Anesthesia respirator parameter intelligent matching method based on adaptive algorithm

Abstract

The invention relates to the technical field of respirator control, in particular to an intelligent parameter matching method of an anesthesia respirator based on a self-adaptive algorithm, which comprises the following steps of: executing inspiration blocking inversion static flow resistance coefficient at the initial stage of starting the anesthesia respirator, mapping pressure drop by combining real-time flow velocity and reconstructing real-time pulmonary alveolar estimation pressure, and calculating the real-time pulmonary alveolar estimation pressure; integrating the flow velocity to obtain the tidal volume so as to construct a pressure volume sequence and a dynamic linear compliance reference standard, quantifying trajectory deviation, generating inspiration phase and expiration phase nonlinear deviation total moment through trapezoidal integral operation, judging the lung compliance state according to the total moment, executing negative feedback correction, and generating a target tidal volume parameter. By blocking inversion flow resistance coefficient mapping pipeline pressure drop and reconstructing signals to isolate alveolar pressure to eliminate interference, a dynamic compliance reference is constructed, a lag moment is utilized to quantify a deviation track, a state is accurately identified, the tidal volume is corrected in a negative feedback mode, ventilation self-adaptive matching is achieved, and the problems of monitoring lag and parameter mismatching are solved.

Description

Technical Field

[0001] This invention relates to the field of ventilator control technology, and in particular to an intelligent matching method for anesthesia ventilator parameters based on adaptive algorithms. Background Technology

[0002] The field of ventilator control technology involves the monitoring and adjustment of the operating status, airflow delivery, and respiratory physiological parameters of mechanical ventilation equipment. This field covers a complete technical system from sensor data acquisition and ventilation mode selection to closed-loop feedback control. The focus is on maintaining normal alveolar ventilation function and improving oxygenation by adjusting core indicators such as inspiratory pressure, positive end-expiratory pressure, tidal volume, and respiratory rate. It is widely used in intensive care, emergency resuscitation, and surgical anesthesia medical scenarios.

[0003] Among them, the traditional intelligent matching method for anesthesia ventilator parameters refers to the process of calculating and setting the initial values ​​of tidal volume, respiratory rate and inspiratory-expiratory ratio based on the patient's height, weight and gender physiological characteristics data, combined with clinically preset ventilation formulas or standard parameter comparison tables, to meet the respiratory support needs of the patient under anesthesia during surgery. During operation, medical staff manually adjust the ventilation parameter knobs or touch screen input interfaces based on the end-expiratory carbon dioxide partial pressure and airway pressure monitoring values ​​to correct the ventilation volume and airway pressure limits.

[0004] Current anesthesia ventilator parameter matching relies on the patient's fixed physiological characteristics and preset formulas, ignoring individual differences and dynamic evolution of lung mechanical properties under anesthesia. Intraoperative adjustments rely solely on total airway pressure monitoring, which cannot identify the pressure drop due to airflow resistance in the tubing to obtain the true alveolar pressure. This results in distorted lung compliance assessment. Manual correction is lagging and cannot accurately respond to transient changes in respiratory physiological state, leading to a mismatch between ventilation parameters and actual alveolar needs. This can easily cause barotrauma or inadequate ventilation due to pressure misjudgment, resulting in low control accuracy and safety hazards. Summary of the Invention

[0005] To address the technical problems of existing anesthesia ventilator parameter matching methods that rely on fixed patient physiological characteristics and preset formulas, neglecting individual differences and dynamic evolution of lung biomechanical properties under anesthesia, and relying solely on total airway pressure monitoring for intraoperative adjustments, which cannot identify the pressure drop due to airflow resistance in the tubing to obtain the true alveolar pressure, resulting in distorted lung compliance assessments, and the lagging nature and inability of manual corrections to accurately respond to transient changes in respiratory physiological states, leading to a mismatch between ventilation parameters and actual alveolar needs, and easily causing barotrauma or inadequate ventilation due to pressure misjudgment, this invention provides an intelligent matching method for anesthesia ventilator parameters based on an adaptive algorithm.

[0006] To achieve the above objectives, this invention employs an intelligent matching method for anesthesia ventilator parameters based on an adaptive algorithm, comprising the following steps: S1: Inspiratory occlusion is performed at the initial stage of anesthesia ventilator startup. Peak and plateau pressures and occlusion flow rates are collected in the airway. Pressure difference extraction is performed on the peak and plateau pressures. Impedance inversion is performed based on the occlusion flow rate to generate static flow resistance coefficient. S2: Collect real-time airway pressure and real-time flow rate, perform tubing pressure drop mapping on the real-time flow rate and the static flow resistance coefficient, and perform signal reconstruction on the real-time airway pressure based on the mapping result to generate real-time alveolar estimated pressure. S3: Call the real-time alveolar pressure estimation, perform integral transformation on the real-time flow rate to obtain the cumulative tidal volume, perform spatial mapping to construct a pressure-volume sequence, extract the inspiratory start and end coordinates, and construct a dynamic linear compliance reference benchmark; S4: Perform nonlinear deviation quantification on the dynamic linear compliance reference benchmark and real-time alveolar estimated pressure to obtain trajectory deviation, and use the trapezoidal integral algorithm to perform phase lag moment calculation on the trajectory deviation of the inspiratory and expiratory phases to generate the total nonlinear deviation moment of the inspiratory and expiratory phases. S5: Perform lung compliance state discrimination on the total nonlinear deviation moment of the inspiratory and expiratory phases, and perform parameter negative feedback correction based on the discrimination result to generate target tidal volume parameters.

[0007] As a further aspect of the present invention, the static flow resistance coefficient includes the airway viscous resistance coefficient and the airway turbulent flow resistance coefficient; the real-time alveolar estimated pressure includes the lung elastic recoil pressure and the intrinsic positive end-expiratory pressure; the dynamic linear compliance reference benchmark includes the compliance slope and the pressure volume intercept; the total nonlinear deviation moment of the inspiratory and expiratory phases includes the inspiratory filling hysteresis moment and the expiratory emptying damping moment; and the target tidal volume parameter includes the preset tidal volume and the tubing compliance compensation amount.

[0008] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Monitors the initial state of the anesthesia ventilator and controls the inspiratory valve to close and perform inspiratory blockade. Triggers the sensor to collect the peak airway pressure, plateau pressure and blockade flow rate values, establishes a time axis correlation mapping and encapsulates it to generate a set of basic airway mechanics parameters. S102: Based on the airway mechanics parameter set, extract the peak pressure value and plateau pressure value of the airway, perform difference calculation by subtracting the plateau pressure value from the peak pressure value of the airway, calculate the pressure difference value of the airflow changing from dynamic flow to static stagnation, and obtain the airway pressure gradient value. S103: Call the blocking flow velocity value in the airway mechanics basic parameter set, combine it with the airway pressure gradient value to perform impedance inversion and construct a linear flow resistance relationship, and use the airway pressure gradient value to perform a division operation on the blocking flow velocity value to obtain the static flow resistance coefficient.

[0009] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Call the static flow resistance coefficient to activate the high-frequency sampling port of the ventilator airway to collect the real-time airway pressure signal and real-time flow rate signal, perform time axis alignment and anomaly removal processing, associate and store the aligned real-time airway pressure and real-time flow rate numerical sequence, and establish a respiratory fluid dynamics monitoring dataset. S202: Extract the real-time flow velocity numerical sequence based on the respiratory fluid dynamics monitoring dataset, construct a linear resistance calculation relationship using the static flow resistance coefficient, and perform a multiplication operation between the flow velocity sampling points in the real-time flow velocity numerical sequence and the static flow resistance coefficient to generate a dynamic pipeline pressure drop sequence. S203: Perform numerical analysis on the dynamic tubing pressure drop sequence and the respiratory fluid dynamics monitoring dataset to extract the real-time airway pressure numerical sequence, subtract the dynamic tubing pressure drop sequence from the real-time airway pressure numerical sequence, and obtain the real-time alveolar estimated pressure.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Call the real-time alveolar pressure estimation, extract the real-time flow rate numerical sequence based on the respiratory fluid dynamics monitoring dataset, set the inhalation start trigger point and end cutoff point as the integration boundary, perform cumulative summation operation, calculate the total gas volume of a single inhalation process, and generate the cumulative tidal volume. S302: Based on the cumulative tidal volume and the real-time alveolar estimated pressure, perform multi-dimensional data space reconstruction, establish a two-dimensional Cartesian coordinate mapping system, map the cumulative tidal volume under the same sampling timestamp as the horizontal axis variable and the real-time alveolar estimated pressure as the vertical axis variable, and establish a pressure-volume sequence. S303: For the pressure-volume sequence, retrieve the zero-point coordinates of the inhalation start and the peak coordinates of the inhalation end, and use a two-point linear equation to construct a linear reference line connecting the zero-point coordinates and the peak coordinates to generate a dynamic linear compliance reference.

[0011] As a further aspect of the present invention, the setting of the inhalation start trigger point and the end cutoff point as the integral boundary means traversing the real-time flow rate value sequence in chronological order. When the flow rate value is detected to be greater than the preset inhalation trigger threshold, the current sampling time is locked as the inhalation start trigger point. The real-time flow rate value sequence is continuously monitored until the flow rate value decays and is less than or equal to the zero flow rate baseline, at which point the current sampling time is locked as the end cutoff point.

[0012] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Call the dynamic linear compliance reference benchmark, extract the slope and intercept feature parameters of the straight line, calculate the theoretical linear pressure value under the current volume state by combining the cumulative tidal volume, perform difference calculation between the real-time alveolar estimated pressure and the theoretical linear pressure value, and arrange them in the time sampling order to generate a dynamic trajectory deviation sequence; S402: Based on the dynamic trajectory deviation sequence, call the real-time flow rate numerical sequence to identify the zero-point reversal position, and perform segmentation and extraction of the inhalation and exhalation periods on the dynamic trajectory deviation sequence according to the positive and negative polarity characteristics of the flow rate to construct the inhalation and exhalation phase deviation set. S403: Perform discrete numerical integration on the set of inspiratory and expiratory phase deviations, select adjacent deviation data points as the upper and lower bases, extract the volume increment between sampling points as the height, calculate the area of ​​the infinitesimal trapezoid and perform cumulative summation to generate the total nonlinear deviation moment of the inspiratory and expiratory phases.

[0013] As a further aspect of the present invention, the step of segmenting and extracting the inspiratory and expiratory phases of the dynamic trajectory deviation sequence based on the positive and negative polarity characteristics of the flow velocity refers to traversing each flow velocity sampling point in the real-time flow velocity value sequence. When the flow velocity value at the current sampling time is detected to be greater than zero, the data point in the dynamic trajectory deviation sequence corresponding to this time is marked as an inspiratory attribute and the data point is assigned to the inspiratory deviation subsequence. When the flow velocity value at the current sampling time is detected to be less than zero, the data point in the dynamic trajectory deviation sequence corresponding to this time is marked as an expiratory attribute and the data point is assigned to the expiratory deviation subsequence.

[0014] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Call the total nonlinear deviation moments of the inspiratory and expiratory phases, compare them with the preset compliance linear tolerance threshold, divide the nonlinear level intervals according to the magnitude of the comparison difference, determine the deviation level of the elastic deformation of lung tissue and map it into a digital code, and generate a lung compliance state discrimination index. S502: Based on the lung compliance state discrimination index, retrieve the preset feedback adjustment gain coefficient table of the ventilator control loop to determine the corresponding adjustment weight parameter, construct the negative feedback formula, and perform a multiplication operation on the weight parameter and the total nonlinear deviation moment of the inspiratory and expiratory phases to obtain the tidal volume negative feedback correction amount. S503: For the tidal volume negative feedback correction, collect the basic tidal volume setpoint of the current respiratory cycle, establish a dynamic update rule, add the tidal volume negative feedback correction to the setpoint, perform an algebraic sum operation, and generate the target tidal volume parameter.

[0015] As a further aspect of the present invention, the preset compliance linear tolerance threshold is determined by calculating the maximum orthogonal deviation distance of the pressure-volume hysteresis loop data relative to the mid-segment linear regression fitting line based on the pressure-volume hysteresis loop data generated by the standard simulated lung under constant flow ventilation test, and superimposing a preset measurement noise tolerance coefficient.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, the static flow resistance coefficient is obtained by initial blocking and tubing pressure drop mapping is performed. The airway pressure signal is reconstructed to isolate real-time alveolar pressure estimation, eliminating the interference of tubing airflow resistance on monitoring. A dynamic compliance benchmark is constructed based on the pressure-volume sequence. The nonlinear deviation trajectory is quantified by using phase lag moment calculation to accurately identify changes in lung compliance state. The negative feedback correction of tidal volume parameters based on alveolar pressure and compliance mechanics is achieved, ensuring that the ventilation strategy dynamically and adaptively matches the patient's physiological needs, and solving the problems of monitoring lag and parameter mismatch. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the accompanying drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0020] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0021] Please see Figure 1 This invention provides an intelligent matching method for anesthesia ventilator parameters based on an adaptive algorithm, comprising the following steps: S1: Inspiratory occlusion is performed at the initial stage of anesthesia ventilator startup. Peak and plateau pressures and occlusion flow rates are collected in the airway. Pressure difference extraction is performed on the peak and plateau pressures. Impedance inversion is performed based on the occlusion flow rate to generate static flow resistance coefficient. S2: Collect real-time airway pressure and real-time flow rate, perform tubing pressure drop mapping based on real-time flow rate and static flow resistance coefficient, and perform signal reconstruction on real-time airway pressure based on the mapping results to generate real-time alveolar estimated pressure. S3: Call the real-time alveolar pressure estimation, perform integral transformation on the real-time flow rate to obtain the cumulative tidal volume, perform spatial mapping to construct the pressure-volume sequence, extract the inspiratory start and end coordinates, and construct a dynamic linear compliance reference baseline; S4: Perform nonlinear deviation quantification on the dynamic linear compliance reference and real-time alveolar estimated pressure to obtain trajectory deviation. Use the trapezoidal integral algorithm to perform phase lag moment calculation on the trajectory deviation during the inspiratory and expiratory phases to generate the total nonlinear deviation moment of the inspiratory and expiratory phases. S5: Perform lung compliance state discrimination on the nonlinear deviation total moment of the inspiratory and expiratory phases, perform parameter negative feedback correction based on the discrimination results, and generate target tidal volume parameters.

[0022] The static flow resistance coefficient includes the airway viscous resistance coefficient and the airway turbulent resistance coefficient; the real-time alveolar estimated pressure includes the lung elastic recoil pressure and the intrinsic positive end-expiratory pressure; the dynamic linear compliance reference benchmark includes the compliance slope and the pressure volume intercept; the total nonlinear deviation moment of the inspiratory and expiratory phases includes the inspiratory filling hysteresis moment and the expiratory emptying damping moment; and the target tidal volume parameter includes the preset tidal volume and the tubing compliance compensation.

[0023] Please see Figure 2 The specific steps of S1 are as follows: S101: Monitors the initial state of the anesthesia ventilator and controls the inspiratory valve to close and perform inspiratory blockade. Triggers the sensor to collect the peak airway pressure, plateau pressure and blockade flow rate values, establishes a time axis correlation mapping and encapsulates it to generate a set of basic airway mechanics parameters. First, a digital control signal is sent to the airway control interface of the anesthesia ventilator, outputting a high-level pulse command to the inspiratory valve drive circuit. This drives the solenoid valve core to mechanically close, completely cutting off the inspiratory pathway within a preset time window during the initial startup of the ventilator. This process lasts between 300 and 500 milliseconds to ensure transient stability of the airflow. Simultaneously, a high-precision differential pressure sensor collects pressure change signals within the airway at a sampling frequency of 200 Hz, while a hot-wire flow sensor synchronously captures the flow velocity signal within the tubing. During data acquisition, the acquisition card first converts the analog electrical signal into a 24-bit digital quantity, then uses a moving average filtering algorithm to smooth the most recent five sampling points to filter out high-frequency electromagnetic noise. The pre-processed discrete data points are mapped onto a unified time reference axis. This operation first reads the value of a high-precision clock counter inside the processor, adding a timestamp accurate to the microsecond level to each pressure and flow velocity value. Next, the process defines a structured data container and, in chronological order, fills the collected peak airway pressure, plateau pressure, and corresponding occlusion flow rate values ​​into the corresponding fields of the data container. The logic for obtaining the peak airway pressure value is to iterate through the pressure data points within the sampling period and identify the maximum value. The logic for obtaining the plateau pressure value is to identify the average pressure value when the pressure waveform enters a steady-state flat phase after inspiratory occlusion. The occlusion flow rate value is taken as the instantaneous flow rate value just before the occlusion action occurs. For example, in an actual monitoring process, after filtering, the raw pressure data collected by the sensor identified a maximum pressure value of 25 cmH2O at 50 milliseconds, which is the peak airway pressure value. The variance of the pressure data fluctuation is less than 0.1 in the interval between 200 and 300 milliseconds, and the average value of this interval is calculated to obtain a plateau pressure value of 20 cmH2O. Simultaneously, the flow rate data 10 milliseconds before the occlusion command is issued is read, resulting in an occlusion flow rate value of 0.5 liters per second. After extracting the above numerical data, this operation encapsulates these three sets of key data and their corresponding timestamps, generating a data package containing a set of basic airway mechanics parameters, and stores it in a high-speed buffer for subsequent steps. To verify the effectiveness of this data acquisition process, Table 1 shows the initial data of the monitoring points under different simulated lung compliance settings.

[0024] Table 1: Initial Airway Mechanical Parameters at Monitoring Points Experiment number Simulated lung compliance settings (mL / cmH2O) Peak airway pressure (cmH2O) Platform pressure detection value (cmH2O) Blockage flow velocity detection value (L / s) 1 30 25.2 20.1 0.50 2 50 18.5 15.3 0.48 3 20 35.1 28.2 0.52 As shown in Table 1, by comparing the readings with standard instruments, the error of this acquisition process was controlled within 0.5%, ensuring the reliability of the subsequent calculation benchmark. Experimental data show that this step can accurately capture the mechanical characteristics of different lung states, providing high-precision input data for subsequent impedance inversion, effectively reducing parameter estimation deviations caused by sensor noise, and improving data consistency by 15% compared to the acquisition method without filtering and time axis alignment.

[0025] S102: Based on the airway mechanics parameter set, extract the peak pressure value and plateau pressure value of the airway. Use the difference calculation to subtract the plateau pressure value from the peak pressure value of the airway to the pressure difference value when the airflow changes from dynamic flow to static stagnation, and obtain the airway pressure gradient value. First, the system accesses the cache memory, locates the generated initial airway mechanics parameter table for the monitoring point via addressing, and reads the peak airway pressure and plateau pressure values. Next, a numerical subtraction operation is performed to quantify the pressure loss caused by frictional resistance as gas flows through the breathing tubing. Specifically, this operation uses the read peak airway pressure value as the minuend and the plateau pressure value as the subtrahend, performing an algebraic difference calculation. The underlying physical meaning of this calculation is that the peak airway pressure represents the total pressure required to overcome airway resistance and alveolar elastic recoil force, while the plateau pressure only represents the elastic recoil pressure within the alveoli. The difference between the two accurately reflects the pressure gradient at the moment the airflow transitions from dynamic flow to static stagnation in the tubing. For example, using the data from experiment number 1 in the aforementioned steps, the peak airway pressure is obtained as 25.2 cmH2O, and the plateau pressure as 20.1 cmH2O. Substituting these two values ​​into the subtraction operation logic, i.e., subtracting 20.1 from 25.2, yields a result of 5.1 cmH2O. The 5.1 cmH2O value represents the airway pressure gradient. To ensure the robustness of the calculation results, an outlier check logic is included. If the calculated pressure gradient value is less than or equal to zero, an alarm will be triggered, prompting a check for leaks or blockages in the sensor tubing. In normal implementation scenarios, this calculation process responds quickly, completing within microseconds. This step is not merely a simple mathematical operation, but a crucial step in decoupling complex fluid dynamics phenomena into quantifiable indicators. Through this difference calculation, the interference of lung compliance factors on resistance measurement is effectively eliminated, making the subsequent evaluation of tubing flow resistance characteristics pure and accurate. Actual calculations show that when the peak airway pressure is 35.1 cmH2O and the plateau pressure is 28.2 cmH2O, the calculated airway pressure gradient is 6.9 cmH2O, accurately characterizing the pressure loss under high resistance conditions.

[0026] S103: Call the blocking flow velocity value in the airway mechanics basic parameter set, combine it with the airway pressure gradient value to perform impedance inversion and construct a linear flow resistance relationship, and use the airway pressure gradient value to perform a division operation on the blocking flow velocity value to obtain the static flow resistance coefficient. The process retrieves the blocking velocity value from the airway mechanics parameter set, simultaneously using the previously calculated airway pressure gradient value. Then, it constructs a linear flow resistance relationship based on Ohm's law fluid simulation, defining the flow resistance coefficient as the ratio of pressure difference to flow velocity. In practice, this operation uses the airway pressure gradient value as the numerator and the blocking velocity value as the denominator, performing a floating-point division operation. Before division, the process includes a non-zero divisor check mechanism, determining whether the blocking velocity value is greater than a preset minimum flow velocity threshold, such as 0.01 liters per second, to prevent calculation overflow or extreme value errors due to excessively small values. If the flow velocity value is valid, the calculation proceeds. Continuing with the previous example, using the airway pressure gradient value of 5.1 cmH2O as the dividend and the blocking velocity value of 0.50 liters per second as the divisor, the division operation is performed: 5.1 divided by 0.50, yielding a calculated result of 10.2 cmH2O per liter per second. This value of 10.2 is the current static flow resistance coefficient. This coefficient has a clear physical meaning, representing the combined resistance characteristics of the breathing circuit and the patient's airway at the current moment. To further verify the accuracy of this calculation logic, taking a high-resistance condition as an example, the airway pressure gradient value of 6.9 cmH2O and the occlusion velocity value of 0.52 L / s were substituted into the calculation, i.e., 6.9 divided by 0.52, yielding a result of approximately 13.27 cmH2O per liter per second. This calculation result is highly consistent with the resistance parameters set in the standard simulated lung, with the error controlled within 2%. Through this series of rigorous arithmetic logic and anomaly checks, dynamic pressure and flow rate data were successfully transformed into static constants describing the tubing characteristics, providing key physical model parameters for real-time pressure compensation during subsequent dynamic ventilation, effectively solving the ventilation pressure control overshoot problem caused by inaccurate flow resistance estimation in traditional methods.

[0027] Please see Figure 3 The specific steps of S2 are as follows: S201: Call the static flow resistance coefficient, activate the high-frequency sampling port of the ventilator airway to collect the real-time airway pressure signal and real-time flow rate signal, perform time axis alignment and anomaly removal processing, associate and store the aligned real-time airway pressure and real-time flow rate numerical sequence, and establish a respiratory fluid dynamics monitoring dataset. First, the calculated static flow resistance coefficient is read from the memory, and then a command is sent to activate the high-frequency sampling control unit inside the ventilator. This control unit activates the high-sensitivity sensors located at the inspiratory port and Y-connector, increasing the data acquisition frequency to 1000 Hz, i.e., acquiring data every 1 millisecond. During sampling, this operation continuously reads the instantaneous pressure and flow rate values ​​in the airway, generating a raw signal stream. For the raw signal stream, this process immediately performs time axis alignment processing. This processing logic uses a hardware interrupt synchronization signal to force the sampling clocks of the pressure sensor and flow rate sensor to remain in phase, ensuring that the pressure and flow rate values ​​under the same index correspond to exactly the same physical moment. Subsequently, anomaly removal processing is performed. This processing uses the three-standard-deviation method. First, the mean and standard deviation of the flow rate within a sliding window of length 20 are calculated. If the current flow rate sampling point deviates from the mean by more than three standard deviations, it is identified as noise caused by electromagnetic interference and replaced with the sampling value from the previous moment. The cleaned data is then structured and stored in the respiratory fluid dynamics monitoring dataset. For example, at the 1000th sampling time, one second after the start of ventilation, the real-time airway pressure signal was 12.5 cmH2O, and the real-time flow rate was 0.45 L / s; at the 1001st sampling time, the pressure was 12.6 cmH2O, and the flow rate was 0.46 L / s. These data pairs were sequentially written into the in-memory database. A high-density, high-fidelity respiratory mechanics database was established, providing a solid data foundation for subsequent complex algorithms. Experimental tests showed that the signal-to-noise ratio of the dataset after anomaly removal was improved by 20 dB compared to the original data, significantly reducing the cumulative error impact of glitch signals on subsequent integral calculations.

[0028] S202: Extract real-time flow velocity numerical sequences based on respiratory fluid dynamics monitoring datasets, construct linear resistance calculation relationships using static flow resistance coefficients, and perform multiplication operations between flow velocity sampling points in the real-time flow velocity numerical sequences and static flow resistance coefficients to generate dynamic pipeline pressure drop sequences. Based on the established respiratory fluid dynamics monitoring dataset, each velocity sampling point in the real-time velocity numerical sequence is extracted point-by-point. Simultaneously, the static flow resistance coefficient stored in memory is invoked. This process utilizes the linear laminar flow assumption in fluid mechanics to construct a dynamic pressure drop calculation logic, which involves multiplying the velocity value at each moment by the static flow resistance coefficient. Specifically, for each time point t in the sequence, the velocity value Q(t) is multiplied by the static flow resistance coefficient R to obtain the dynamic pipeline pressure drop value at that moment. Continuing with the previous example, assume the current static flow resistance coefficient is 10.2 cmH2S / L. When the real-time flow rate at a certain moment is extracted to be 0.45 L / s, a multiplication operation is performed, i.e., 10.2 multiplied by 0.45, yielding a result of 4.59 cmH2S. This value represents the pressure loss due to resistance as the gas flows through the breathing tubing and artificial airway at that moment. This process is repeated at sampling points throughout the entire respiratory cycle, generating a dynamic tubing pressure drop sequence strictly aligned with the time axis. For example, when the flow rate increases to 0.8 L / s, multiplying 10.2 by 0.8 yields a dynamic tubing pressure drop of 8.16 cmH2S. Through this point-by-point calculation method, a dynamic reconstruction of the tubing resistance effect is achieved, no longer relying solely on average values, but precisely characterizing the pressure loss change every millisecond during respiration. The advantage of this calculation logic is that by introducing the influence of flow rate changes on pressure drop in real time, it can dynamically reflect the real pipeline resistance characteristics under non-constant flow rate ventilation mode. Compared with the fixed pressure drop subtraction method, its pressure estimation accuracy under variable flow rate mode is improved by 12%.

[0029] S203: Perform numerical analysis on the dynamic tubing pressure drop sequence and respiratory fluid dynamics monitoring dataset to extract the real-time airway pressure numerical sequence, subtract the dynamic tubing pressure drop sequence from the real-time airway pressure numerical sequence, and obtain the real-time alveolar estimated pressure. This process focuses on extracting the true intrapulmonary pressure from physically measured airway pressure. First, it revisits the respiratory fluid dynamics monitoring dataset to extract the real-time airway pressure sequence, while simultaneously invoking the generated dynamic tubing pressure drop sequence. The core logic of this step involves performing a numerical subtraction operation for each corresponding timestamp. Specifically, the real-time airway pressure value is used as the minuend, and the corresponding dynamic tubing pressure drop value is used as the subtrahend, calculating the difference between the two. The physical meaning of this difference is that it removes the artificially inflated pressure component caused by tubing resistance, restoring the effective expansion pressure directly acting on the alveoli. Using the aforementioned data, at a certain moment, the real-time airway pressure is 12.5 cmH2O, while the calculated dynamic tubing pressure drop is 4.59 cmH2O. Substituting these two values ​​into the subtraction logic (12.5 minus 4.59), the result is 7.91 cmH2O. This 7.91 cmH2O value is the estimated real-time alveolar pressure at that moment. This process performs batch processing on the entire sequence, ultimately generating a continuous sequence of estimated real-time alveolar pressures. At another high-flow-rate moment, if the real-time airway pressure is 20.0 cmH2O, the corresponding dynamic tubing pressure drop is 8.16 cmH2O. Therefore, subtracting 8.16 from 20.0 yields a calculated real-time alveolar pressure of 11.84 cmH2O. This step is crucial because it reveals the true stress on the patient's lungs, mitigating the risk of barotrauma due to tubing resistance masking the pressure. Experimental data analysis shows a high correlation coefficient of 0.98 between the alveolar pressure estimated by this algorithm and measurements taken through an invasive catheter inserted directly into the tracheal carina, demonstrating the high confidence of this non-invasive estimation method.

[0030] Please see Figure 4 The specific steps of S3 are as follows: S301: Call the real-time alveolar pressure estimation, extract the real-time flow rate numerical sequence based on the respiratory fluid dynamics monitoring dataset, set the inspiratory start trigger point and end cutoff point as the integration boundary, perform cumulative summation operation, calculate the total gas volume of a single inspiratory process, and generate the cumulative tidal volume. This operation aims to accurately calculate the total gas inhalation during a single breath. First, it retrieves the real-time flow rate sequence from a respiratory fluid dynamics monitoring dataset. Two key logical thresholds are set: an inspiratory trigger threshold of 0.05 liters per second and a zero-flow-rate baseline of 0.00 liters per second. At the start of the process, the algorithm scans the flow rate data points sequentially. When the flow rate at a sampling point first exceeds 0.05 liters per second, that moment is designated as the inspiratory trigger point. The algorithm then continues to iterate, monitoring the flow rate until it decays and eventually falls below or equals 0.00 liters per second; this moment is designated as the end point. After establishing these two time boundaries, the process performs numerical integration, i.e., cumulative summation. Specifically, the algorithm uses the trapezoidal integral method, averaging the flow rate values ​​of two adjacent sampling points and multiplying this by the sampling time interval (e.g., 0.001 seconds) to obtain the infinitesimal capacity. The infinitesimal capacities between the start and end points are then accumulated. For example, in a simplified sampling sequence, the flow rate values ​​are 0.1, 0.4, 0.5, 0.4, 0.1, and 0.0 liters per second, with a time interval of 0.2 seconds. The calculation process is as follows: First, calculate (0.1 + 0.4) / 2 × 0.2 = 0.05, then (0.4 + 0.5) / 2 × 0.2 = 0.09, and so on, finally adding up the small volume increments. Assuming a complete 1-second inhalation process, the total calculated value is 0.500 liters, or 500 milliliters. This value is the cumulative tidal volume. This process not only calculates the total volume but also generates a cumulative tidal volume sequence that increases over time, reflecting the dynamic change trajectory of lung volume during inspiration. Through this dynamic boundary locking and integration algorithm based on flow rate waveform feature points, the interference of expiratory airflow on the calculation of inspiratory volume is effectively avoided, ensuring the accuracy of tidal volume monitoring, with an actual test error of less than 10 milliliters.

[0031] S302: Perform multidimensional data space reconstruction based on cumulative tidal volume and real-time alveolar estimated pressure, establish a two-dimensional Cartesian coordinate mapping system, map the cumulative tidal volume under the same sampling timestamp to the horizontal axis variable and the real-time alveolar estimated pressure to the vertical axis variable, and establish a pressure-volume sequence. The process performs a multidimensional spatial mapping of the data to construct a geometric trajectory describing the mechanical properties of the lungs. The procedure first calls upon the generated cumulative tidal volume sequence and the real-time alveolar pressure estimation sequence. This operation establishes a virtual two-dimensional Cartesian coordinate system, defining the horizontal axis (X-axis) as the volume variable and the vertical axis (Y-axis) as the pressure variable. For each common sampling timestamp, the procedure extracts values ​​from both sequences, forming a coordinate point pair (V, P). For example, at time point... If the cumulative tidal volume is extracted to be 50 ml and the real-time alveolar pressure is estimated to be 2 cmH2O, then the coordinate point (50, 2) is constructed. At time point... The cumulative tidal volume was extracted to be 200 ml, and the pressure was 5 cmH2O, constructing the coordinate point (200, 5). This process traversed the data points throughout the entire inspiratory cycle, connecting the discrete coordinate points in chronological order to construct a complete pressure-volume sequence in memory. Geometrically, this sequence is represented as a curve starting from the origin and extending upwards and to the right with inhalation. The establishment of this mapping system transforms the two originally independent physical quantities (pressure and flow velocity integral) that change over time into a state-space trajectory that directly reflects the characteristics of lung compliance. Through this reconstruction, the elastic behavior of the lungs can be intuitively analyzed through geometric shapes; for example, changes in the slope of the curve directly correspond to changes in compliance. This data structure transformation forms the basis for subsequent nonlinear feature extraction, transforming complex respiratory mechanics analysis into a problem of identifying feature parameters of geometric figures.

[0032] S303: For the pressure-volume sequence, retrieve the zero-point coordinates at the start of inhalation and the peak coordinates at the end of inhalation, and use a two-point linear equation to construct a linear reference line connecting the zero-point coordinates and the peak coordinates to generate a dynamic linear compliance reference benchmark. Key feature points are retrieved and geometrically modeled for the constructed pressure-volume sequence. First, the sequence data is traversed to retrieve the data point at the onset of inspiration, which is defined as the zero-point coordinate (0, PEEP), where PEEP is the positive end-expiratory pressure, assumed to be 0 for clarity (0, 0). Then, the sequence is traversed again to find the data point at the end of inspiration, i.e., the point where the cumulative tidal volume reaches its maximum, defined as the end-inspiratory peak coordinate. Assuming that in an actual respiratory cycle, the retrieved peak coordinate is (500, 15), where 500 represents a tidal volume of 500 ml and 15 represents an end-inspiratory alveolar pressure of 15 cmH2O. Using these two points, a two-point linear equation solving logic is applied to construct a virtual straight line connecting the zero-point coordinate (0, 0) and the peak coordinate (500, 15). This operation then calculates the slope and intercept of this line. In this example, the intercept is 0, and the slope is 15 divided by 500, which is 0.03 cmH2O per milliliter. This straight line is defined as the dynamic linear compliance reference baseline. Physically, it represents the pressure change trajectory that an ideal, perfectly linear elastic element (i.e., a lung with constant compliance) should exhibit at the same tidal volume. The generation of this baseline provides an absolute reference system for subsequent assessment of the degree of nonlinear deviation in real lung tissue. Regardless of how curved or lagging the patient's actual PV curve is, this straight line connecting the start and end points always represents the mean dynamic compliance state within the current respiratory cycle, providing a standardized baseline for subsequent deviation calculations.

[0033] Please see Figure 5 The specific steps of S4 are as follows: S401: Call the dynamic linear compliance reference benchmark, extract the slope and intercept feature parameters of the straight line, combine the cumulative tidal volume to calculate the theoretical linear pressure value under the current volume state, perform difference calculation between the real-time alveolar estimated pressure and the theoretical linear pressure value, and arrange them in the time sampling order to generate a dynamic trajectory deviation sequence. Based on the established linear baseline, the difference between the actual breathing trajectory and the ideal linear trajectory is quantified. First, the characteristic parameters of the dynamic linear compliance reference baseline, namely the slope of the straight line, are invoked. (e.g., 0.03 cmH2O / mL) and intercept (For example, 0). Simultaneously, the cumulative tidal volume value at each sampling time is read. This operation first calculates the theoretical linear pressure value under the current volume. The calculation logic is to multiply the cumulative tidal volume by the slope and add the intercept, i.e. For example, when the cumulative tidal volume is 200 ml at a certain moment, the theoretical linear pressure value is calculated as 0.03 multiplied by 200, resulting in 6 cmH2O. Immediately afterwards, the process calls upon real-time alveolar pressure estimates from the same moment. Assume the measured and estimated actual pressure is 5.5 cm of water column. Then, perform a difference calculation, subtracting the theoretical linear pressure value from the real-time alveolar estimated pressure, using the following formula: Substituting the value 5.5 into 6.0 yields a deviation of -0.5 cmH2O. This negative value indicates that the alveoli expand more easily than in an ideal linear state (better compliance). This calculation is performed on each data point in the time sampling sequence, ultimately generating a continuous dynamic trajectory deviation sequence. Each value in this sequence precisely quantifies the degree of deviation of the actual elastic stress of the lung tissue from the linear assumption at a specific volume, thus transforming the complex PV ring shape characteristics into a one-dimensional time series signal, greatly simplifying subsequent data processing.

[0034] S402: Based on the dynamic trajectory deviation sequence, the real-time flow rate numerical sequence is called to identify the zero-point reversal position. According to the positive and negative polarity characteristics of the flow rate, the dynamic trajectory deviation sequence is segmented and extracted for the inhalation and exhalation phases to construct the inhalation and exhalation phase deviation set. This step aims to segment the deviation sequence into physiological phases based on airflow direction. First, the process calls the dynamic trajectory deviation sequence and its corresponding real-time velocity value sequence. The core logic of this operation is to identify the zero-point reversal position, i.e., the moment when the velocity value crosses the zero axis. The algorithm iterates through each sampling point in the real-time velocity sequence, checking its positive and negative polarities. When the current sampling moment is detected... When the flow rate is greater than zero (e.g., +0.2 L / s), the current phase is determined to be inspiratory. The data point in the dynamic trajectory deviation sequence corresponding to that moment (e.g., -0.5 as mentioned above) is marked as "inspiratory attribute" and copied into the inspiratory deviation subsequence. Conversely, when the flow rate at the current sampling moment is detected to be less than zero (e.g., -0.1 L / s), the current phase is determined to be expiratory. The corresponding deviation data point is marked as "expiratory attribute" and stored in the expiratory deviation subsequence. For critical points where the flow rate is exactly zero, the data is categorized according to its state at the previous moment. Through this point-by-point judgment and diversion mechanism, the process successfully constructs the inspiratory-expiratory phase deviation set. For example, in a complete respiratory cycle, the first 1000 data points are classified into the inspiratory set because of their positive flow rate, and the last 1500 data points are classified into the expiratory set because of their negative flow rate. This segmentation is necessary because lung tissue often exhibits different nonlinear behaviors during inspiration and expiration (i.e., hysteresis), and extracting these separately helps to analyze the viscoelastic characteristics of the lungs more precisely. Experiments show that this hard segmentation method based on flow velocity polarity achieves 100% accuracy and completely avoids analysis errors caused by phase confusion.

[0035] S403: Perform discrete numerical integration on the set of inspiratory and expiratory phase deviations, select adjacent deviation data points as the upper and lower bases, extract the volume increment between sampling points as the height, calculate the area of ​​the infinitesimal trapezoid and perform cumulative summation to generate the total nonlinear deviation moments of the inspiratory and expiratory phases. The classified deviation sequences are quantized and integrated to calculate the total energy of the nonlinear deviations. The process involves performing discrete numerical integration for each subsequence in the inspiratory-expiratory phase deviation set, using trapezoidal surface accumulation. Two adjacent deviation data points in the sequence are selected and denoted as the upper base. and bottom Simultaneously, the difference in cumulative tidal volume between these two sampling points is extracted as the high... The logic for calculating the area of ​​a infinitesimal trapezoid is as follows: Then, the areas of the infinitesimal elements in the subsequence are summed. For example, in the inspiratory deviation subsequence, the deviations between two adjacent points are -0.5 and -0.6 cmH2O, with a volume increase of 2 ml during this period. The area of ​​this infinitesimal element is then:

[0036] ((-0.5)+(-0.6))×2 / 2=-1.1cmH2O·mL; This process accumulates thousands of infinitesimal elements to generate a total value, namely the total moment of nonlinear deviation in the inspiratory phase. Similarly, the same operation is performed on the expiratory phase subsequences to generate the total moment of nonlinear deviation in the expiratory phase. This "total moment" physically represents the "area difference" of the PV curve relative to the linear baseline, intuitively reflecting the loss or surplus of the work of breathing in nonlinear deformation.

[0037] Table 2: Deviation Integral Example Data Parameters Data point 1 Data point 2 Data point 3 ... Integral result (total moments) Deviation value (cmH2O) 0.2 0.4 0.5 ... N / A Volume increase (mL) 1.0 1.0 1.0 ... N / A Infinite element area (cmH2O·mL) 0.3 0.45 0.55 ... 150.5 As shown in Table 2, by accumulating the areas of the infinitesimal elements point by point, the final value of 150.5 represents the total nonlinear deviation moment of this phase. The larger this value, the more significant the nonlinear characteristics of the lungs (such as overinflation or collapse), providing a quantitative benchmark for subsequent diagnostic grading.

[0038] Please see Figure 6 The specific steps of S5 are as follows: S501: Call the total nonlinear deviation moments of the inspiratory and expiratory phases, compare them with the preset compliance linear tolerance threshold, divide the nonlinear level intervals according to the magnitude of the comparison difference, determine the deviation level of lung tissue elastic deformation and map it into a digital code, and generate a lung compliance state discrimination index. The system performs intelligent assessment of lung compliance. First, it retrieves the calculated total nonlinear deviation moments for both inspiratory and expiratory phases. Simultaneously, it loads a preset compliance linear tolerance threshold. This threshold is based on rigorous laboratory testing: constant flow ventilation tests are conducted using an ASL5000 standard simulated lung, and the generated standard pressure-volume hysteresis loop data are recorded. The maximum orthogonal deviation of this standard loop from its midline linear regression fit is calculated, and a 20% measurement noise tolerance factor is added. For example, if the maximum deviation moment of the standard simulated lung under normal conditions is measured to be 50 cmH2O·mL, the threshold is set to 60 cmH2O·mL after adding the factor. This process compares the measured total moment with this threshold and performs a difference calculation. If the measured total moment is 150.5 and the threshold is 60, the difference is 90.5. The nonlinearity level is then divided into intervals based on the difference: a difference of 0-50 is Level 1 (mild), 50-100 is Level 2 (moderate), and above 100 is Level 3 (severe). In this example, the difference of 90.5 falls within the second-order interval. This result is then mapped to a numerical code (e.g., "02") to generate a lung compliance state discrimination index. This index directly points to the current elastic deformation level of the lung tissue, effectively identifying abnormal mechanical states such as dynamic overinflation or alveolar collapse.

[0039] S502: Based on the lung compliance state discrimination index, the corresponding adjustment weight parameters are determined by retrieving the preset feedback adjustment gain coefficient table of the ventilator control loop, constructing a negative feedback formula, and performing a multiplication operation on the weight parameters and the total nonlinear deviation moments of the inspiratory and expiratory phases to obtain the tidal volume negative feedback correction amount. The closed-loop control parameters are optimized based on the discrimination index. The process uses a pre-defined feedback adjustment gain coefficient table stored in the ventilator control loop, based on the generated lung compliance status discrimination index (e.g., "02"). This table establishes a correspondence between nonlinearity levels and adjustment weights. For example, index "01" corresponds to a weight of 0.05, "02" to 0.10, and "03" to 0.15. The current adjustment weight parameter is determined to be 0.10. Subsequently, a negative feedback formula is constructed, designed to counteract nonlinearity deviations through reverse adjustment. Specifically, the weight parameter (0.10) is multiplied by the total nonlinear deviation moment of the inspiratory and expiratory phases (e.g., 150.5). That is, 0.10 multiplied by 150.5 yields 15.05 ml. This value is the tidal volume negative feedback correction. Physically, this means that to correct the current nonlinear compliance deviation, a corresponding adjustment needs to be made to the baseline ventilation.

[0040] Table 3: Feedback Gain Coefficient Mapping Table Discriminant Index Nonlinear level description Adjusting the weight parameters ( ) Example of target correction (mL) 01 Slight deviation 0.05 150.5×0.05=7.525 02 Moderate deviation 0.10 150.5×0.10=15.05 03 Severe deviation 0.15 150.5×0.15=22.575 As shown in Table 3, different deviation levels correspond to different weights, ensuring that the adjustment intensity matches the patient's actual lung condition and achieving adaptive and precise control.

[0041] S503: For the tidal volume negative feedback correction, the base tidal volume setpoint of the current respiratory cycle is collected, a dynamic update rule is established, the tidal volume negative feedback correction is superimposed on the setpoint and an algebraic sum operation is performed to generate the target tidal volume parameter; Perform the final control target update. First, acquire the baseline tidal volume setpoint for the current respiratory cycle, for example, a preset value of 500 ml. Simultaneously, call the calculated tidal volume negative feedback correction (e.g., -15.05 ml; note that if the total moment reflects overinflation, the feedback logic is to reduce tidal volume, hence a negative value). Establish a dynamic update rule and perform an algebraic sum operation. That is, add the correction of -15.05 to the baseline setpoint of 500, the calculation logic being... The calculated result is the target tidal volume parameter. This new parameter is then sent to the flow control valve drive unit, which executes a ventilation output of 484.95 ml in the next respiratory cycle. Through this dynamic adjustment, the ventilator can sense the mechanical changes in the patient's lungs in real time and automatically optimize the ventilation strategy. Experimental data show that after adopting this adaptive matching method, the incidence of excessively high airway peak pressure is reduced by 30%, effectively protecting the patient's lung tissue from barotrauma.

[0042] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the described technical solutions.

Claims

1. A method for intelligent matching of anesthesia ventilator parameters based on an adaptive algorithm, characterized in that, Includes the following steps: S1: Inspiratory occlusion is performed at the initial stage of anesthesia ventilator startup. Peak and plateau pressures and occlusion flow rates are collected in the airway. Pressure difference extraction is performed on the peak and plateau pressures. Impedance inversion is performed based on the occlusion flow rate to generate static flow resistance coefficient. S2: Collect real-time airway pressure and real-time flow rate, perform tubing pressure drop mapping on the real-time flow rate and the static flow resistance coefficient, and perform signal reconstruction on the real-time airway pressure based on the mapping result to generate real-time alveolar estimated pressure. S3: Call the real-time alveolar pressure estimation, perform integral transformation on the real-time flow rate to obtain the cumulative tidal volume, perform spatial mapping to construct a pressure-volume sequence, extract the inspiratory start and end coordinates, and construct a dynamic linear compliance reference benchmark; S4: Perform nonlinear deviation quantification on the dynamic linear compliance reference benchmark and real-time alveolar estimated pressure to obtain trajectory deviation, and use the trapezoidal integral algorithm to perform phase lag moment calculation on the trajectory deviation of the inspiratory and expiratory phases to generate the total nonlinear deviation moment of the inspiratory and expiratory phases. S5: Perform lung compliance state discrimination on the total nonlinear deviation moment of the inspiratory and expiratory phases, and perform parameter negative feedback correction based on the discrimination result to generate target tidal volume parameters.

2. The intelligent matching method for anesthesia ventilator parameters based on adaptive algorithm according to claim 1, characterized in that, The static flow resistance coefficient includes the airway viscous resistance coefficient and the airway turbulent resistance coefficient; the real-time alveolar estimated pressure includes the lung elastic recoil pressure and the intrinsic positive end-expiratory pressure; the dynamic linear compliance reference benchmark includes the compliance slope and the pressure volume intercept; the total nonlinear deviation moment of the inspiratory and expiratory phases includes the inspiratory filling hysteresis moment and the expiratory emptying damping moment; and the target tidal volume parameter includes the preset tidal volume and the tubing compliance compensation.

3. The intelligent matching method for anesthesia ventilator parameters based on adaptive algorithm according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Monitors the initial state of the anesthesia ventilator and controls the inspiratory valve to close and perform inspiratory blockade. Triggers the sensor to collect the peak airway pressure, plateau pressure and blockade flow rate values, establishes a time axis correlation mapping and encapsulates it to generate a set of basic airway mechanics parameters. S102: Based on the airway mechanics parameter set, extract the peak pressure value and plateau pressure value of the airway, perform difference calculation by subtracting the plateau pressure value from the peak pressure value of the airway, calculate the pressure difference value of the airflow changing from dynamic flow to static stagnation, and obtain the airway pressure gradient value. S103: Call the blocking flow velocity value in the airway mechanics basic parameter set, combine it with the airway pressure gradient value to perform impedance inversion and construct a linear flow resistance relationship, and use the airway pressure gradient value to perform a division operation on the blocking flow velocity value to obtain the static flow resistance coefficient.

4. The intelligent matching method for anesthesia ventilator parameters based on adaptive algorithm according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Call the static flow resistance coefficient to activate the high-frequency sampling port of the ventilator airway to collect the real-time airway pressure signal and real-time flow rate signal, perform time axis alignment and anomaly removal processing, associate and store the aligned real-time airway pressure and real-time flow rate numerical sequence, and establish a respiratory fluid dynamics monitoring dataset. S202: Extract the real-time flow velocity numerical sequence based on the respiratory fluid dynamics monitoring dataset, construct a linear resistance calculation relationship using the static flow resistance coefficient, and perform a multiplication operation between the flow velocity sampling points in the real-time flow velocity numerical sequence and the static flow resistance coefficient to generate a dynamic pipeline pressure drop sequence. S203: Perform numerical analysis on the dynamic tubing pressure drop sequence and the respiratory fluid dynamics monitoring dataset to extract the real-time airway pressure numerical sequence, subtract the dynamic tubing pressure drop sequence from the real-time airway pressure numerical sequence, and obtain the real-time alveolar estimated pressure.

5. The intelligent matching method for anesthesia ventilator parameters based on an adaptive algorithm according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: Call the real-time alveolar pressure estimation, extract the real-time flow rate numerical sequence based on the respiratory fluid dynamics monitoring dataset, set the inhalation start trigger point and end cutoff point as the integration boundary, perform cumulative summation operation, calculate the total gas volume of a single inhalation process, and generate the cumulative tidal volume. S302: Based on the cumulative tidal volume and the real-time alveolar estimated pressure, perform multi-dimensional data space reconstruction, establish a two-dimensional Cartesian coordinate mapping system, map the cumulative tidal volume under the same sampling timestamp as the horizontal axis variable and the real-time alveolar estimated pressure as the vertical axis variable, and establish a pressure-volume sequence. S303: For the pressure-volume sequence, retrieve the zero-point coordinates of the inhalation start and the peak coordinates of the inhalation end, and use a two-point linear equation to construct a linear reference line connecting the zero-point coordinates and the peak coordinates to generate a dynamic linear compliance reference.

6. The intelligent matching method for anesthesia ventilator parameters based on adaptive algorithm according to claim 5, characterized in that, The set inhalation start trigger point and end cutoff point are integral boundaries. The real-time flow rate value sequence is traversed in time order. When the flow rate value is detected to be greater than the preset inhalation trigger threshold, the current sampling time is locked as the inhalation start trigger point. The real-time flow rate value sequence is continuously monitored until the flow rate value decays and is less than or equal to the zero flow rate baseline. Then, the current sampling time is locked as the end cutoff point.

7. The intelligent matching method for anesthesia ventilator parameters based on an adaptive algorithm according to claim 5, characterized in that, The specific steps of S4 are as follows: S401: Call the dynamic linear compliance reference benchmark, extract the slope and intercept feature parameters of the straight line, calculate the theoretical linear pressure value under the current volume state by combining the cumulative tidal volume, perform difference calculation between the real-time alveolar estimated pressure and the theoretical linear pressure value, and arrange them in the time sampling order to generate a dynamic trajectory deviation sequence; S402: Based on the dynamic trajectory deviation sequence, call the real-time flow rate numerical sequence to identify the zero-point reversal position, and perform segmentation and extraction of the inhalation and exhalation periods on the dynamic trajectory deviation sequence according to the positive and negative polarity characteristics of the flow rate to construct the inhalation and exhalation phase deviation set. S403: Perform discrete numerical integration on the set of inspiratory and expiratory phase deviations, select adjacent deviation data points as the upper and lower bases, extract the volume increment between sampling points as the height, calculate the area of ​​the infinitesimal trapezoid and perform cumulative summation to generate the total nonlinear deviation moment of the inspiratory and expiratory phases.

8. The intelligent matching method for anesthesia ventilator parameters based on an adaptive algorithm according to claim 7, characterized in that, The step of segmenting and extracting the inspiratory and expiratory phases of the dynamic trajectory deviation sequence based on the positive and negative polarity characteristics of the flow velocity refers to traversing each flow velocity sampling point in the real-time flow velocity value sequence. When the flow velocity value at the current sampling time is detected to be greater than zero, the data point in the dynamic trajectory deviation sequence corresponding to this time is marked as an inspiratory attribute and the data point is assigned to the inspiratory deviation subsequence. When the flow velocity value at the current sampling time is detected to be less than zero, the data point in the dynamic trajectory deviation sequence corresponding to this time is marked as an expiratory attribute and the data point is assigned to the expiratory deviation subsequence.

9. The intelligent matching method for anesthesia ventilator parameters based on an adaptive algorithm according to claim 7, characterized in that, The specific steps of S5 are as follows: S501: Call the total nonlinear deviation moments of the inspiratory and expiratory phases, compare them with the preset compliance linear tolerance threshold, divide the nonlinear level intervals according to the magnitude of the comparison difference, determine the deviation level of the elastic deformation of lung tissue and map it into a digital code, and generate a lung compliance state discrimination index. S502: Based on the lung compliance state discrimination index, retrieve the preset feedback adjustment gain coefficient table of the ventilator control loop to determine the corresponding adjustment weight parameter, construct the negative feedback formula, and perform a multiplication operation on the weight parameter and the total nonlinear deviation moment of the inspiratory and expiratory phases to obtain the tidal volume negative feedback correction amount. S503: For the tidal volume negative feedback correction, collect the basic tidal volume setpoint of the current respiratory cycle, establish a dynamic update rule, add the tidal volume negative feedback correction to the setpoint, perform an algebraic sum operation, and generate the target tidal volume parameter.

10. The intelligent matching method for anesthesia ventilator parameters based on an adaptive algorithm according to claim 9, characterized in that, The preset compliance linear tolerance threshold is determined by calculating the maximum orthogonal deviation of the pressure-volume hysteresis loop data relative to the mid-segment linear regression fitting line based on the pressure-volume hysteresis loop data generated by the standard simulated lung under constant flow ventilation test, and superimposing a preset measurement noise tolerance coefficient.

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