system. Background Art

[0002] Airway sputum retention is a common clinical problem in patients with respiratory diseases, severely impacting their respiratory function and quality of life. Currently, widely used airway sputum removal techniques include postural drainage, mechanical vibration sputum removal, high-frequency chest wall oscillation, and intra-airway vibration sputum removal. Among these, airway vibration sputum removal has been widely used in clinical practice due to its non-invasive nature, ease of use, and high safety.

[0003] However, traditional airway vibration expectoration systems still face technical bottlenecks in clinical applications, especially the lack of an effective compensation mechanism for changes in vibration transmission efficiency caused by differences in the elasticity of the airway wall tissue. In patients with diseases such as chronic bronchitis and pneumonia, there are often irregular inflammatory areas, edematous tissue, and fibrotic lesions on the airway wall. These areas form obvious elastic interfaces with normal airway tissue, resulting in complex reflection, refraction, and scattering of vibration waves during propagation. When the vibration wave encounters these elastic mutation interfaces, the energy transfer efficiency is significantly reduced, resulting in insufficient effective vibration energy received by the deep sputum area, and the therapeutic threshold for loosening sputum cannot be reached. Especially for elderly patients and critically ill patients, the elasticity differences of their airway tissue are more significant. Traditional expectoration equipment with fixed frequencies or simple frequency combinations cannot adapt to this complex acoustic environment, resulting in incomplete removal of deep sputum, prolonged treatment cycles, and increased risks of respiratory infections and respiratory failure. In addition, due to the lack of accurate identification and quantification of tissue elastic interfaces, clinicians find it difficult to adjust treatment parameters in a targeted manner and can only make rough estimates based on experience. This not only reduces treatment efficiency, but may also aggravate patients' local discomfort due to uneven distribution of vibration energy, reduce treatment compliance, and ultimately affect the overall clinical efficacy.

[0004] In view of this, the present invention proposes a multi-band airway vibration sputum removal adaptive optimization system to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a multi-band airway vibration sputum removal adaptive optimization system, comprising:

[0006] Data acquisition module, used to obtain the patient's physiological parameter data and sputum distribution characteristic data during the treatment process;

[0007] a sputum removal demand analysis module, configured to construct a sputum removal demand priority matrix for each area of ​​the patient's airway based on the physiological parameter data and the sputum distribution characteristic data;

[0008] The acoustic impedance analysis module is used to obtain the acoustic impedance distribution data of each micro-segment of the patient's airway inner wall and identify abnormal areas of tissue interface based on the changes in acoustic impedance gradients between adjacent micro-segments;

[0009] A wave propagation characteristic calculation module, configured to calculate the reflection enhancement coefficient and the transmission attenuation coefficient of the vibration wave in the abnormal tissue interface region based on the acoustic impedance mutation characteristics of the abnormal tissue interface region;

[0010] A multipath interference modeling module, configured to construct a multipath interference model of shock wave propagation in the airway based on the reflection enhancement coefficient and the transmission attenuation coefficient;

[0011] A vibration wave field generation module, configured to generate a multi-band vibration wave composite field based on the multi-path interference model and the expectoration demand priority matrix;

[0012] A real-time monitoring and analysis module, configured to identify characteristic resonance peaks generated by the coupling of the multi-band vibration wave composite field and sputum by real-time monitoring of the spectrum characteristics of the pressure pulsation signal in the airway;

[0013] A parameter adaptive optimization module, configured to dynamically adjust the phase difference and frequency interval of the multi-band vibration wave composite field according to the frequency offset and peak value change of the characteristic resonance peak;

[0014] The equipment control execution module is used to control the expectoration equipment based on the adjusted parameters of the multi-band vibration wave composite field, so as to achieve accurate loosening and efficient discharge of airway sputum.

[0015] Preferably, the identifying of abnormal tissue interface areas based on the change in acoustic impedance gradient between adjacent micro-segments includes:

[0016] Normalizing the acoustic impedance value of each micro-segment in the acoustic impedance distribution data to obtain a normalized acoustic impedance value;

[0017] Calculating the difference in the normalized acoustic impedance values ​​between adjacent micro-segments as the acoustic impedance gradient between the adjacent micro-segments;

[0018] Counting the adjacent micro-segment pairs whose acoustic impedance gradient is greater than a preset gradient threshold, and recording them as candidate abnormal interfaces;

[0019] Performing local variance analysis on the acoustic impedance values ​​of the micro-segments on both sides of the candidate abnormal interface to obtain the local acoustic impedance fluctuation corresponding to the candidate abnormal interface;

[0020] The candidate abnormal interface whose local acoustic impedance fluctuation is greater than a preset fluctuation threshold is marked as the tissue interface abnormal area.

[0021] Preferably, the calculation of the reflection enhancement coefficient and the transmission attenuation coefficient of the shock wave in the abnormal tissue interface region based on the acoustic impedance mutation characteristics of the abnormal tissue interface region includes:

[0022] Acquiring acoustic impedance values ​​of micro-segments on both sides of the abnormal area of ​​the tissue interface, which are recorded as a first acoustic impedance value and a second acoustic impedance value respectively;

[0023] Calculating an acoustic impedance mutation coefficient of the abnormal tissue interface region according to a ratio of the first acoustic impedance value to the second acoustic impedance value;

[0024] Calculating a reflection enhancement coefficient of the shock wave in the abnormal area of ​​the tissue interface based on the acoustic impedance mutation coefficient and the incident angle of the shock wave;

[0025] The transmission attenuation coefficient of the shock wave in the abnormal area of ​​the tissue interface is calculated according to the product of the reflection enhancement coefficient and the acoustic impedance mutation coefficient in combination with the frequency of the shock wave.

[0026] Preferably, constructing a multipath interference model of shock wave propagation in the airway according to the reflection enhancement coefficient and the transmission attenuation coefficient includes:

[0027] constructing a geometric topological model of the inner wall of the airway according to the acoustic impedance distribution data;

[0028] Marking the position of the abnormal area of ​​the tissue interface in the geometric topological model, and generating a shock wave propagation path map including the abnormal area of ​​the tissue interface;

[0029] Based on the shock wave propagation path diagram, calculating the energy distribution ratio of the shock wave between the reflection path and the transmission path in each abnormal area of ​​the tissue interface;

[0030] According to the energy distribution ratio, the reflection enhancement coefficient and the transmission attenuation coefficient, a multipath interference model of the shock wave is constructed. The multipath interference model includes the interference intensity distribution and phase superposition distribution of the shock wave on the inner wall of the airway.

[0031] Preferably, the generating of a multi-band vibration wave composite field based on the multi-path interference model and the sputum removal demand priority matrix includes:

[0032] Determining the vibration energy demand weights of each area in the airway according to the expectoration demand priority matrix;

[0033] Based on the multipath interference model, calculating the initial phase distribution and frequency combination of the multi-band vibration waves that meet the vibration energy requirement weight;

[0034] performing phase modulation on the initial phase distribution to generate a phase-modulated multi-band shock wave signal;

[0035] The phase-modulated multi-band shock wave signal is superimposed on the geometric topological model of the airway inner wall to generate the multi-band shock wave composite field, and the vibration energy transfer efficiency of the multi-band shock wave composite field in the abnormal area of ​​the tissue interface is compensated.

[0036] Preferably, the real-time monitoring of the spectrum characteristics of the pressure pulsation signal in the airway to identify the characteristic resonance peak generated by the coupling of the multi-band vibration wave composite field and sputum includes:

[0037] Performing Fourier transform on the intra-airway pressure pulsation signal to obtain a frequency spectrum distribution of the pressure pulsation signal;

[0038] Extracting spectrum peaks with amplitudes greater than a preset amplitude threshold from the spectrum distribution and recording them as candidate resonance peaks;

[0039] Calculating a matching degree between the frequency of the candidate resonance peak and the frequency interval of the multi-band shock wave composite field, wherein the matching degree is determined by a ratio of the frequency of the candidate resonance peak to the frequency interval of the multi-band shock wave composite field;

[0040] The candidate resonance peak whose matching degree is greater than a preset matching threshold is marked as the characteristic resonance peak.

[0041] Preferably, dynamically adjusting the phase difference and frequency interval of the multi-band vibration wave composite field according to the frequency offset and peak value change of the characteristic resonance peak includes:

[0042] Calculating the difference between the frequency of the characteristic resonance peak and the center frequency of the multi-band vibration wave composite field, and recording it as the frequency offset;

[0043] Counting the change rate of the peak value of the characteristic resonance peak within a preset time period, and recording it as the peak change;

[0044] determining a phase difference adjustment amount and a frequency interval adjustment amount of the multi-band shock wave composite field according to the frequency offset amount and the peak value change;

[0045] Based on the phase difference adjustment amount and the frequency interval adjustment amount, the phase difference and the frequency interval of the multi-band shock wave composite field are updated.

[0046] Preferably, constructing a sputum removal demand priority matrix for each area of ​​the patient's airway based on the physiological parameter data and the sputum distribution characteristic data includes:

[0047] Normalizing the physiological parameter data to obtain a weighted sum of the physiological parameter data, which is recorded as a physiological load value of the airway area;

[0048] Calculate the sputum density and sputum viscosity in each airway region based on the sputum distribution characteristic data, and record them as sputum load values;

[0049] The product of the physiological load value and the sputum load value is used as the sputum discharge demand value of each airway area;

[0050] Based on the sputum discharge requirement value, the sputum discharge requirement priority matrix is ​​constructed, wherein the element value of the sputum discharge requirement priority matrix is ​​a normalized result of the sputum discharge requirement value.

[0051] Preferably, performing local variance analysis on the acoustic impedance values ​​of the micro-segments on both sides of the candidate abnormal interface to obtain the local acoustic impedance fluctuation corresponding to the candidate abnormal interface includes:

[0052] Taking the candidate abnormal interface as the center, micro-segments of preset lengths on both sides are selected as analysis windows;

[0053] Calculating the variance of the acoustic impedance values ​​of all micro-segments within the analysis window, and recording it as the initial variance;

[0054] Performing Gaussian weighting on the acoustic impedance values ​​within the analysis window to obtain a weighted acoustic impedance value sequence;

[0055] Calculating the variance of the weighted acoustic impedance value sequence, which is recorded as weighted variance;

[0056] The ratio of the initial variance to the weighted variance is used as the local acoustic impedance fluctuation.

[0057] Preferably, the calculating, based on the shock wave propagation path diagram, the energy distribution ratio of the reflection path and the transmission path of the shock wave in each abnormal area of ​​the tissue interface includes:

[0058] Determining the incident angle of the shock wave at each abnormal area of ​​the tissue interface according to the shock wave propagation path diagram;

[0059] Calculating the reflected energy ratio of the shock wave at the abnormal area of ​​the tissue interface based on the incident angle and the reflection enhancement coefficient;

[0060] Calculating the transmission energy ratio of the shock wave in the abnormal area of ​​the tissue interface according to the transmission attenuation coefficient and the reflected energy ratio;

[0061] The reflected energy ratio and the transmitted energy ratio are normalized to obtain the energy distribution ratio.

[0062] The technical effects and advantages of the multi-band airway vibration sputum removal adaptive optimization system of the present invention are as follows:

[0063] By accurately identifying abnormal interfaces such as inflamed areas, edematous tissue, and fibrotic lesions, and calculating their acoustic properties in real time, the present invention can intelligently construct personalized vibration compensation strategies to ensure that vibration energy penetrates these acoustically obstructed areas and reaches deep sputum deposits. This precise energy transfer compensation mechanism significantly improves the efficiency of loosening and expelling deep sputum, especially for patients with severe inflammation and complex airway structures, where the improvement in treatment efficacy is particularly pronounced. Patients experience greater comfort during treatment, no longer experiencing discomfort due to excessive or insufficient local vibration energy, significantly improving treatment compliance and satisfaction. For patients with chronic respiratory diseases, efficient sputum clearance means more unobstructed airways, reduced risk of infection, and better oxygenation, thereby improving quality of life. For hospitalized patients, especially those in intensive care units, the present invention accelerates the sputum clearance process, effectively shortening the duration of ventilator dependence and hospitalization. Medical staff also benefit from this, obtaining an objective and quantitative treatment effect assessment tool that allows them to more scientifically formulate and adjust treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a schematic diagram of a multi-band airway vibration and sputum removal adaptive optimization system of the present invention. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0066] This application provides a multi-band airway vibration adaptive optimization system for expectoration. The system's execution entities include, but are not limited to, expectoration equipment, medical control systems, monitoring gateways, and medical edge computing units, which can be considered general computing nodes of this application. The medical control system includes, but is not limited to, at least one of a medical PLC controller, a distributed medical monitoring system, and a programmable medical controller.

[0067] The present invention provides a multi-band airway vibration adaptive optimization system for expectoration. By acquiring real-time patient physiological data, sputum distribution characteristics, and acoustic impedance properties, it constructs a precise airway model and expectoration demand priority matrix. Combined with a multipath interference model, it generates a phase-modulated multi-band vibration wave composite field. Through dynamic monitoring and parameter adjustment, it achieves precise loosening and efficient discharge of airway sputum. Highly adaptable, it can optimize expectoration parameters in real time based on individual patient differences and sputum characteristics, significantly improving expectoration efficiency and alleviating patient discomfort.

[0068] See also Figure 1 In an embodiment of the present invention, a multi-band airway vibration sputum removal adaptive optimization system includes: a data acquisition module, a sputum removal demand analysis module, an acoustic impedance analysis module, a wave propagation characteristic calculation module, a multi-path interference modeling module, a vibration wave field generation module, a real-time monitoring and analysis module, a parameter adaptive optimization module and a device control execution module.

[0069] First, the data acquisition module collects the patient's physiological parameters and sputum distribution characteristics during treatment. Physiological parameters, including key indicators such as respiratory rate, blood oxygen saturation, lung compliance, and airway resistance, are collected in real time by medical monitoring equipment. Sputum distribution characteristics, including sputum location in the airway, density distribution, and viscosity, are obtained through medical imaging technology and clinical assessment. This data provides a foundation for formulating expectoration strategies, ensuring targeted and effective treatment.

[0070] The sputum removal demand analysis module constructs a sputum removal demand priority matrix for each patient's airway region based on physiological parameter data and sputum distribution characteristics. This matrix quantifies the urgency of sputum removal in different airway regions and reflects the priority of clinical intervention. The sputum removal demand priority matrix is ​​calculated by weighted fusion of physiological load factors and sputum load factors, providing a decision-making basis for subsequent vibration wave parameter optimization. High-priority areas in the matrix receive more precise vibration energy allocation, ensuring the optimal use of treatment resources.

[0071] The acoustic impedance analysis module uses acoustic impedance imaging technology to obtain acoustic impedance distribution data for each microsegment of the patient's airway wall. Abnormal tissue interface areas are identified based on changes in acoustic impedance gradients between adjacent microsegments. Acoustic impedance imaging is a non-invasive detection method that accurately reflects the characteristics of airway wall tissue and sputum distribution. The system normalizes the acoustic impedance distribution data, calculates the acoustic impedance gradient between adjacent microsegments, and identifies candidate abnormal interfaces using preset thresholds. This analysis, combined with local acoustic impedance fluctuation analysis, identifies abnormal tissue interface areas, laying the foundation for analysis of vibration wave energy transfer characteristics.

[0072] The wave propagation characteristics calculation module calculates the reflection enhancement coefficient and transmission attenuation coefficient of the shock wave at the abnormal tissue interface based on the acoustic impedance mutation characteristics of the abnormal tissue interface. These two coefficients directly determine the transmission efficiency and distribution characteristics of the shock wave energy within the airway. The calculation process first obtains the acoustic impedance values ​​on both sides of the abnormal tissue interface and calculates the acoustic impedance mutation coefficient. Then, combining the incident angle and frequency characteristics of the shock wave, the reflection enhancement coefficient and transmission attenuation coefficient are calculated respectively, providing key parameters for constructing the multipath interference model.

[0073] The multipath interference modeling module constructs a multipath interference model of shock wave propagation within the airway based on the reflection enhancement coefficient and transmission attenuation coefficient. This model describes the propagation path and energy distribution of shock waves within the complex airway structure, taking into account multiple reflections, transmissions, and interference effects. The model construction process includes geometric topology modeling of the airway inner wall, marking of abnormal areas at the tissue interface, shock wave propagation path analysis, and energy distribution calculation. Ultimately, a complete model is generated, including interference intensity distribution and phase superposition distribution, providing a theoretical basis for the generation of multi-band shock wave composite fields.

[0074] The vibration wave field generation module generates a multi-band vibration wave composite field with phase modulation characteristics based on the multi-path interference model and the expectoration demand priority matrix. The multi-band vibration wave composite field is a special form of vibration energy distribution. Through the precise modulation and superposition of multiple frequency components, the energy is focused and enhanced in a specific area. The generation process first determines the energy demand weight of each area according to the expectoration demand priority, and then calculates the initial phase distribution and frequency combination that meets the demand based on the multi-path interference model. The initial phase is then modulated and optimized, and finally a multi-band vibration wave composite field is generated that can effectively compensate for the energy transfer efficiency in abnormal areas of the tissue interface.

[0075] The real-time monitoring and analysis module monitors the spectral characteristics of the pressure pulsation signal within the airway in real time to identify characteristic resonance peaks generated by the coupling of the multi-band vibration wave composite field with sputum. These characteristic resonance peaks provide direct evidence of effective coupling between the vibration wave and sputum, reflecting the real-time effectiveness of the expectoration process. The monitoring process involves performing a Fourier transform on the pressure pulsation signal to obtain its spectral distribution, extracting candidate resonance peaks that meet amplitude requirements, calculating the degree of match between the candidate peaks and the frequency intervals of the vibration wave composite field, and selecting characteristic resonance peaks with high matching scores to provide feedback for subsequent parameter adjustments.

[0076] The parameter adaptive optimization module dynamically adjusts the phase difference and frequency interval of the multi-band vibration wave composite field based on the frequency offset and peak change of the characteristic resonance peak, achieving directional focusing of the vibration energy. This process forms a closed-loop feedback control, ensuring continuous optimization of the treatment effect. The adjustment process includes calculating the offset between the characteristic resonance peak and the center frequency of the composite field, calculating the rate of change of the peak value, determining the adjustment amount of the phase difference and frequency interval, and updating the multi-band vibration wave composite field parameters accordingly, so that the vibration energy can be more accurately focused on the sputum location, improving the efficiency of expectoration.

[0077] The device control module controls the expectoration device based on the adjusted parameters of the multi-band vibration wave composite field, achieving precise loosening and efficient expulsion of sputum from the airway. This is the final execution step of the entire system, translating the optimized control parameters into actual physical action. Based on the received control instructions, the expectoration device generates the required multi-band vibration waves, which act on the patient's airway. The optimized vibration waves effectively loosen and expel sputum, achieving the therapeutic goal.

[0078] In an embodiment of the present invention, the detailed implementation steps of identifying abnormal areas of tissue interfaces based on the change in acoustic impedance gradient between adjacent micro-segments include:

[0079] Normalization of the acoustic impedance values ​​for each microsegment in the acoustic impedance distribution data was performed to obtain normalized acoustic impedance values. This normalization process eliminated absolute differences in acoustic impedance values ​​between different patients and different regions, making the data comparable. The Min-Max normalization method was used to map the raw acoustic impedance values ​​to the [0, 1] interval, eliminating dimensionality effects and facilitating subsequent analysis.

[0080] The difference in normalized acoustic impedance between adjacent microsegments is calculated as the acoustic impedance gradient between them. The acoustic impedance gradient directly reflects the degree of change in tissue properties or sputum distribution and is a key indicator for identifying interface abnormalities. A larger gradient indicates a more significant difference in acoustic properties between two adjacent regions, suggesting the presence of a tissue interface or sputum boundary.

[0081] Adjacent microsegment pairs with acoustic impedance gradients greater than a preset gradient threshold are identified as candidate abnormal interfaces. This threshold is determined based on clinical data statistics and expert experience and is typically set at three standard deviations of the acoustic impedance variation of adjacent tissues under normal physiological conditions to ensure screening sensitivity and specificity. The threshold can be dynamically adjusted based on the characteristics of different patient populations, with different reference values ​​for children, the elderly, or patients with special pathological conditions.

[0082] Local variance analysis is performed on the acoustic impedance values ​​of microsegments on both sides of the candidate abnormal interface to determine the local acoustic impedance fluctuation corresponding to the candidate abnormal interface. Local variance analysis is an important step in further verifying the candidate interface. By evaluating the acoustic impedance stability of the area surrounding the interface, it can distinguish true tissue interface abnormalities from random fluctuations. The analysis process involves selecting an analysis window centered on the candidate interface, calculating the variance characteristics of the acoustic impedance values ​​within the window, and deriving a quantitative indicator reflecting the degree of local fluctuation.

[0083] Candidate abnormal interfaces with local acoustic impedance fluctuations greater than a preset fluctuation threshold are marked as tissue interface abnormalities. The preset fluctuation threshold is a reference value determined based on the statistical characteristics of the acoustic impedance fluctuations of healthy airway tissue interfaces and is typically set to twice the median of the local fluctuations of normal tissue interfaces. This step, through dual screening (gradient screening and fluctuation screening), highly accurately identifies the actual tissue interface abnormalities within the airway, providing precise target locations for subsequent shock wave parameter optimization.

[0084] In an embodiment of the present invention, the detailed implementation steps of calculating the reflection enhancement coefficient and the transmission attenuation coefficient of the shock wave in the abnormal tissue interface region based on the acoustic impedance mutation characteristics of the abnormal tissue interface region include:

[0085] Acoustic impedance values ​​are obtained for microsegments on either side of the abnormal tissue interface, recorded as the first and second acoustic impedance values, respectively. These two acoustic impedance values ​​characterize the acoustic properties of the tissue or sputum on either side of the interface and serve as the basis for calculating acoustic wave propagation characteristics. The acquisition process uses a boundary localization algorithm to precisely determine the interface position and extract the impedance value at the corresponding location from the acoustic impedance distribution data to ensure the representativeness and accuracy of the data.

[0086] Based on the ratio of the first acoustic impedance value to the second acoustic impedance value, the acoustic impedance mutation coefficient XD of the abnormal area of ​​the tissue interface is calculated. The acoustic impedance mutation coefficient quantifies the difference in acoustic properties on both sides of the interface and is a key parameter for predicting the propagation characteristics of shock waves. The calculation formula is:

[0087] ; Wherein, D2 is the second acoustic impedance value, and D1 is the first acoustic impedance value;

[0088] The value range of this dimensionless coefficient is [0,1]. The closer the value is to 1, the higher the degree of interface mutation and the stronger the reflection effect of the shock wave.

[0089] Based on the acoustic impedance mutation coefficient and the incident angle of the shock wave, the reflection enhancement coefficient FD of the shock wave at the abnormal area of ​​the tissue interface is calculated. The reflection enhancement coefficient determines the proportion of vibration energy reflected back by the interface and directly affects the distribution of energy within the airway. The calculation uses a modified acoustic wave reflection law that takes into account the non-ideal characteristics of the tissue interface. The formula is:

[0090] ; Where RD is the angle of incidence;

[0091] This calculation model takes into account the influence of the incident angle on the reflection effect. The larger the incident angle, the more obvious the reflection enhancement effect is, which is consistent with the basic principles of wave mechanics.

[0092] The transmission attenuation coefficient (TD) of the vibration wave in the abnormal area of ​​the tissue interface is calculated based on the product of the reflection enhancement coefficient and the acoustic impedance mutation coefficient, combined with the frequency of the vibration wave. The transmission attenuation coefficient determines the degree of attenuation of the vibration energy after passing through the interface and is an important indicator for evaluating the efficiency of energy transfer in deep tissue. The calculation formula is:

[0093] The reference frequency CD is a standardized parameter based on the acoustic properties of airway tissue, typically set at 100 Hz; ZD is the frequency of the vibration wave. This model accounts for the effect of frequency on transmission characteristics. Higher frequencies lead to greater energy attenuation at the abrupt interface, consistent with clinical observations.

[0094] In an embodiment of the present invention, the detailed implementation steps of constructing a multipath interference model of shock wave propagation in the airway based on the reflection enhancement coefficient and the transmission attenuation coefficient include:

[0095] Based on acoustic impedance distribution data, a geometric topological model of the airway inner wall is constructed. This geometric topological model mathematically describes the three-dimensional structure of the patient's airway, including its shape, size, and branching characteristics. The construction process combines acoustic impedance imaging data with clinical anatomical knowledge, using B-spline surfacing or NURBS technology to create a smooth and continuous airway surface model, accurately reproducing the airway's geometric features. The model achieves millimeter-level resolution, accurately representing changes in airway microstructure and providing a spatial foundation for wave propagation analysis.

[0096] The location of abnormal tissue interface areas is marked in the geometric topological model, and a shock wave propagation path map that includes the abnormal tissue interface areas is generated. The propagation path map is a visual expression that shows the possible propagation paths and key reflection points of shock waves in the airway. The marking process uses feature mapping technology to accurately locate the identified abnormal tissue interface areas on the geometric model and mark them with different colors or labels to facilitate subsequent analysis. The propagation path map takes into account the structural characteristics of the airway, such as curvature and bifurcation, and includes a main path and multiple secondary paths, fully covering the possible propagation of shock waves.

[0097] Based on the shock wave propagation path diagram, the energy distribution ratio of the reflection path and the transmission path of the shock wave in each abnormal area of ​​the tissue interface is calculated. The energy distribution ratio determines the energy flow of the shock wave after encountering the interface and is the core parameter for constructing the interference model. The calculation process first determines the incident angle of the shock wave at each interface. Then, based on the reflection enhancement coefficient and the transmission attenuation coefficient, the reflection energy ratio and the transmission energy ratio are calculated respectively, and normalized to obtain the final distribution ratio. This step takes into account multiple reflections and transmissions of the wave, and a stable solution is obtained through iterative calculation to ensure energy conservation.

[0098] Based on the energy distribution ratio, reflection enhancement coefficient, and transmission attenuation coefficient, a multipath interference model of the shock wave is constructed. The multipath interference model includes the interference intensity distribution and phase superposition distribution of the shock wave on the inner wall of the airway. The multipath interference model is a complex wave field description that takes into account the interference effect generated when shock waves on various propagation paths meet. The model is constructed using a wavefront tracking algorithm and the principle of phase superposition to calculate the composite effect of shock waves from different paths at any spatial point. The interference intensity distribution describes the energy level at each point in space, and the phase superposition distribution describes the phase characteristics of the wave. The two together determine the spatial distribution law of the vibration effect, providing a theoretical basis for the subsequent design of multi-band shock waves.

[0099] In an embodiment of the present invention, the detailed implementation steps for generating a multi-band vibration wave composite field with phase modulation characteristics based on a multipath interference model and a sputum removal demand priority matrix include:

[0100] Based on the expectoration priority matrix, the vibration energy requirement weights for each airway region are determined. These weights reflect the degree of vibration energy demand in different regions and directly influence the energy allocation strategy. The determination process combines the expectoration priority matrix with clinical experience, assigning higher energy requirement weights to high-priority regions to ensure that treatment resources are allocated to the areas most in need. Weight values ​​typically range from [0, 1], summing to 1, forming a normalized energy allocation strategy.

[0101] Based on a multipath interference model, the initial phase distribution and frequency combination of multi-band vibration waves that meet the required vibration energy weights are calculated. These initial phase distribution and frequency combination are fundamental parameters of the multi-band vibration wave and determine the fundamental characteristics of the wave field. The calculation process employs an inverse problem-solving approach, with the goal of meeting the required energy weights as the goal, and an iterative optimization algorithm is used to search for the optimal parameter combination. The initial phase distribution typically contains 3-7 different phase angles, and the frequency combination contains 2-5 dominant frequency components. These parameter combinations constitute the fundamental vibration wave morphology.

[0102] Phase modulation is performed on the initial phase distribution to generate a phase-modulated multi-band shock wave signal. Phase modulation is a key technology for enhancing the directionality and penetration of shock waves. It achieves spatial reconstruction of wave energy by precisely controlling the phase relationship. The modulation process utilizes a dynamic phase modulation algorithm. Based on the structural characteristics of the airway and the distribution of tissue interfaces, the initial phase is nonlinearly transformed to generate a phase spectrum with specific spatial focusing characteristics. The phase modulation parameters are dynamically adjusted according to the acoustic characteristics of the airway region to ensure the accuracy and adaptability of the modulation effect.

[0103] The phase-modulated multi-band vibration wave signal is superimposed on the geometric topological model of the inner wall of the airway to generate a multi-band vibration wave composite field. The vibration energy transfer efficiency of the multi-band vibration wave composite field in the abnormal area of ​​the tissue interface is compensated. The multi-band vibration wave composite field is a special form of energy distribution. By controlling the phase relationship of multi-frequency components, the energy enhancement and attenuation in a specific area are achieved. The superposition process uses wave field synthesis technology to calculate the propagation characteristics of each frequency component in the airway model, and considers their interaction effects to finally form a complete vibration wave composite field. The composite field specifically compensates for the energy transfer efficiency of abnormal areas of the tissue interface. Through specific frequency combinations and phase relationships, it overcomes the energy barrier caused by the interface, ensures that the deep area obtains sufficient vibration energy, and improves the efficiency of expectoration.

[0104] In an embodiment of the present invention, the detailed implementation steps of real-time monitoring of the spectrum characteristics of the pressure pulsation signal in the airway and identifying the characteristic resonance peak generated by the coupling of the multi-band vibration wave composite field and sputum include:

[0105] A Fourier transform is performed on the intra-airway pressure pulsation signal to obtain its spectral distribution. The spectral distribution represents the pressure signal in the frequency domain and directly reflects the energy distribution of each frequency component. The transformation process utilizes the Fast Fourier Transform (FFT) algorithm to convert the time-domain signal into a frequency-domain representation. Window functions such as the Hanning window are also employed to reduce spectral leakage and improve the accuracy of spectral analysis. The analysis utilizes a 512-2048-point FFT with a frequency resolution of 0.1-0.5 Hz, enabling precise capture of subtle resonant characteristics.

[0106] Spectral peaks with amplitudes greater than a preset amplitude threshold are extracted from the spectrum distribution and recorded as candidate resonance peaks. Candidate resonance peaks are frequency points in the spectrum with significant energy and may represent meaningful resonance phenomena. The extraction process uses a peak detection algorithm to identify local maxima in the spectrum and filter out significant peaks using an amplitude threshold. The preset amplitude threshold is typically set to the spectrum mean plus two standard deviations to ensure that the extracted peaks are statistically significant. Peak extraction takes into account background noise and baseline drift in the spectrum and uses adaptive thresholding technology to improve detection robustness.

[0107] Calculate the matching degree PU between the frequency of the candidate resonance peak and the frequency interval of the multi-band vibration wave composite field. The matching degree is determined by the ratio of the frequency of the candidate resonance peak to the frequency interval of the multi-band vibration wave composite field. The matching degree is a key indicator for evaluating whether the resonance peak is generated by the coupling of vibration waves and sputum. A high matching degree indicates that the peak is likely to be a valid resonance response. The calculation formula is:

[0108] ; Where HU is the frequency of the candidate resonance peak, ZU is the nearest composite field frequency, and FU is the composite field frequency interval;

[0109] This formula normalizes the deviation between the candidate peak and the composite field frequency as a ratio of relative frequency separation, with a value ranging from [0, 1], where smaller values ​​indicate a higher degree of match. This design takes into account the frequency offset characteristics of resonance phenomena, allowing for a certain deviation between the resonance peak and the excitation frequency, which is more consistent with actual physical conditions.

[0110] Candidate resonance peaks with a matching degree greater than the preset matching threshold are marked as characteristic resonance peaks. The characteristic resonance peak is a key indicator reflecting the effective coupling of vibration waves and sputum, and is directly used to guide subsequent parameter adjustments. The preset matching threshold is usually set to 0.75-0.85 to ensure that the selected resonance peaks have sufficiently high credibility. The marking process records the characteristic parameters of the characteristic resonance peaks, such as frequency, amplitude, and half-height width, providing complete information for subsequent analysis. These characteristic resonance peaks represent the frequency points where vibration energy is effectively absorbed by sputum, and are a direct basis for evaluating treatment effects and optimizing control parameters.

[0111] In an embodiment of the present invention, the detailed implementation steps of dynamically adjusting the phase difference and frequency interval of the multi-band vibration wave composite field according to the frequency offset and peak value change of the characteristic resonance peak include:

[0112] The difference between the frequency of the characteristic resonance peak and the center frequency of the multi-band vibration wave composite field is calculated and recorded as the frequency offset. The frequency offset reflects the deviation between the actual resonance frequency of the resonant system and the expected value and is an important basis for adjusting the composite field parameters. The calculation formula is:

[0113] Frequency offset = characteristic resonance peak frequency - center frequency of multi-band vibration wave composite field;

[0114] The offset can be positive or negative; a positive value indicates that the actual resonant frequency is higher than expected, while a negative value indicates that it is lower than expected. The center frequency is usually the highest-energy frequency component in the composite field, or a weighted average of multiple primary frequency components, representing the dominant frequency of the composite field.

[0115] The rate of change of the peak value of the characteristic resonance peak within a preset time period is statistically calculated, recorded as the peak change (FB). The peak change reflects the dynamic characteristics of the resonance intensity and is a direct indicator for evaluating the effectiveness of expectoration. The statistical process uses a sliding time window technique to track the continuous changes in the amplitude of the characteristic resonance peak and calculate the relative rate of change per unit time. This indicator is expressed in % / s and represents the rate of change of the resonance intensity. A positive value indicates an increase in resonance, while a negative value indicates a decrease in resonance. The preset time period is typically 5-15 seconds and can be dynamically adjusted according to the patient's respiratory cycle to ensure that the complete trend of change is captured.

[0116] Based on the frequency offset and peak value changes, the phase difference adjustment and frequency interval adjustment of the multi-band vibration wave composite field are determined. The phase difference adjustment is positively correlated with the frequency offset, while the frequency interval adjustment is negatively correlated with the peak value change. The adjustment determines the amplitude and direction of the change in the composite field parameters, which directly affects the subsequent vibration effect. The phase difference adjustment adopts a linear mapping relationship:

[0117] Phase difference adjustment amount = K1 × frequency offset;

[0118] K1 is the proportional coefficient, usually ranging from 0.5 to 2.0 rad / Hz, which is dynamically adjusted according to the patient's airway characteristics. The frequency interval adjustment adopts the inverse correlation relationship:

[0119] Frequency interval adjustment amount = -K2 × peak change;

[0120] K2 is a proportional coefficient, typically ranging from 0.1 to 0.5 Hz / %, and is dynamically adjusted according to the treatment phase. This adjustment strategy accurately responds to changes in resonant characteristics, increasing the phase difference to compensate for frequency deviation when the frequency offset increases, and reducing the frequency interval to enhance the resonant effect when the peak change decreases.

[0121] Based on the phase difference adjustment and frequency interval adjustment, the phase difference and frequency interval of the multi-band vibration wave composite field are updated to achieve directional focusing of vibration energy. Parameter update is the execution link of closed-loop control, converting the analysis results into control actions. The update formula is:

[0122] New phase difference = original phase difference + phase difference adjustment amount;

[0123] New frequency interval = original frequency interval + frequency interval adjustment amount;

[0124] Parameter updates utilize a smooth transition strategy to avoid discomfort caused by sudden changes, typically limiting single adjustments to no more than 15% of the original value. The updated parameters are used to regenerate a multi-band vibration wave composite field, enabling precise control and targeted focusing of vibration energy. This allows the energy to more effectively target sputum, improving expectoration efficiency and patient comfort.

[0125] In an embodiment of the present invention, the detailed implementation steps of constructing a sputum removal demand priority matrix for each area of ​​the patient's airway based on physiological parameter data and sputum distribution characteristic data include:

[0126] Physiological parameter data are normalized to obtain a weighted sum, which is recorded as the physiological load value of the airway region. The physiological load value quantifies the degree of abnormality in the physiological state of each region and reflects the urgency of clinical intervention. Normalization uses the Z-score standardization method to convert each physiological parameter into a standard score, eliminating dimension and range differences. The weighted sum calculation takes into account the clinical importance of different physiological parameters. Respiratory rate and blood oxygen saturation are generally given higher weights, reflecting their direct impact on the need for expectoration. The physiological load value is a dimensionless index, usually ranging from [0,10]. Higher values ​​indicate that the regional physiological state requires more intervention.

[0127] Based on sputum distribution data, sputum density and viscosity are calculated for each airway region, representing the sputum load value. This sputum load value quantifies the severity of sputum obstruction in each region and serves as a direct indicator of the need for expectoration. The calculation process is based on quantitative analysis of medical imaging data. Sputum density is obtained by converting image grayscale values ​​to a standard control curve, while sputum viscosity is estimated through clinical assessment and physical modeling. These two indicators are combined to form a comprehensive sputum load value, reflecting the impact of sputum on airway function. Sputum load values ​​typically range from [0 to 10], with higher values ​​indicating more severe sputum problems.

[0128] The product of the physiological load value and the sputum load value is used as the sputum drainage requirement for each airway region. The sputum drainage requirement comprehensively considers both physiological status and sputum characteristics, and is a comprehensive quantitative expression of sputum drainage priority. This multiplication reflects the synergistic effect of these two factors. When a region experiences both physiological abnormalities and sputum obstruction, the sputum drainage requirement increases significantly. This calculation method is consistent with clinical practice and accurately reflects the urgency of sputum drainage in each region. The sputum drainage requirement is a dimensionless indicator with a theoretical range of [0, 100], but in practice it is typically distributed in the interval [0, 50].

[0129] Based on the sputum removal need values, a sputum removal need priority matrix is ​​constructed, where each element in the matrix represents the normalized sputum removal need value. The sputum removal need priority matrix is ​​a two- or three-dimensional array that corresponds to the anatomical divisions of the airway and visually displays the sputum removal priority of each region. Normalization is performed using the Min-Max method, mapping the sputum removal need values ​​to the interval [0, 1] to form a standardized priority expression. Regions with values ​​closer to 1 in the matrix have higher priorities and receive more vibration energy during treatment, ensuring optimal utilization of treatment resources. Priority matrices are often visualized using heat maps to provide an intuitive reference for clinical decision-making.

[0130] In an embodiment of the present invention, the detailed implementation steps of performing local variance analysis on the acoustic impedance values ​​of the micro-segments on both sides of the candidate abnormal interface to obtain the local acoustic impedance fluctuation corresponding to the candidate abnormal interface include:

[0131] Centered on the candidate abnormal interface, microsegments of preset lengths on both sides are selected as analysis windows. The analysis window is the spatial range for local characteristic analysis and contains complete information about the interface and surrounding tissues. The preset length is typically 5-15 microsegments on each side of the interface, and is dynamically adjusted based on the airway region and imaging resolution to ensure that the window fully encompasses local features while not being so large as to include irrelevant areas. Window selection employs an adaptive strategy, appropriately reducing the window size in complex areas such as airway bifurcations and expanding it in simpler areas such as straight sections, improving the accuracy and adaptability of the analysis.

[0132] The variance of the acoustic impedance values ​​for all microsegments within the analysis window is calculated and recorded as the initial variance. This initial variance reflects the overall fluctuation of the acoustic impedance values ​​within the window and is a fundamental indicator for evaluating local characteristics. This calculation uses an unbiased variance estimation method, which accounts for the impact of sample size on estimation accuracy. Larger initial variance values ​​indicate more uneven acoustic characteristics in the local area, potentially indicating the presence of significant tissue interfaces or pathological changes.

[0133] Gaussian weighting is performed on the acoustic impedance values ​​within the analysis window to obtain a weighted acoustic impedance value sequence. Gaussian weighting is a processing method that emphasizes the central area and weakens the influence of the edge area. It can highlight the interface characteristics and reduce the influence of noise. The weighting formula is:

[0134] ;

[0135] ;

[0136] Where d_i is the distance from the i-th micro-segment to the center of the window, σ is the standard deviation parameter of the Gaussian function, usually 1 / 4 of the window length, w_i is the weight of the i-th micro-segment, Z_i is the weighted acoustic impedance value of the i-th micro-segment, and z_i is the acoustic impedance value of the i-th micro-segment. This weighting strategy allows the analysis to focus more on the area near the interface, improving the accuracy and sensitivity of feature extraction.

[0137] Calculate the variance of the weighted acoustic impedance value sequence, denoted as the weighted variance. The weighted variance reflects the fluctuation characteristics of the acoustic impedance after accounting for spatial distribution factors, and better reflects the true characteristics of the interface. The calculation method is similar to the initial variance, but uses weighted values ​​and considers the influence of weights in the statistical calculation. Comparing the weighted variance with the initial variance can reveal the spatial distribution pattern of interface characteristics and is an important basis for identifying true interface anomalies.

[0138] The ratio of the initial variance to the weighted variance is taken as the local acoustic impedance fluctuation. The local acoustic impedance fluctuation is a dimensionless indicator that comprehensively reflects the local variation characteristics of the acoustic impedance. The physical significance of this indicator is that for real tissue interface abnormal areas, the initial variance should be significantly greater than the weighted variance, because the abnormalities are mainly concentrated near the interface; while for random fluctuations or pseudo-anomalies, the difference between the initial variance and the weighted variance is not obvious. Therefore, the larger the fluctuation value, the more likely the candidate interface is to be a real tissue interface abnormal area. The fluctuation is usually greater than 1, and for obvious interface abnormalities, its value can reach 3-5 or even higher.

[0139] In an embodiment of the present invention, the detailed implementation steps of calculating the energy distribution ratio of the reflection path and the transmission path of the shock wave in each abnormal area of ​​the tissue interface based on the shock wave propagation path diagram include:

[0140] Based on the shock wave propagation path diagram, the angle of incidence of the shock wave at each abnormal region of the tissue interface is determined. The angle of incidence is a key geometric parameter when the wave meets the interface, directly affecting the reflection and transmission characteristics. This determination is based on ray tracing technology, which geometrically calculates the propagation path of the shock wave and the direction of the interface normal to obtain the precise angle of incidence. For curved interfaces, the local tangent plane approximation is used to calculate the local normal direction to ensure the accuracy of the angle calculation. The angle of incidence is expressed in radians or degrees, typically in the range [0, π / 2], and may vary in different regions and along different propagation paths.

[0141] Based on the incident angle and the reflection enhancement coefficient, the reflected energy ratio R of the shock wave in the abnormal area of ​​the tissue interface is calculated. The reflected energy ratio determines the proportion of the shock wave energy reflected back by the interface and is a key parameter for energy distribution. The calculation uses a modified reflection law that takes into account the non-ideal characteristics and frequency dependence of the tissue interface. The formula is:

[0142] ;

[0143] This formula takes into account the effects of the reflection enhancement coefficient and the angle of incidence. As the angle of incidence increases, the proportion of reflected energy increases, in line with the fundamental laws of wave mechanics. The reflected energy ratio ranges from [0,1] and represents the proportion of incident energy that is reflected.

[0144] Based on the transmission attenuation coefficient and the reflected energy ratio, the transmission energy ratio F of the shock wave in the abnormal area of ​​the tissue interface is calculated. The transmission energy ratio determines the proportion of energy that continues to propagate through the interface and is an important indicator for evaluating the efficiency of deep energy transfer. Based on the principle of energy conservation, the calculation formula is:

[0145] ;

[0146] This calculation logic takes into account two factors: first, the sum of the transmitted and reflected energies must equal the incident energy; second, some energy is lost during transmission due to factors such as dielectric absorption, which is represented by the transmission attenuation coefficient. The transmitted energy ratio ranges from [0, 1] and is typically complementary to the reflected energy ratio, but their sum is less than 1. The difference represents energy loss due to interface absorption.

[0147] The energy distribution ratio is obtained by normalizing the reflected energy ratio and the transmitted energy ratio. The energy distribution ratio is the parameter ultimately used to construct the multipath interference model, describing the distribution of incident energy among different propagation paths. The normalization process ensures the standardization of parameters and the consistency of calculations. The formula is:

[0148] Normalized reflected energy ratio = reflected energy ratio / (reflected energy ratio + transmitted energy ratio);

[0149] Normalized transmitted energy ratio = transmitted energy ratio / (reflected energy ratio + transmitted energy ratio);

[0150] The normalized ratios sum to 1, directly reflecting the relative energy distribution between the reflected and transmitted paths, facilitating subsequent interference model construction and energy focusing calculations. This distribution ratio dynamically adjusts with changes in vibration wave frequency, incident angle, and interface properties, ensuring the model accurately reflects the actual physical process.

[0151] This invention utilizes acoustic impedance imaging technology, tissue interface anomaly identification, multipath interference modeling, and multi-band vibration wave optimization to achieve precise loosening and efficient expulsion of airway sputum. The invention's adaptive optimization features adjust treatment parameters in real time based on individual patient differences and dynamic changes during treatment, significantly improving sputum removal efficiency and alleviating patient discomfort.

[0152] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0153] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0154] In the description of the present invention, it should be understood that the terms "first", "second", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0155] In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0156] In the description of the present invention, “several” means one or more, and “a large number” means two or more.

[0157] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0158] The formulas in this manual are all dimensionless and calculated using numerical values. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field based on actual conditions.

[0159] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A multi-band airway vibration sputum removal adaptive optimization system, characterized in that: include: Data acquisition module, used to obtain the patient's physiological parameter data and sputum distribution characteristic data during the treatment process; a sputum removal demand analysis module, configured to construct a sputum removal demand priority matrix for each area of ​​the patient's airway based on the physiological parameter data and the sputum distribution characteristic data; The acoustic impedance analysis module is used to obtain the acoustic impedance distribution data of each micro-segment of the patient's airway inner wall and identify abnormal areas of tissue interface based on the changes in acoustic impedance gradients between adjacent micro-segments; A wave propagation characteristic calculation module, configured to calculate the reflection enhancement coefficient and the transmission attenuation coefficient of the vibration wave in the abnormal tissue interface region based on the acoustic impedance mutation characteristics of the abnormal tissue interface region; A multipath interference modeling module, configured to construct a multipath interference model of shock wave propagation in the airway based on the reflection enhancement coefficient and the transmission attenuation coefficient; A vibration wave field generation module, configured to generate a multi-band vibration wave composite field based on the multi-path interference model and the expectoration demand priority matrix; A real-time monitoring and analysis module, configured to identify characteristic resonance peaks generated by the coupling of the multi-band vibration wave composite field and sputum by real-time monitoring of the spectrum characteristics of the pressure pulsation signal in the airway; A parameter adaptive optimization module, configured to dynamically adjust the phase difference and frequency interval of the multi-band vibration wave composite field according to the frequency offset and peak value change of the characteristic resonance peak; The equipment control execution module is used to control the expectoration equipment based on the adjusted parameters of the multi-band vibration wave composite field, so as to achieve accurate loosening and efficient discharge of airway sputum.

2. A multi-band airway vibration sputum removal adaptive optimization system according to claim 1, characterized in that: The method of identifying abnormal areas of tissue interfaces based on changes in acoustic impedance gradients between adjacent micro-segments includes: Normalizing the acoustic impedance value of each micro-segment in the acoustic impedance distribution data to obtain a normalized acoustic impedance value; Calculating the difference in the normalized acoustic impedance values ​​between adjacent micro-segments as the acoustic impedance gradient between the adjacent micro-segments; Counting the adjacent micro-segment pairs whose acoustic impedance gradient is greater than a preset gradient threshold, and recording them as candidate abnormal interfaces; Performing local variance analysis on the acoustic impedance values ​​of the micro-segments on both sides of the candidate abnormal interface to obtain the local acoustic impedance fluctuation corresponding to the candidate abnormal interface; The candidate abnormal interface whose local acoustic impedance fluctuation is greater than a preset fluctuation threshold is marked as the tissue interface abnormal area.

3. The multi-band airway vibration sputum removal adaptive optimization system according to claim 1, characterized in that: The calculation of the reflection enhancement coefficient and the transmission attenuation coefficient of the shock wave in the abnormal tissue interface region based on the acoustic impedance mutation characteristics of the abnormal tissue interface region includes: Acquiring acoustic impedance values ​​of micro-segments on both sides of the abnormal area of ​​the tissue interface, which are recorded as a first acoustic impedance value and a second acoustic impedance value respectively; Calculating an acoustic impedance mutation coefficient of the abnormal tissue interface region according to a ratio of the first acoustic impedance value to the second acoustic impedance value; Calculating a reflection enhancement coefficient of the shock wave in the abnormal area of ​​the tissue interface based on the acoustic impedance mutation coefficient and the incident angle of the shock wave; The transmission attenuation coefficient of the shock wave in the abnormal area of ​​the tissue interface is calculated according to the product of the reflection enhancement coefficient and the acoustic impedance mutation coefficient in combination with the frequency of the shock wave.

4. The multi-band airway vibration sputum removal adaptive optimization system according to claim 1, characterized in that: The constructing of a multipath interference model of shock wave propagation in the airway according to the reflection enhancement coefficient and the transmission attenuation coefficient includes: constructing a geometric topological model of the inner wall of the airway according to the acoustic impedance distribution data; Marking the position of the abnormal area of ​​the tissue interface in the geometric topological model, and generating a shock wave propagation path map including the abnormal area of ​​the tissue interface; Based on the shock wave propagation path diagram, calculating the energy distribution ratio of the shock wave between the reflection path and the transmission path in each abnormal area of ​​the tissue interface; According to the energy distribution ratio, the reflection enhancement coefficient and the transmission attenuation coefficient, a multipath interference model of the shock wave is constructed. The multipath interference model includes the interference intensity distribution and phase superposition distribution of the shock wave on the inner wall of the airway.

5. The multi-band airway vibration sputum removal adaptive optimization system according to claim 1, characterized in that: The generating of a multi-band vibration wave composite field based on the multipath interference model and the expectoration demand priority matrix includes: Determining the vibration energy demand weights of each area in the airway according to the expectoration demand priority matrix; Based on the multipath interference model, calculating the initial phase distribution and frequency combination of the multi-band vibration waves that meet the vibration energy requirement weight; performing phase modulation on the initial phase distribution to generate a phase-modulated multi-band shock wave signal; The phase-modulated multi-band shock wave signal is superimposed on the geometric topological model of the airway inner wall to generate the multi-band shock wave composite field, and the vibration energy transfer efficiency of the multi-band shock wave composite field in the abnormal area of ​​the tissue interface is compensated.

6. The multi-band airway vibration sputum removal adaptive optimization system according to claim 1, characterized in that: The method of real-time monitoring of the spectrum characteristics of the pressure pulsation signal in the airway and identifying the characteristic resonance peak generated by the coupling of the multi-band vibration wave composite field and sputum includes: Performing Fourier transform on the intra-airway pressure pulsation signal to obtain a frequency spectrum distribution of the pressure pulsation signal; Extracting spectrum peaks with amplitudes greater than a preset amplitude threshold from the spectrum distribution and recording them as candidate resonance peaks; Calculating a matching degree between the frequency of the candidate resonance peak and the frequency interval of the multi-band shock wave composite field, wherein the matching degree is determined by a ratio of the frequency of the candidate resonance peak to the frequency interval of the multi-band shock wave composite field; The candidate resonance peak whose matching degree is greater than a preset matching threshold is marked as the characteristic resonance peak.

7. The multi-band airway vibration sputum removal adaptive optimization system according to claim 1, characterized in that: The dynamically adjusting the phase difference and frequency interval of the multi-band vibration wave composite field according to the frequency offset and peak value change of the characteristic resonance peak includes: Calculating the difference between the frequency of the characteristic resonance peak and the center frequency of the multi-band vibration wave composite field, and recording it as the frequency offset; Counting the change rate of the peak value of the characteristic resonance peak within a preset time period, and recording it as the peak change; determining a phase difference adjustment amount and a frequency interval adjustment amount of the multi-band shock wave composite field according to the frequency offset amount and the peak value change; Based on the phase difference adjustment amount and the frequency interval adjustment amount, the phase difference and the frequency interval of the multi-band shock wave composite field are updated.

8. The multi-band airway vibration sputum removal adaptive optimization system according to claim 1, characterized in that: The step of constructing a sputum removal priority matrix for each area of ​​the patient's airway based on the physiological parameter data and the sputum distribution characteristic data includes: Normalizing the physiological parameter data to obtain a weighted sum of the physiological parameter data, which is recorded as a physiological load value of the airway area; Calculate the sputum density and sputum viscosity in each airway region based on the sputum distribution characteristic data, and record them as sputum load values; The product of the physiological load value and the sputum load value is used as the sputum discharge demand value of each airway area; Based on the sputum discharge requirement value, the sputum discharge requirement priority matrix is ​​constructed, wherein the element value of the sputum discharge requirement priority matrix is ​​a normalized result of the sputum discharge requirement value.

9. The multi-band airway vibration sputum removal adaptive optimization system according to claim 2, characterized in that: The performing of local variance analysis on the acoustic impedance values ​​of the micro-segments on both sides of the candidate abnormal interface to obtain the local acoustic impedance fluctuation corresponding to the candidate abnormal interface includes: Taking the candidate abnormal interface as the center, micro-segments of preset lengths on both sides are selected as analysis windows; Calculating the variance of the acoustic impedance values ​​of all micro-segments within the analysis window, and recording it as the initial variance; Performing Gaussian weighting on the acoustic impedance values ​​within the analysis window to obtain a weighted acoustic impedance value sequence; Calculating the variance of the weighted acoustic impedance value sequence, which is recorded as weighted variance; The ratio of the initial variance to the weighted variance is used as the local acoustic impedance fluctuation.

10. The multi-band airway vibration sputum removal adaptive optimization system according to claim 4, characterized in that: The calculating, based on the shock wave propagation path diagram, the energy distribution ratio of the reflection path and the transmission path of the shock wave in each abnormal area of ​​the tissue interface includes: Determining the incident angle of the shock wave at each abnormal area of ​​the tissue interface according to the shock wave propagation path diagram; Calculating the reflected energy ratio of the shock wave at the abnormal area of ​​the tissue interface based on the incident angle and the reflection enhancement coefficient; Calculating the transmission energy ratio of the shock wave in the abnormal area of ​​the tissue interface according to the transmission attenuation coefficient and the reflected energy ratio; The reflected energy ratio and the transmitted energy ratio are normalized to obtain the energy distribution ratio.

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