Breathing machine pipeline dynamic stabilizing system based on mechanical intelligence

Abstract

The invention belongs to the technical field of breathing machine pipeline intelligent control, and discloses a breathing machine pipeline dynamic stabilization system based on mechanical intelligence, which is characterized in that fluid acoustic characteristics, gas micro-dynamic waveforms and patient breathing coupling data of a breathing machine pipeline are acquired, and a pipeline micro-disturbance holographic model is constructed; performing resonance domain dynamic analysis and gas flow parameter quantum-level fluctuation detection to obtain a gas fluctuation characteristic spectrum; constructing an adaptive tuning matrix according to the characteristic spectrum, and applying an intelligent intervention strategy set to obtain a pipeline steady-state fluid model and a stability score; evaluating system reliability based on a stability score, performing monitoring compilation, and continuously optimizing a characteristic spectrum and a tuning matrix through clinical verification; according to the invention, continuous evolution of dynamic and stable control of the breathing machine pipeline is realized, and the safety and the reliability of breathing treatment are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of ventilator pipelines, and more specifically, to a dynamic stabilization system of ventilator pipelines based on mechanical intelligence. Background Art

[0002] Existing ventilator tubing systems have a problem of great clinical significance in specific clinical environments: the amplification effect of gas micro-disturbance in the nonlinear resonance domain. Under mechanical ventilation modes of specific frequencies, the tubing system will form acoustic-fluid dynamics coupled resonance domains, which will selectively amplify small disturbances in the airflow. This phenomenon causes heterogeneity and periodic pulsation in the respiratory gas composition at the microscopic scale, which is particularly evident during high-precision oxygen therapy and anesthetic gas delivery. Clinical data show that after such gas micro-disturbance is amplified in the resonance domain, it may cause fluctuations in the gas composition received by the patient. In the scenarios of anesthetic gas and precise oxygen concentration control, such fluctuations will cause instability in the therapeutic concentration, thereby endangering the safety and effectiveness of precise oxygen therapy, anesthesia and special gas therapy. Traditional technologies mainly alleviate the problem through passive methods such as changing the rigidity of the tubing material or adding dampers. They lack the ability to monitor the dynamic changes of the resonance domain in real time and the intelligent control mechanism to implement precise active intervention.

[0003] In view of this, the present invention proposes a ventilator pipeline dynamic stabilization system based on mechanical intelligence to solve the above problems. Summary of the Invention

[0004] The present application provides a ventilator circuit dynamic stabilization system based on mechanical intelligence, which is used to effectively identify and suppress the micro-disturbance amplification phenomenon in the ventilator circuit and maintain gas delivery stability, capture the fluid acoustic characteristics and the resonance modes in the gas micro-dynamic waveform, reveal the hidden relationship between the circuit resonance and the patient's respiratory coupling, and realize the precise modeling of the resonance domain and the intelligent regulation of the airflow micro-disturbance, effectively solving the limitations of the traditional passive suppression method that lacks real-time monitoring and active intervention capabilities, improving the accuracy of patient treatment and enhancing clinical safety.

[0005] The present application provides a ventilator circuit dynamic stabilization system based on mechanical intelligence, and the ventilator circuit dynamic stabilization system based on mechanical intelligence includes:

[0006] Perturbation modeling module: collects ventilator pipeline fluid acoustic characteristic data, gas microdynamic waveform data and patient respiratory coupling data; constructs a pipeline resonance map based on the fluid acoustic characteristic data, performs resonance characteristic analysis on the pipeline resonance map, and obtains resonance characteristic quantitative parameters; constructs a fluid disturbance propagation network based on the gas microdynamic waveform data and the patient respiratory coupling data, performs propagation path analysis on the fluid disturbance propagation network, and obtains disturbance amplification sensitive point data; constructs a pipeline microdisturbance holographic model based on the resonance characteristic quantitative parameters and the disturbance amplification sensitive point data;

[0007] Characteristic analysis module: performs resonance domain dynamic analysis based on the pipeline micro-disturbance holographic model to obtain a resonance domain spatiotemporal distribution map; performs quantum-level fluctuation detection on the airflow parameters in the pipeline micro-disturbance holographic model to obtain a micro-disturbance amplification curve; performs gas fluctuation analysis based on the resonance domain spatiotemporal distribution map and the micro-disturbance amplification curve to obtain a gas fluctuation characteristic spectrum;

[0008] Steady-state control module: constructing an adaptive tuning matrix based on the gas fluctuation characteristic spectrum to obtain an intelligent intervention strategy set; applying the intelligent intervention strategy set to the pipeline system to suppress resonance to obtain a pipeline steady-state fluid model; performing a multi-dimensional stability assessment on the pipeline steady-state fluid model to obtain a stability score;

[0009] Evidence-based optimization module: Assess the safety of gas delivery based on the stability score to obtain the pipeline system reliability level; compile monitoring based on the pipeline system reliability level to obtain real-time monitoring guidelines; conduct clinical verification and collection of the application effect of the pipeline system to obtain performance data; optimize the gas fluctuation characteristic spectrum, the adaptive tuning matrix and the real-time monitoring guidelines based on the performance data to achieve continuous evolution of dynamic stability control of the ventilator pipeline.

[0010] The technical effects and advantages of the mechanical intelligence-based dynamic stabilization system for ventilators of the present invention are as follows:

[0011] By constructing a holographic model of pipeline micro-disturbance, the present invention achieves comprehensive capture and precise modeling of tiny disturbances in the ventilator pipeline system. By fusion analysis of fluid acoustic characteristic data and gas micro-dynamic waveform data, it achieves precise identification of pipeline resonance characteristics and disturbance propagation paths, improving the sensitivity and accuracy of disturbance perception. Through resonance domain dynamic analysis and quantum-level fluctuation detection, multi-dimensional feature extraction and hazard pattern recognition of gas disturbances are achieved, enhancing the comprehensiveness and accuracy of risk assessment. Adopting an adaptive tuning matrix and intelligent intervention strategy set, dynamic regulation of pipeline system resonance suppression is achieved. Through multi-dimensional stability assessment, the stability performance of the system under various operating conditions is comprehensively considered. Using gas delivery safety assessment and reliability level classification, the clinical significance of pipeline system stability and the level of patient safety are deeply explored. Through dynamic stability status reporting and real-time monitoring guidelines, the visualization and operability of system operation are improved. By adopting clinical verification and performance data feedback mechanisms, the system has self-optimization capabilities and can continuously evolve based on clinical application results. Through refined disturbance modeling based on frequency domain hierarchical analysis and resonance feature extraction, stable control of gas flow at the submicroscopic level is achieved. By using deep neural network fluctuation identifier and chaos theory analysis, the system's ability to recognize complex disturbance patterns is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 Schematic diagram of the dynamic stabilization system of the ventilator pipeline based on mechanical intelligence of the present invention;

[0013] Figure 2 Schematic diagram of the dynamic stabilization method of ventilator pipeline based on mechanical intelligence of the present invention. DETAILED DESCRIPTION

[0014] 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.

[0015] Example 1;

[0016] See also Figure 1As shown, the mechanical intelligence-based ventilator pipeline dynamic stabilization system described in this embodiment includes: a disturbance modeling module: collecting ventilator pipeline fluid acoustic characteristic data, gas microdynamic waveform data and patient respiratory coupling data; constructing a pipeline resonance map based on the fluid acoustic characteristic data, performing resonance characteristic analysis on the pipeline resonance map to obtain resonance characteristic quantitative parameters; constructing a fluid disturbance propagation network based on the gas microdynamic waveform data and the patient respiratory coupling data, performing propagation path analysis on the fluid disturbance propagation network to obtain disturbance amplification sensitive point data; and constructing a pipeline microdisturbance holographic model based on the resonance characteristic quantitative parameters and the disturbance amplification sensitive point data.

[0017] Characteristic analysis module: performs resonance domain dynamic analysis based on the pipeline micro-disturbance holographic model to obtain a resonance domain spatiotemporal distribution map; performs quantum-level fluctuation detection on the airflow parameters in the pipeline micro-disturbance holographic model to obtain a micro-disturbance amplification curve; performs gas fluctuation analysis based on the resonance domain spatiotemporal distribution map and the micro-disturbance amplification curve to obtain a gas fluctuation characteristic spectrum;

[0018] Steady-state control module: constructing an adaptive tuning matrix based on the gas fluctuation characteristic spectrum to obtain an intelligent intervention strategy set; applying the intelligent intervention strategy set to the pipeline system to suppress resonance to obtain a pipeline steady-state fluid model; performing a multi-dimensional stability assessment on the pipeline steady-state fluid model to obtain a stability score;

[0019] Evidence-based optimization module: gas delivery safety is assessed based on the stability score to obtain a pipeline system reliability level; monitoring is compiled based on the pipeline system reliability level to obtain a real-time monitoring guide; clinical verification and collection of the application effect of the pipeline system are performed to obtain performance data; based on the performance data, the gas fluctuation characteristic spectrum, the adaptive tuning matrix and the real-time monitoring guide are optimized to achieve continuous evolution of dynamic stability control of the ventilator pipeline; each module is connected by wired and / or wireless means to achieve data transmission between modules.

[0020] Preferably, the disturbance modeling module may specifically include the following steps:

[0021] (1) Collecting ventilator circuit fluid acoustic characteristic data, gas microdynamic waveform data, and patient respiratory coupling data;

[0022] (2) performing frequency domain hierarchical analysis on the fluid acoustic characteristic data to obtain a spectrum hierarchy set; extracting resonance points from the spectrum hierarchy set to obtain a resonance node sequence; and constructing a pipeline resonance mapping diagram based on the resonance node sequence;

[0023] (3) extracting features from the pipeline resonance map using a resonance feature extraction algorithm to obtain resonance feature quantization parameters;

[0024] (4) performing spatiotemporal correlation analysis on the gas microdynamic waveform data and the patient respiratory coupling data to obtain a fluid-patient interaction initial network; calculating a complex system entropy value on the fluid-patient interaction initial network to obtain a system complexity evaluation; and constructing a fluid disturbance propagation network based on the fluid-patient interaction initial network and the system complexity evaluation;

[0025] (5) applying a topological analysis algorithm to discover key nodes in the fluid disturbance propagation network to obtain a disturbance propagation hub; quantifying sensitivity based on the disturbance propagation hub to obtain disturbance amplification sensitive point data;

[0026] (6) constructing a micro-disturbance body framework, mapping the resonance characteristic quantization parameters and the disturbance amplification sensitive point data to the micro-disturbance body framework to form an initial disturbance model;

[0027] (7) Using multi-scale fusion technology to add spatial and temporal attributes to the initial disturbance model, a spatiotemporal disturbance model is obtained; performing micro-disturbance behavior inference on the spatiotemporal disturbance model, a pipeline micro-disturbance holographic model is obtained.

[0028] Specifically, first, a professional sensor array is used to collect fluid acoustic characteristic data in the ventilator pipeline system. These sensors include ultra-micro acoustic wave sensors, high-precision pressure sensors, and flow sensors, which are distributed at key nodes of the pipeline system, such as pipeline connections, bends, and inner diameter changes. The fluid acoustic characteristic data is divided into frequency domain windows according to four frequency scales: ultra-micro frequency band (0 to 10Hz), low frequency band (10 to 100Hz), medium frequency band (100 to 1000Hz), and high frequency band (greater than 1000Hz) to obtain a multi-level frequency domain window set. Wavelet transform and Fourier analysis are applied to the acoustic data in each frequency domain window to convert the time domain signal into frequency domain representation to obtain standardized spectrum data. The core formula of wavelet transform is: Among them, Wtr(a,b) is the wavelet coefficient, a is the scale parameter, b is the translation parameter, f(t) is the original signal, is the conjugate of the wavelet basis function. By calculating the energy density of different frequency bands and identifying the peak points, the energy distribution spectrum is obtained. The standardized spectrum data and energy distribution spectrum are grouped by frequency level to form a spectrum level set. This spectrum level set is the basis for subsequent resonance point extraction. Resonance point extraction is performed on the spectrum level set to identify frequency points with significant resonance characteristics. Resonance point extraction uses a combination of peak detection and harmonic analysis. The peak detection algorithm is used to identify local maximum points in the energy distribution spectrum:

[0029] Point = {f_i|E(f_i)>E(f_i1)and E(f_i)>E(f_i+1)and E(f_i)>θ}; where Point is the peak point set, f_i is the frequency point, E(f_i) is the energy density at the frequency point f_i, and θ is the preset threshold. Harmonic analysis is used to determine whether there is a harmonic relationship between these peak points: Among them, Rea(f_i,f_j) represents the harmonic relationship between frequency points fi and f_j, ε is the tolerance threshold, and round is the rounding function. Through peak detection and harmonic analysis, the resonance node sequence is obtained. A pipeline resonance mapping diagram is constructed based on the resonance node sequence. The mapping diagram is a multi-dimensional visualization structure, in which the horizontal axis represents the physical position of the pipeline, the vertical axis represents the frequency, and the color depth represents the resonance intensity. For each node in the resonance node sequence, there is a corresponding point on the mapping diagram, and its color depth is determined by the amplitude. In this way, a complete pipeline resonance mapping diagram is constructed. The resonance feature extraction algorithm is used to extract features from the pipeline resonance mapping diagram. The resonance feature extraction algorithm first performs regional segmentation on the mapping diagram, dividing the entire mapping diagram into several sub-regions: for each region, its resonance center frequency, frequency bandwidth, resonance intensity and resonance persistence are calculated: Resonance center frequency: Frequency bandwidth: BW(Area)=max(f_i)-min(f_i); Resonance intensity: IV(Area)=max(Af); Resonance persistence: For all f_i∈Area, where |Area| represents the number of resonant nodes in the area and Af represents the amplitude, these calculations yield a set of quantitative parameters for the resonant characteristics. Simultaneously, gas microdynamic waveform data and patient respiration coupling data are collected. A spatiotemporal correlation analysis is performed on these two types of data to establish a corresponding relationship between gas dynamics and patient respiration. Spatiotemporal correlation analysis uses a cross-correlation function to calculate the correlation between two time series: Where RL_xy(τ) is the cross-correlation coefficient at time delay τ, x(t) and y(t) are the time series of gas microdynamic data and patient respiration data, respectively, and Sum is the sequence length. By calculating the cross-correlation coefficient at different time delays, the temporal relationship between gas dynamics and patient respiration is determined, thereby obtaining the initial fluid-patient interaction network. The complex system entropy value of the initial fluid-patient interaction network is calculated to assess the system complexity. The entropy value is calculated using the sample entropy method: Where SampEn is the sample entropy, m is the embedding dimension, r is the tolerance parameter, N is the number of data points, and A^m(r) and B^m(r) are the pattern matching counts respectively. The higher the sample entropy, the higher the complexity of the system and the greater the uncertainty. Based on the initial network of fluid-patient interaction and the system complexity assessment, a fluid disturbance propagation network is constructed. The network is a directed weighted graph, where nodes represent key positions in the pipeline system, edges represent the propagation paths of disturbances, and the weights of edges represent the intensity of disturbance propagation. A topological analysis algorithm is applied to discover key nodes in the fluid disturbance propagation network. Key nodes are locations that have an important influence in the disturbance propagation process and are identified by calculating the centrality indicators of the nodes. The centrality indicators include degree centrality, betweenness centrality, and eigenvector centrality. By comprehensively considering these centrality indicators, a set of disturbance propagation hubs is identified. Sensitivity quantification is performed based on the disturbance propagation hubs. The sensitivity quantification process first simulates the generation of small disturbances at each hub, and then observes the propagation and amplification effect of the disturbance in the entire network: Where S(cen) is the sensitivity of the pivot point cen, ΔO_e is the input perturbation amplitude generated at pivot point O_e, ΔO_r is the output response amplitude observed at node O_r, and Nd is the total number of nodes. By calculating the sensitivity of each pivot point, perturbation amplification sensitive point data is obtained. A perturbation ontology framework is constructed to uniformly represent and organize perturbation-related concepts and relationships. The ontology framework consists of three levels: categories: resonance, perturbation, propagation, and impact; attributes: frequency, intensity, persistence, sensitivity; and relationships: generation, propagation, amplification, and attenuation. The resonance feature quantization parameters and perturbation amplification sensitive point data are mapped to the perturbation ontology framework to form an initial perturbation model. The mapping process associates specific data instances with corresponding concepts in the ontology framework. For example, elements in the resonance feature quantization parameters Q are mapped to instances of the resonance class, and elements in the perturbation amplification sensitive point data S are mapped to instances of the perturbation class, and relationships are established between them. Use multi-scale fusion technology to add spatial and temporal attributes to the initial disturbance model. Spatial attributes represent the distribution of disturbances in the physical space of the pipeline, and temporal attributes represent the evolution of disturbances over time. Multi-scale fusion technology includes spatial interpolation and time series prediction; spatial interpolation: using Kriging interpolation to estimate the disturbance value of unmeasured points; time series prediction: using the autoregressive integrated moving average model to predict the time evolution of disturbances; through multi-scale fusion, a spatiotemporal disturbance model is obtained, which can describe the distribution and changes of disturbances in time and space dimensions. Perform micro-disturbance behavior inference on the spatiotemporal disturbance model to predict the propagation and impact of disturbances under different conditions. Behavioral reasoning is based on probabilistic graphical models (such as Bayesian networks) or rule reasoning systems: Here, PT(E|T) is the posterior probability of event E given observation data T, PT(T|E) is the likelihood probability of observation data T given event E, PT(E) is the prior probability of event E, and PT(T) is the marginal probability of observation data T. Through this inference mechanism, different disturbance behaviors and their potential impacts are predicted, ultimately resulting in a holographic model of pipeline micro-disturbance.

[0030] Preferably, the feature analysis module may specifically include the following steps:

[0031] (1) Obtain ventilation mode parameters, flow setting parameters, and pressure limit parameters from the ventilator operation database to construct an operating parameter baseline sequence;

[0032] (2) Based on the baseline sequence of operating parameters and the holographic model of pipeline micro-perturbations, a resonance prediction dynamics model is constructed to obtain the spatiotemporal distribution map of the resonance domain;

[0033] (3) Extract the microscopic parameter sequence of the airflow from the pipeline micro-disturbance holographic model, build a quantum-level disturbance measurement network, and obtain the original microscopic disturbance data;

[0034] (4) Perform nonlinear dynamic analysis and bifurcation theory on the original data of micro-perturbation to identify the critical perturbation amplification point and obtain the micro-perturbation amplification curve;

[0035] (5) Perform multi-dimensional cross-mapping of the resonance domain spatiotemporal distribution map and the perturbation amplification curve, calculate the resonance perturbation synergy index, and obtain the interaction strength spectrum;

[0036] (6) Based on the interaction intensity spectrum, a deep neural network fluctuation identifier is constructed to identify unstable patterns at clinical risk levels and obtain a gas delivery analysis report;

[0037] (7) Feature extraction and clinical risk grading are performed on the gas delivery analysis report to identify typical fluctuation hazard patterns and obtain a gas fluctuation characteristic spectrum.

[0038] Specifically, the operating parameters required for the current treatment are first extracted from the ventilator operation database, including ventilation modes (such as assisted control ventilation, synchronized intermittent mandatory ventilation, pressure support ventilation, etc.), flow settings (such as constant flow, decreasing flow, sinusoidal flow, etc.) and pressure limits (such as peak pressure, plateau pressure, positive end-expiratory pressure, etc.). These parameters constitute the operating parameter baseline sequence, which represents the working state and setting conditions of the ventilator. Based on the operating parameter baseline sequence and the previously constructed pipeline micro-perturbation holographic model, a resonance prediction dynamics model is constructed. This model uses fluid mechanics equations and acoustic propagation theory to predict the resonance behavior of the pipeline system under different operating conditions: and Where ρ is the gas density, v is the gas velocity vector, p is the pressure, μ is the dynamic viscosity, g is the gravitational acceleration, represents the viscous diffusion term, represents the Laplace operator, represents the convective derivative, the velocity change caused by the gas's own motion. By numerically solving these equations, the distribution of resonance domains in the pipeline system under different operating conditions is obtained. The resonance domain refers to the frequency and spatial region where the system is prone to resonance, and the spatiotemporal distribution of the resonance domain is obtained by calculation. The microscopic parameter sequence of the airflow is extracted from the pipeline micro-disturbance holographic model, including parameters such as microscale flow velocity fluctuations, pressure pulsations, and temperature gradient changes. These parameters constitute a description of the microscopic state of the airflow and require the use of high-precision sensors and special signal processing techniques to capture. Based on these microscopic parameters, a quantum-level perturbation measurement network is constructed. The network consists of multiple distributed sensor nodes, each of which captures local microscopic perturbation information and integrates it into a global perturbation picture through a data fusion algorithm to obtain the raw data of the microscopic perturbation. Nonlinear dynamic analysis is performed on the raw data of the microscopic perturbation to identify nonlinear behavior and instability in the system. Nonlinear dynamics analysis involves phase space reconstruction, Lyapunov exponent calculation, and related dimension estimation: Phase space reconstruction: Using time-delay embedding to map a one-dimensional time series into a multidimensional phase space: Xo(t) = [xo(t), xo(t+δ), ..., x(t+(dv)δ)]; where Xo(t) is the reconstructed phase space vector, xo(t) is the original time series, δ is the time delay, and dv is the embedding dimension. Lyapunov exponent: A measure of a system's sensitivity to initial conditions. A positive maximum Lyapunov exponent indicates chaotic behavior. Where LN is the Lyapunov index, d(0) is the distance between two points in the initial phase space, and d(t) is the distance between two points after time t. Combined with bifurcation theory, the dynamic behavior changes of the system when the parameters change are analyzed. Bifurcation refers to the phenomenon that the qualitative behavior of the system undergoes a sudden change when the parameters change, such as from a stable state to a periodic oscillation or chaotic state. Through bifurcation analysis, the critical perturbation amplification point of the system is identified, that is, the point where a small perturbation may be significantly amplified when the system parameters reach a certain critical value. Based on these analyses, the micro-perturbation amplification curve is drawn. The resonance domain spatiotemporal distribution map and the micro-perturbation amplification curve are multi-dimensionally cross-mapped to quantify the interaction between resonance and perturbation. Multi-dimensional cross-mapping is a nonlinear time series analysis method used to detect the causal relationship between two variables: Where YS(XL→YL) is the mapping value from XL to YL, y_n_i is the value in the YL sequence, and w_i is the weight. By calculating the cross-mapping strength between resonance and perturbation, the resonance-perturbation synergy index is obtained. These synergy indices are integrated to form an interaction strength spectrum. Based on the interaction strength spectrum, a deep neural network fluctuation identifier is constructed. This identifier uses an architecture that combines convolutional neural networks and recurrent neural networks: the convolutional layer captures the spatial features in the interaction strength spectrum; the recurrent layer captures the dynamic features in the time series. Through deep learning, the identifier can learn clinically dangerous unstable patterns from the interaction strength spectrum, such as sharp pressure fluctuations or unstable changes in gas concentration. The identification results form a gas delivery analysis report that details the unstable phenomena in the system and their potential clinical impact. Chaos theory is applied to the gas delivery analysis report to identify deterministic chaotic attractors. Chaotic attractors are singular structures in phase space that represent the long-term behavior of the system and have fractal dimensions and sensitive dependence on initial conditions. By calculating the fractal dimension and Lyapunov spectrum of the trajectories in phase space, the chaotic attractor structure in the system is identified, resulting in an unstable structure map. This map describes the type, intensity, and distribution characteristics of the unstable behavior in the system. A gas fluctuation hazard classification system was constructed, encompassing four main types of fluctuations: oxygen concentration pulsation, characterized by periodic or irregular fluctuations in oxygen concentration, which may lead to unstable patient oxygenation; anesthetic depth fluctuation, characterized by fluctuations in anesthetic gas concentration, which may lead to unstable anesthetic depth; pressure spike propagation, characterized by sudden and propagating pressure changes in the pipeline system, which may cause lung damage; and humidity inhomogeneity, characterized by uneven distribution of gas humidity, which may lead to airway dryness or secretion accumulation. Based on a clinical medical knowledge base and historical case database, clinical hazard levels were assigned to various patterns in the unstable structure map. The hazard levels were categorized into four levels: low risk, moderate risk, high risk, and critical risk, based on the severity of the potential clinical consequences of the fluctuations. Hazard rating data was generated through expert evaluation and historical data analysis. Clinical impact parameters for different fluctuation types were calculated, including fluctuation amplitude, frequency characteristics, and duration. Fluctuation amplitude indicates the degree of deviation from normal values, frequency characteristics indicate the periodicity or randomness of the fluctuation, and duration indicates how long the fluctuation persists. Together, these parameters constitute the fluctuation clinical risk index, which quantifies the potential impact of fluctuations on patient safety. A patient safety impact prediction model is constructed based on the fluctuation clinical risk index. This model uses machine learning methods, such as support vector machines or random forests, to predict the impact of specific fluctuation patterns on different patient groups: Among them, YAC is the predicted safety impact level, Xor is the fluctuation clinical risk index vector, are model parameters, and fh is the prediction function. Through training and validation, an accurate prediction model is obtained, forming a complete gas fluctuation signature spectrum. This signature spectrum describes the characteristics, degree of harm, and impact range of different types of fluctuations, providing a basis for the subsequent formulation of intervention strategies.

[0039] Preferably, the steady-state control module may specifically include the following steps:

[0040] (1) Based on the gas fluctuation characteristic spectrum, a micro-disturbance suppression strategy generator is constructed to calculate the optimal intervention plan and obtain an intervention strategy library;

[0041] (2) Using reinforcement learning algorithms to construct an adaptive tuning matrix and transform the intervention strategy library into an intelligent intervention strategy set;

[0042] (3) Extract key disturbance parameters from the pipeline micro-disturbance holographic model, apply the intelligent intervention strategy set to perform real-time intervention, and obtain the corrected fluid dynamics parameters;

[0043] (4) recalculating the key stability indicators of the pipeline system based on the corrected fluid dynamics parameters to obtain a steady-state fluid parameter set;

[0044] (5) verifying the clinical applicability of the steady-state fluid parameter set in combination with medical safety standards and individual physiological needs of patients to obtain verified stable parameters;

[0045] (6) constructing a pipeline steady-state fluid model based on the verified stable parameters to display the real-time stable state of the system;

[0046] (7) Applying a multi-dimensional stability evaluation algorithm, a comprehensive stability evaluation is performed on the pipeline steady-state fluid model to obtain a stability score.

[0047] Specifically, based on the previously constructed gas fluctuation characteristic spectrum, a micro-disturbance suppression strategy generator is designed. This generator uses an optimization algorithm to calculate the optimal intervention plan, aiming to minimize the perturbation and resonance effects in the system. The optimization problem can be expressed as: minJ(αt) = ∑we_u × D_i(αt); where αt is the intervention parameter vector, J(αt) is the objective function, D_u(αt) is the degree of the u-th perturbation type, and we_u is the interference weight coefficient. By solving this optimization problem, the optimal intervention parameters are obtained and a library of intervention strategies is generated, each of which targets a specific perturbation pattern. An adaptive tuning matrix is ​​constructed using a reinforcement learning algorithm. Reinforcement learning is a machine learning method that learns optimal strategies through interaction with the environment. In this embodiment, a deep Q-network algorithm is used: Q(St,Ac) = E[rn+z×max_Ac′Q(St′,Ac′)|St,Ac]; where Q(St,Ac) is the value function of taking action Ac in state St, rn is the immediate reward, z is the discount factor, St′ is the next state, and Ac′ is the next action. By continuously interacting with the environment and updating the Q value, the optimal strategy is learned. The adaptive tuning matrix TZ represents the mapping relationship between different intervention strategies applied in different system states: TZ = [st si,ci ]_(mn×rn); among them, st si,ci Represents the probability or weight of applying strategy ci under state si, mn is the number of system states, and rn is the number of strategies. Through the adaptive tuning matrix, the intervention strategy library is converted into an intelligent intervention strategy set, where each strategy is a mapping relationship that maps the system state to a specific intervention action. Key disturbance parameters are extracted from the pipeline micro-disturbance holographic model, including resonance frequency, disturbance amplitude, propagation speed and amplification factor. These parameters constitute a description of the current disturbance state of the system. According to the current state, the corresponding strategy in the intelligent intervention strategy set is applied for real-time intervention. Intervention measures include adjusting the pipeline length, modifying the flow curve, changing the pressure limit or adding a damper to suppress disturbances and resonance. Through these intervention measures, the fluid dynamics behavior of the system is corrected to obtain the corrected fluid dynamics parameters. The key stability indicators of the pipeline system are recalculated based on the corrected fluid dynamics parameters. Stability indicators include flow smoothness, pressure fluctuation amplitude, gas concentration uniformity and response time, which together reflect the stable state of the system. The calculation formulas include: flow smoothness: Where σf is the flow standard deviation, μf is the flow mean; pressure fluctuation amplitude: A_pa = max(pa) - min(pa), indicating the maximum pressure fluctuation range; gas concentration uniformity: Where cq is the gas concentration; response time: Tr = t_la - t_ne; where t_la is the input change time and t_ne is the time it takes for the output to reach a stable state; by calculating these indicators, a steady-state fluid parameter set is obtained. The steady-state fluid parameter set is verified for clinical applicability based on medical safety standards and individual patient physiological needs. The verification process compares the parameter values ​​with the safety ranges specified by medical standards and the individual patient needs to ensure that the system's stable state meets clinical requirements. Verification standards include: Medical safety standards: referencing international standards such as ISO80601 (medical ventilator safety standard) and national standards; Individual patient needs: Developing personalized requirements based on the patient's age, condition, respiratory function, and treatment goals; Through verification, parameters that meet the requirements are screened, forming verified stable parameters. A multidimensional fluid stability evaluation system is constructed to comprehensively assess system stability. The evaluation system consists of four main dimensions: pressure stability, which assesses the extent and characteristics of pressure fluctuations in the system; composition uniformity, which assesses the uniformity and stability of gas composition distribution; flow smoothness, which assesses the smoothness and controllability of the flow curve; and patient adaptability, which assesses the system's adaptability to changes in the patient's breathing pattern. Each dimension includes multiple evaluation indicators, which together form a complete evaluation system. Verified stability parameters are standardized to eliminate dimensional differences between different indicators. Standardization methods include range normalization and Z-score normalization. This process maps the values ​​of different indicators to the same scale, resulting in a standardized stability indicator set. The weights of each dimension and indicator are determined using a combination of the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM). The AHP constructs a judgment matrix based on expert judgment. The indicator weights are obtained by calculating the maximum eigenvalue and corresponding eigenvector of the judgment matrix. The Entropy Weight Method (EWM) calculates the weights of each indicator based on the principle of information entropy. The results of the two methods are combined to obtain a comprehensive weight vector. A comprehensive scoring model is constructed, and a weighted fusion algorithm is used to calculate the system's weighted stability score. A fuzzy logic evaluation function is used to convert the weighted stability score into a stability rating. Fuzzy logic evaluation uses membership functions to map continuous score values ​​to discrete levels:

[0048]

[0049] Among them, LS_A(temp) is the membership of the element temp to the set Az, and Yms and Ymx are threshold parameters. By setting membership functions of different levels, the weighted stability score is converted into a stability level (such as "excellent", "good", "general", "needs improvement", etc.), and a pipeline steady-state fluid model is generated, which comprehensively describes the stability state and performance of the system. A multi-dimensional stability evaluation algorithm is applied to conduct a comprehensive stability evaluation of the pipeline steady-state fluid model. The evaluation algorithm comprehensively considers the static stability, dynamic stability and robustness of the system, and through mathematical models and simulation tests, quantifies the stability performance of the system under different conditions, and finally obtains a stability score. This score is a value between 0 and 100, which represents the comprehensive level of system stability.

[0050] Preferably, the evidence-based optimization module may specifically include the following steps:

[0051] (1) constructing a gas transmission safety evaluation standard based on the stability score, performing reliability rating on the pipeline system, and obtaining a pipeline system reliability grade;

[0052] (2) combining patient sensitivity indicators and clinical treatment accuracy requirements, the reliability level of the pipeline system is individually adjusted to obtain a patient-specific safety rating;

[0053] (3) designing graded warning thresholds based on the patient-specific safety ratings, building a multi-level safety monitoring mechanism, and generating dynamic stable state signals;

[0054] (4) compiling a real-time monitoring guide based on the dynamic stability state signal, including the system stability state, potential risk points, and optimization suggestions;

[0055] (5) Collect clinical physicians’ feedback on the performance of the circuit system and patient treatment outcome data to form effectiveness data;

[0056] (6) performing quantitative analysis of the effects based on the performance data, identifying optimization directions, and obtaining intelligent iteration parameters;

[0057] (7) Applying the intelligent iterative parameters to optimize the gas fluctuation characteristic spectrum, adaptive tuning matrix and real-time monitoring guide, the continuous evolution of the stable control system is achieved.

[0058] Specifically, based on the previously obtained stability scores, a gas delivery safety assessment standard was constructed. This standard maps the stability score to a safety level, taking into account clinical risk factors and a safety margin: SafetyLevel = fsh(Score, Risk, Margin); where SafetyLevel is the safety level, Score is the stability score, Risk is the clinical risk factor, and Margin is the safety margin. Based on this assessment standard, the pipeline system reliability is rated, categorizing the system into four reliability levels: Level 1 (highly reliable), Level 2 (relatively reliable), Level 3 (basicly reliable), and Level 4 (requires monitoring). The pipeline system reliability level is individually adjusted based on patient sensitivity indicators and clinical treatment precision requirements. Patient sensitivity indicators include: respiratory function status (such as the degree of lung function impairment and airway responsiveness); disease severity (such as acute respiratory distress syndrome and chronic obstructive pulmonary disease); age characteristics (such as special populations such as newborns and the elderly); and clinical treatment precision requirements determined by the treatment plan, such as anesthesia depth control and precision oxygen therapy. By considering these factors, the system reliability level is adjusted to obtain a patient-specific safety rating. A hierarchical warning threshold is designed based on the patient-specific safety rating. The warning threshold is the critical value that triggers a system warning. Different safety levels correspond to different warning thresholds: T_lev = hc(S_p,lev); where T_lev is the threshold for the lev-level warning, S_p is the patient-specific safety rating, and lev is the warning level (e.g., Level 1, Level 2, or Level 3). Based on these warning thresholds, a multi-level safety monitoring mechanism is constructed, consisting of parameter monitoring, trend monitoring, and pattern monitoring. Parameter monitoring detects in real time whether key parameters exceed safety limits; trend monitoring analyzes parameter trends and predicts potential risks; and pattern monitoring identifies abnormal system behavior patterns, such as unusual fluctuations or periodic disturbances. Through these three levels of monitoring, dynamic stability signals are generated, each of which reflects the stability of a specific aspect of the system. A real-time monitoring guide is compiled based on the dynamic stability signals. The monitoring guide consists of three main sections: system stability: displays the current system stability indicators and safety level; potential risk points: identifies areas and parameters in the system that may pose risks; and optimization suggestions: provides specific recommendations and measures to improve system stability. A micro-disturbance control effectiveness evaluation scale is designed to comprehensively assess the system's control effectiveness. The evaluation scale consists of four dimensions: Stability Assessment, which assesses the system's ability to suppress disturbances and maintain a stable state; Accuracy Assessment, which assesses the system's ability to achieve precise gas delivery; Clinical Applicability Assessment, which assesses the system's adaptability to different clinical scenarios; and Patient Comfort Assessment, which assesses the system's impact on patient comfort. Each dimension consists of multiple evaluation items and is scored on a 5-point scale. The scale tracks the degree to which the actual gas composition delivery stability matches the expected stability target.By monitoring the actual delivery of gas components (such as oxygen concentration, anesthetic gas concentration, etc.) in real time and comparing it with the set target value, the stability deviation is calculated:

[0059] Among them, ΔLes is the stability deviation, tru is the actual observation value, tar is the target value, and all is the number of observation points. By calculating the stability deviation of different parameters, the precise control data is obtained to reflect the precise control capability of the system. Collect application data of different clinical scenarios and analyze the stability effect of the system in different scenarios. Common clinical scenarios include: anesthetic gas delivery: stable delivery of gases used for surgical anesthesia (such as sevoflurane, isoflurane, etc.); precise oxygen therapy: precise oxygen concentration control for different oxygen therapy requirements; special therapeutic gas delivery: such as nitric oxide, iodine heptafluoride and other special gases. By analyzing the performance of the system in these scenarios, evaluating its adaptability and stability, and obtaining scenario adaptability evaluation data. Integrate precise control data and scenario adaptability evaluation data to construct a comprehensive performance evaluation index. The comprehensive index adopts a weighted summation method:

[0060] Eta = ∑con × cw + ∑sence × sw; where Eta is a comprehensive performance evaluation metric, and cw and sw are weighting coefficients for the precision control data and scenario adaptability assessment data, respectively. By calculating these comprehensive metrics, performance data is generated, comprehensively reflecting the system's performance and effectiveness. Based on this performance data, quantitative performance analysis is conducted to identify the system's strengths and weaknesses. Quantitative analysis includes descriptive statistics and comparative analysis. Descriptive statistics calculates performance statistics such as the mean, standard deviation, maximum, and minimum values ​​of the performance data; comparative analysis compares the performance data of the current system with historical data or data from competing systems. Through these analyses, optimization areas for the system are identified, such as improving stability in specific scenarios or reducing the fluctuation of certain parameters. Intelligent iteration parameters are then derived. The intelligent iteration parameters are then applied to optimize the gas fluctuation profile, adaptive tuning matrix, and real-time monitoring guide. The optimization process employs an iterative update strategy: Based on the intelligent iteration parameters, the parameters and thresholds in the gas fluctuation profile are adjusted; the weights and strategy mappings in the adaptive tuning matrix are updated; and the performance of the optimized system and the accuracy of the monitoring guide are verified. If performance and accuracy improve, the update is retained; otherwise, it is rolled back. Through this continuous optimization process, the stable control system can be continuously evolved, the system's performance and adaptability can be improved, and safe, stable and precise respiratory support can be provided to patients.

[0061] This embodiment comprehensively captures and accurately models tiny perturbations in the ventilator pipeline system by constructing a holographic model of pipeline micro-perturbations. By integrating and analyzing fluid acoustic signature data and gas micro-dynamic waveform data, it accurately identifies pipeline resonance characteristics and perturbation propagation paths, improving the sensitivity and accuracy of perturbation perception. Through dynamic analysis in the resonance domain and quantum-level fluctuation detection, multi-dimensional feature extraction and hazard pattern recognition of gas perturbations are achieved, enhancing the comprehensiveness and accuracy of risk assessment. An adaptive tuning matrix and intelligent intervention strategy set are employed to dynamically control pipeline system resonance suppression. Multi-dimensional stability assessment comprehensively evaluates the system's stability performance under various operating conditions. Gas delivery safety assessment and reliability grading are used to further explore the clinical significance of pipeline system stability and patient safety. Dynamic stability status reporting and real-time monitoring guidance enhance the visualization and operability of system operation. Clinical validation and performance data feedback mechanisms enable the system to self-optimize and continuously evolve based on clinical application results. Through refined disturbance modeling based on frequency domain hierarchical analysis and resonance feature extraction, stable control of gas flow at the submicroscopic level is achieved. By using deep neural network fluctuation identifier and chaos theory analysis, the system's ability to recognize complex disturbance patterns is improved.

[0062] Example 2;

[0063] See also Figure 2 As shown, for the parts not described in detail in this embodiment, please refer to the description of Example 1. The method for dynamic stabilization of a ventilator circuit based on mechanical intelligence includes:

[0064] S1. Collecting ventilator pipeline fluid acoustic characteristic data, gas microdynamic waveform data, and patient respiratory coupling data; constructing a pipeline resonance map based on the fluid acoustic characteristic data, performing resonance characteristic analysis on the pipeline resonance map, and obtaining resonance characteristic quantization parameters; constructing a fluid disturbance propagation network based on the gas microdynamic waveform data and the patient respiratory coupling data, performing propagation path analysis on the fluid disturbance propagation network, and obtaining disturbance amplification sensitive point data; constructing a pipeline microdisturbance holographic model based on the resonance characteristic quantization parameters and the disturbance amplification sensitive point data;

[0065] S2. Performing a resonance domain dynamic analysis based on the pipeline micro-perturbation holographic model to obtain a resonance domain spatiotemporal distribution map; performing quantum-level fluctuation detection on the airflow parameters in the pipeline micro-perturbation holographic model to obtain a micro-perturbation amplification curve; performing a gas fluctuation analysis based on the resonance domain spatiotemporal distribution map and the micro-perturbation amplification curve to obtain a gas fluctuation characteristic spectrum;

[0066] S3. constructing an adaptive tuning matrix based on the gas fluctuation characteristic spectrum to obtain an intelligent intervention strategy set; applying the intelligent intervention strategy set to the pipeline system to suppress resonance to obtain a pipeline steady-state fluid model; and performing a multidimensional stability assessment on the pipeline steady-state fluid model to obtain a stability score.

[0067] S4. Assess the safety of gas delivery based on the stability score to obtain a pipeline system reliability level; compile monitoring based on the pipeline system reliability level to obtain a real-time monitoring guide; conduct clinical verification and collection of the application effect of the pipeline system to obtain performance data; optimize the gas fluctuation characteristic spectrum, the adaptive tuning matrix and the real-time monitoring guide based on the performance data to achieve continuous evolution of dynamic stability control of the ventilator pipeline.

[0068] Example 3;

[0069] This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the mechanical intelligence-based ventilator pipeline dynamic stabilization method provided above is implemented.

[0070] Since the electronic device introduced in this embodiment is an electronic device used to implement the method for dynamic stabilization of a ventilator circuit based on mechanical intelligence in the embodiment of this application, those skilled in the art will be able to understand the specific implementation of the electronic device of this embodiment and its various variations based on the method for dynamic stabilization of a ventilator circuit based on mechanical intelligence introduced in the embodiment of this application, so how the electronic device implements the method in the embodiment of this application will not be described in detail here. As long as those skilled in the art implement the electronic device used in the method for dynamic stabilization of a ventilator circuit based on mechanical intelligence in the embodiment of this application, it falls within the scope of protection to be provided by this application.

[0071] The above formulas are all dimensionless and numerical calculations. 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 according to actual conditions.

[0072] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for users of ordinary skill in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A dynamic stabilization system for ventilator circuits based on mechanical intelligence, characterized by: include: Perturbation modeling module: collects ventilator circuit fluid acoustic characteristic data, gas microdynamic waveform data and patient respiratory coupling data; constructing a pipeline resonance map based on the fluid acoustic characteristic data, performing resonance characteristic analysis on the pipeline resonance map, and obtaining a resonance characteristic quantitative parameter; A fluid disturbance propagation network is constructed based on the gas microdynamic waveform data and the patient's respiratory coupling data, and a propagation path analysis is performed on the fluid disturbance propagation network to obtain disturbance amplification sensitive point data; a pipeline microdisturbance holographic model is constructed based on the resonance characteristic quantization parameter and the disturbance amplification sensitive point data; Characteristic analysis module: performs resonance domain dynamic analysis based on the pipeline micro-disturbance holographic model to obtain a resonance domain spatiotemporal distribution map; performs quantum-level fluctuation detection on the airflow parameters in the pipeline micro-disturbance holographic model to obtain a micro-disturbance amplification curve; performs gas fluctuation analysis based on the resonance domain spatiotemporal distribution map and the micro-disturbance amplification curve to obtain a gas fluctuation characteristic spectrum; Steady-state control module: constructing an adaptive tuning matrix based on the gas fluctuation characteristic spectrum to obtain an intelligent intervention strategy set; Applying the intelligent intervention strategy set to the pipeline system to suppress resonance and obtain a pipeline steady-state fluid model; performing a multi-dimensional stability evaluation on the pipeline steady-state fluid model to obtain a stability score; Evidence-based optimization module: Assess the safety of gas delivery based on the stability score to obtain the pipeline system reliability level; compile monitoring based on the pipeline system reliability level to obtain real-time monitoring guidelines; conduct clinical verification and collection of the application effect of the pipeline system to obtain performance data; optimize the gas fluctuation characteristic spectrum, the adaptive tuning matrix and the real-time monitoring guidelines based on the performance data to achieve continuous evolution of dynamic stability control of the ventilator pipeline.

2. The mechanical intelligence-based ventilator circuit dynamic stabilization system according to claim 1, characterized in that: The fluid acoustic characteristic data includes: pressure fluctuation data in the pipeline, acoustic resonance spectrum data and fluid shear stress data; the gas micro-dynamic waveform data includes: gas component concentration change data, microparticle distribution data and turbulent pulsation data; the patient respiratory coupling data includes: patient respiratory rate data, airway resistance data and lung compliance data.

3. The mechanical intelligence-based ventilator circuit dynamic stabilization system according to claim 1, characterized in that: The method for constructing the pipeline micro-perturbation holographic model includes: Performing frequency domain hierarchical analysis on the fluid acoustic characteristic data to obtain a spectrum hierarchy set; extracting resonance points from the spectrum hierarchy set to obtain a resonance node sequence; and constructing a pipeline resonance mapping diagram based on the resonance node sequence; Using a resonance feature extraction algorithm to extract features from the pipeline resonance map to obtain resonance feature quantization parameters; performing spatiotemporal correlation analysis on the gas microdynamic waveform data and the patient respiratory coupling data to obtain an initial fluid-patient interaction network; calculating a complex system entropy value on the initial fluid-patient interaction network to obtain a system complexity assessment; and constructing a fluid disturbance propagation network based on the initial fluid-patient interaction network and the system complexity assessment; Applying a topological analysis algorithm to discover key nodes in the fluid disturbance propagation network to obtain a disturbance propagation hub; quantifying sensitivity based on the disturbance propagation hub to obtain disturbance amplification sensitive point data; Constructing a micro-disturbance ontology framework, mapping the resonance characteristic quantization parameter and the disturbance amplification sensitive point data to the micro-disturbance ontology framework to form an initial disturbance model; Multi-scale fusion technology is used to add spatial and temporal attributes to the initial disturbance model to obtain a spatiotemporal disturbance model; micro-disturbance behavior reasoning is performed on the spatiotemporal disturbance model to obtain a pipeline micro-disturbance holographic model.

4. The mechanical intelligence-based ventilator circuit dynamic stabilization system according to claim 3, characterized in that: The frequency domain hierarchical analysis of the fluid acoustic characteristic data is performed to obtain a spectrum hierarchy set, including: The frequency domain window of the fluid acoustic characteristic data is divided according to the four frequency scales of ultra-micro frequency band, low frequency band, medium frequency band and high frequency band to obtain a multi-level frequency domain window set; Performing wavelet transform and Fourier analysis on the acoustic data in the multi-level frequency domain window set to obtain standardized spectrum data; Performing energy density calculation and peak identification on the standardized spectrum data to obtain an energy distribution spectrum; The standardized spectrum data and the energy distribution spectrum are grouped according to frequency levels to form a spectrum level set.

5. The mechanical intelligence-based ventilator circuit dynamic stabilization system according to claim 1, characterized in that: The resonance domain dynamic analysis is performed based on the pipeline micro-disturbance holographic model to obtain a resonance domain spatiotemporal distribution map; the airflow parameters in the pipeline micro-disturbance holographic model are subjected to quantum level fluctuation detection to obtain a micro-disturbance amplification curve; Performing gas fluctuation analysis based on the resonance domain spatiotemporal distribution map and the perturbation amplification curve to obtain a gas fluctuation characteristic spectrum includes: Obtain ventilation mode parameters, flow setting parameters, and pressure limit parameters from the ventilator operation database to construct an operating parameter baseline sequence; Based on the operating parameter baseline sequence and the pipeline micro-perturbation holographic model, a resonance prediction dynamics model is constructed to obtain a resonance domain spatiotemporal distribution map; Extracting the microscopic parameter sequence of the airflow from the pipeline micro-disturbance holographic model, constructing a quantum-level disturbance measurement network, and obtaining the original microscopic disturbance data; Performing nonlinear dynamic analysis and bifurcation theory on the raw micro-disturbance data to identify the critical perturbation amplification point and obtain a micro-disturbance amplification curve; Performing multi-dimensional cross-mapping of the resonance domain spatiotemporal distribution map and the perturbation amplification curve, calculating the resonance-perturbation cooperative index, and obtaining the interaction strength spectrum; Based on the interaction intensity spectrum, a deep neural network fluctuation identifier is constructed to identify unstable patterns at clinical risk levels and obtain a gas delivery analysis report; Feature extraction and clinical risk grading are performed on the gas delivery analysis report to identify typical fluctuation hazard patterns and obtain a gas fluctuation characteristic spectrum.

6. The mechanical intelligence-based dynamic stabilization system for ventilator circuits according to claim 5, characterized in that: The feature extraction and clinical risk classification of the gas delivery analysis report, identification of typical fluctuation hazard patterns, and acquisition of a gas fluctuation characteristic spectrum include: Applying chaos theory analysis to the gas transport analysis report to identify deterministic chaotic attractors and obtain an unstable structure map; Establish a gas fluctuation hazard classification system, including oxygen concentration pulsation type, anesthesia depth fluctuation type, pressure spike propagation type, and humidity inhomogeneity type; Based on a clinical medical knowledge base and a historical case database, marking a clinical hazard level for the unstable structure map to obtain hazard rating data; Calculate the clinical impact parameters of different fluctuation types, including fluctuation amplitude, frequency characteristics, and duration, to obtain fluctuation clinical risk indicators; A patient safety impact prediction model is constructed based on the fluctuation clinical risk index to form a gas fluctuation characteristic spectrum.

7. The mechanical intelligence-based ventilator circuit dynamic stabilization system according to claim 1, characterized in that: The adaptive tuning matrix is ​​constructed according to the gas fluctuation characteristic spectrum to obtain an intelligent intervention strategy set; the intelligent intervention strategy set is applied to the pipeline system to suppress resonance to obtain a pipeline steady-state fluid model; A multi-dimensional stability evaluation is performed on the pipeline steady-state fluid model to obtain a stability score, including: Based on the gas fluctuation characteristic spectrum, a micro-disturbance suppression strategy generator is constructed, an optimal intervention plan is calculated, and an intervention strategy library is obtained; constructing an adaptive tuning matrix using a reinforcement learning algorithm to transform the intervention strategy library into an intelligent intervention strategy set; Extracting key disturbance parameters from the pipeline micro-disturbance holographic model, applying the intelligent intervention strategy set to perform real-time intervention, and obtaining corrected fluid dynamics parameters; recalculating key stability indicators of the pipeline system based on the corrected fluid dynamics parameters to obtain a steady-state fluid parameter set; Combining medical safety standards and individual physiological needs of patients, the steady-state fluid parameter set is clinically validated to obtain validated stable parameters; Building a pipeline steady-state fluid model based on the verified stable parameters to display the real-time stable state of the system; A multi-dimensional stability evaluation algorithm is applied to perform an all-round stability evaluation on the pipeline steady-state fluid model to obtain a stability score.

8. The mechanical intelligence-based ventilator circuit dynamic stabilization system according to claim 7, characterized in that: The step of constructing a pipeline steady-state fluid model based on the verified stable parameters includes: Construct a multi-dimensional fluid stability evaluation system, covering the dimensions of pressure stability, composition uniformity, flow rate smoothness, and patient adaptability; Standardizing the verified stability parameters and mapping them to a multidimensional fluid stability evaluation system to obtain a standardized stability index set; The weights of each dimension and indicator are determined by combining the analytic hierarchy process and the entropy weight method, and a comprehensive scoring model is constructed to obtain a weighted stable score. The weighted stability score is converted into a stability grade by applying a fuzzy logic evaluation function to generate a pipeline steady-state fluid model.

9. The mechanical intelligence-based ventilator circuit dynamic stabilization system according to claim 1, characterized in that: The gas delivery safety is assessed based on the stability score to obtain a pipeline system reliability level; monitoring is compiled based on the pipeline system reliability level to obtain a real-time monitoring guide; clinical verification and collection of application effects of the pipeline system are performed to obtain performance data; and based on the performance data, the gas fluctuation characteristic spectrum, the adaptive tuning matrix, and the real-time monitoring guide are optimized to achieve continuous evolution of dynamic stability control of the ventilator pipeline, including: A gas transmission safety evaluation standard is established based on the stability score, and a reliability rating of the pipeline system is performed to obtain a pipeline system reliability grade; Combining patient sensitivity indicators and clinical treatment accuracy requirements, the reliability level of the pipeline system is individually adjusted to obtain a patient-specific safety rating; Designing graded warning thresholds based on the patient-specific safety ratings, building a multi-level safety monitoring mechanism, and generating dynamic stable state signals; Compile a real-time monitoring guide based on the dynamic stability state signal, including system stability state, potential risk points and optimization suggestions; Collect clinicians' feedback on the performance of the tubing system and patient treatment effect data to form effectiveness data; Perform quantitative analysis of the effects based on the performance data, identify optimization directions, and obtain intelligent iteration parameters; The intelligent iterative parameters are applied to optimize the gas fluctuation characteristic spectrum, the adaptive tuning matrix and the real-time monitoring guide to achieve continuous evolution of the stable control system.

10. The mechanical intelligence-based dynamic stabilization system for ventilator circuits according to claim 9, characterized in that: The collection of clinician evaluation feedback on the performance of the tubing system and patient treatment effect data to form effectiveness data includes: Design a micro-disturbance control effect evaluation scale, including stability evaluation, accuracy evaluation, clinical applicability evaluation and patient comfort evaluation; Track the consistency between the actual gas delivery stability and the expected stability target, calculate the stability deviation, and obtain accurate control data; Collect application data from different clinical scenarios, analyze the system's stability in anesthetic gas delivery, precision oxygen therapy, and special therapeutic gas delivery, and obtain scenario adaptability evaluation data; Integrate the precise control degree data and the scenario adaptability evaluation data to construct a comprehensive performance evaluation index to form performance data.

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