Anesthetic gas output control system for anesthesia machine and method thereof

Abstract

The present invention relates to an anesthetic gas output control system and method for an anesthesia machine, and relates to the technical field of anesthesia machines. The system includes: a data acquisition module for collecting a patient's respiratory parameters and gas state parameters and constructing a respiratory dynamics model including nonlinear correction; a concentration prediction module for constructing a gas mixture concentration prediction model with dynamic pressure compensation based on the airway pressure of the anesthetic gas output by the respiratory dynamics model; a parameter control module for constructing a control objective function based on the concentration deviation, flow rate change rate, and pressure deviation of the anesthetic gas; an adaptive compensation module for determining an adaptive compensation factor based on the concentration deviation and pressure change rate of the anesthetic gas; a flow correction module for calculating flow correction values ​​for various anesthetic gases; and a gas output module for outputting anesthetic gas based on the flow correction values ​​for various anesthetic gases. This system can improve the accuracy of anesthetic gas output control.

Description

Technical Field

[0001] The present invention relates to the technical field of anesthesia machines, and in particular to an anesthetic gas output control system, method, electronic device, and non-transient computer-readable storage medium for an anesthesia machine. Background Art

[0002] An anesthesia machine is a medical device that provides anesthetic gases to patients during surgery. One of its core functions is to mix oxygen, nitrous oxide, and volatile anesthetics (such as isoflurane and sevoflurane) in a set ratio and deliver a stable output to ensure the patient's anesthesia during surgery. Existing anesthetic gas output control methods primarily utilize mechanical or electronic flow control valves. These valves, combined with components such as flow meters, pressure sensors, and proportional valves, regulate the flow of different gases and deliver a stable mixed gas output using a gas mixer.

[0003] However, existing control methods generally suffer from delayed response, limited control accuracy, and poor adaptability to varying patient lung functions. For example, traditional proportional valve regulation mechanisms struggle to achieve real-time, high-precision control of output flow in rapidly changing clinical environments. This can lead to deviations between the actual inhaled anesthetic gas concentration and the set value, particularly when changes in the patient's lung compliance or ventilation resistance occur. Summary of the Invention

[0004] The present invention addresses the technical problems existing in the prior art and provides an anesthetic gas output control system, method, electronic device and non-transitory computer-readable storage medium for an anesthesia machine, which can improve the accuracy of anesthetic gas output control.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] The present invention provides an anesthetic gas output control system for an anesthesia machine, the system comprising:

[0007] The data acquisition module is used to collect the patient's respiratory parameters and gas state parameters and build a respiratory dynamics model including nonlinear correction;

[0008] A concentration prediction module, configured to construct a gas mixture concentration prediction model with dynamic pressure compensation according to the airway pressure of the anesthetic gas output by the respiratory dynamics model;

[0009] a parameter control module, configured to construct a control objective function based on the concentration deviation, flow rate change rate, and pressure deviation of the anesthetic gas, and control the concentration, flow rate, and pressure of the anesthetic gas to be close to respective target values;

[0010] an adaptive compensation module, configured to determine an adaptive compensation factor based on a concentration deviation and a pressure change rate of the anesthetic gas;

[0011] A flow correction module, used for calculating the flow correction value of each type of anesthetic gas;

[0012] The gas output module is used to output anesthetic gas according to the flow correction value of each type of anesthetic gas.

[0013] Optionally, the data acquisition module is further used to:

[0014] Obtaining real-time measurements of the patient's lung compliance, airway resistance, and respiratory rate;

[0015] Synchronously collect the real-time flow rate, airway pressure and gas concentration of each gas output by the anesthesia machine;

[0016] The respiratory dynamics model is constructed based on the real-time measured values ​​of the patient's lung compliance, airway resistance, and respiratory rate, as well as the real-time flow rate, airway pressure, and gas concentration of each gas output by the anesthesia machine.

[0017] Optionally, the respiratory dynamics model is expressed as:

[0018]

[0019] in, is the pressure, C is the lung compliance, is the gas flow rate, R is the airway resistance, is the nonlinear correction coefficient, and f is the respiratory frequency.

[0020] Optionally, the nonlinear correction coefficient is determined by the following steps:

[0021] Obtain basic parameters under different body posture characteristics through clinical big data training; the basic parameters include real-time respiratory waveforms;

[0022] Dynamically correcting the real-time respiratory waveform using a Kalman filter to obtain correction parameters;

[0023] The correction parameter is updated online based on the recursive least square method to obtain the nonlinear correction coefficient.

[0024] Optionally, the concentration prediction module is further configured to:

[0025] Obtaining the ratio of the airway pressure of the anesthetic gas to the standard pressure to obtain a pressure normalization factor;

[0026] Performing weighted summation on the concentrations of each type of anesthetic gas to obtain a mixed concentration;

[0027] The gas mixture concentration prediction model is constructed according to the pressure normalization factor and the mixture concentration.

[0028] Optionally, the gas mixture concentration prediction model is expressed as:

[0029]

[0030] in, It is predicted Anesthetic gas mixture concentration at all times, is the concentration of the i-th type of anesthetic gas at time t, is the weight of the anesthetic gas of type i, are the first correction parameter, the second correction parameter and the third correction parameter, is the standard pressure.

[0031] Optionally, the parameter control module is further configured to:

[0032] Obtain target anesthetic gas mixture concentration and reference pressure;

[0033] Obtaining a concentration deviation of the anesthetic gas according to a difference between the predicted mixed concentration of the anesthetic gas and the target mixed concentration of the anesthetic gas;

[0034] The pressure deviation of the anesthetic gas is obtained according to the difference between the current airway pressure of the anesthetic gas and the reference pressure.

[0035] Optionally, the adaptive compensation module is further configured to:

[0036] obtaining a reference factor representing a reference gain;

[0037] determining a rate of change of the airway pressure of the anesthetic gas based on the current airway pressure of the anesthetic gas;

[0038] The adaptive compensation factor is determined according to the concentration deviation of the anesthetic gas, the airway pressure change rate, and the reference factor.

[0039] Optionally, the flow correction module is further configured to:

[0040] determining a concentration deviation rate of change of the anesthetic gas;

[0041] Obtain the integral adjustment coefficient and the differential adjustment coefficient;

[0042] According to the concentration deviation change rate and concentration deviation of the anesthetic gas, and the adaptive compensation factor, the current flow rate of each type of the anesthetic gas is corrected to obtain the flow correction value of each type of the anesthetic gas.

[0043] The present invention also provides an anesthetic gas output control method for an anesthesia machine, the method comprising:

[0044] Collect the patient's respiratory parameters and gas state parameters, and build a respiratory dynamics model including nonlinear correction;

[0045] Constructing a gas mixture concentration prediction model with dynamic pressure compensation according to the airway pressure of the anesthetic gas output by the respiratory dynamics model;

[0046] constructing a control objective function based on the concentration deviation, flow rate change rate, and pressure deviation of the anesthetic gas to control the concentration, flow rate, and pressure of the anesthetic gas to approach respective target values;

[0047] determining an adaptive compensation factor based on a concentration deviation and a pressure change rate of the anesthetic gas;

[0048] Calculating flow correction values ​​of various anesthetic gases;

[0049] Anesthetic gas is output according to the flow correction value of each type of anesthetic gas.

[0050] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing an anesthetic gas output control method for an anesthesia machine as described above.

[0051] In addition, to achieve the above-mentioned purpose, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, it implements the anesthetic gas output control method for an anesthesia machine as described above.

[0052] The beneficial effects of the present invention are:

[0053] (1) The present invention establishes an accurate respiratory dynamics model by monitoring the patient's respiratory parameters in real time, thereby ensuring that the output anesthetic gas concentration meets the patient's needs. The gas mixing prediction model is used to dynamically predict the future gas concentration based on factors such as gas flow, pressure, and concentration, thereby avoiding overshoot or undershoot of concentration and improving control accuracy.

[0054] (2) The present invention introduces an adaptive compensation factor that can be dynamically adjusted according to the patient's real-time respiratory status, rather than using fixed parameter control. Combined with the integral-differential adjustment mechanism, it can automatically adapt to the physiological state of different patients and meet personalized anesthesia needs.

[0055] (3) The present invention can quickly respond to changes in respiratory parameters through a dynamic compensation mechanism, avoid accumulation of concentration deviations, and ensure that the output anesthetic gas concentration is always within the optimal range.

[0056] In summary, the present invention realizes the intelligent management of anesthetic gas through precise control, adaptive adjustment, multi-objective optimization and dynamic compensation technologies, improves control accuracy, adaptability and safety, while reducing gas waste and environmental pollution, and has broad clinical application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A scene diagram of an anesthetic gas output control method for an anesthesia machine provided by the present invention;

[0058] Figure 2 This is a structural diagram of an anesthetic gas output control system for an anesthesia machine provided by the present invention;

[0059] Figure 3 A flowchart of an anesthetic gas output control method for an anesthesia machine provided by the present invention;

[0060] Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0061] Figure 5 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0062] 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 those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0063] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0064] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0065] See also Figure 1 , Figure 1 This is a scene diagram of an anesthetic gas output control method for an anesthesia machine provided by the present invention. Figure 1 As shown, the terminal and server are connected via a network, such as a wired or wireless network. Terminals include, but are not limited to, portable devices such as mobile phones and tablets installed with various network platform applications, as well as fixed devices such as computers, kiosks, and advertising machines. The server provides various business services to users, including service push servers and user recommendation servers.

[0066] It should be noted that Figure 1 The scenario diagram of the anesthetic gas output control method for an anesthesia machine shown is only an example. The terminal, server and application scenario described in the embodiment of the present invention are intended to more clearly illustrate the technical solution of the embodiment of the present invention, and do not generate any limitation on the technical solution provided by the embodiment of the present invention. Ordinary technicians in this field can know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0067] Among them, the terminal can be used to:

[0068] Collect the patient's respiratory parameters and gas state parameters, and build a respiratory dynamics model including nonlinear correction;

[0069] Constructing a gas mixture concentration prediction model with dynamic pressure compensation according to the airway pressure of the anesthetic gas output by the respiratory dynamics model;

[0070] constructing a control objective function based on the concentration deviation, flow rate change rate, and pressure deviation of the anesthetic gas to control the concentration, flow rate, and pressure of the anesthetic gas to approach respective target values;

[0071] determining an adaptive compensation factor based on a concentration deviation and a pressure change rate of the anesthetic gas;

[0072] Calculating flow correction values ​​of various anesthetic gases;

[0073] Anesthetic gas is output according to the flow correction value of each type of anesthetic gas.

[0074] See also Figure 2 , Figure 2 This is a structural schematic diagram of an anesthetic gas output control system for an anesthesia machine provided by the present invention.

[0075] like Figure 2 As shown, an anesthetic gas output control system for an anesthesia machine proposed in an embodiment of the present invention includes:

[0076] The data acquisition module 201 is used to collect the patient's respiratory parameters and gas state parameters and construct a respiratory dynamics model including nonlinear correction;

[0077] a concentration prediction module 202 for constructing a gas mixture concentration prediction model with pressure dynamic compensation according to the airway pressure of the anesthetic gas output by the respiratory dynamics model;

[0078] a parameter control module 203 for constructing a control objective function based on the concentration deviation, flow rate change rate, and pressure deviation of the anesthetic gas, and controlling the concentration, flow rate, and pressure of the anesthetic gas to approach respective target values;

[0079] An adaptive compensation module 204 is configured to determine an adaptive compensation factor based on the concentration deviation and pressure change rate of the anesthetic gas;

[0080] A flow correction module 205 is used to calculate the flow correction value of each type of anesthetic gas;

[0081] The gas output module 206 is configured to output anesthetic gas according to the flow rate correction values ​​of the various anesthetic gases.

[0082] In some embodiments, the data acquisition module 201 is further configured to:

[0083] Obtaining real-time measurements of the patient's lung compliance, airway resistance, and respiratory rate;

[0084] Synchronously collect the real-time flow rate, airway pressure and gas concentration of each gas output by the anesthesia machine;

[0085] The respiratory dynamics model is constructed based on the real-time measured values ​​of the patient's lung compliance, airway resistance, and respiratory rate, as well as the real-time flow rate, airway pressure, and gas concentration of each gas output by the anesthesia machine.

[0086] In some embodiments, the respiratory dynamics model is expressed as:

[0087]

[0088] in, is the pressure, C is the lung compliance, is the gas flow rate, R is the airway resistance, is the nonlinear correction coefficient, and f is the respiratory frequency.

[0089] Specifically, represents the patient's airway pressure at time t. C represents the expansion capacity of the lungs. Indicates the amount of gas flowing per unit time. R indicates the degree of obstruction to gas flow. Reflects the nonlinear effect of gas flow. f represents the number of breaths per minute.

[0090] Lung compliance , C is lung compliance, which indicates how much the lungs can expand under a certain pressure. The greater the compliance, the greater the lung's ability to expand and the lower the airway pressure. is the airflow rate, the volume of air passing through the airway per unit time, usually expressed in L / min. The integral term represents the lung's response to the airflow, similar to expanding an elastic volume. It represents the effect of lung compliance on airway pressure. The greater the compliance, the smaller the response of pressure to flow. It is the integral of the gas flow rate and represents the cumulative effect of gas flow on pressure, that is, it takes into account the gradual pressure changes caused by gas flow on the expansion of the lungs.

[0091] Lung compliance This primarily reflects the effect of lung expansion. The integral of gas flow reveals the lung's "absorption" of gas flow. If lung compliance C is high, airway pressure will be low.

[0092] Airway resistance , R is the airway resistance, which reflects the resistance encountered by gas during flow and is usually related to factors such as the shape and viscosity of the airway. It is the gas flow rate, which indicates the amount of gas flowing through the airway. Airway resistance is the product of airway resistance and flow rate, and is used to describe the effect of airway resistance on airway pressure. The greater the airway resistance, the greater the airway pressure, and this pressure is linearly related to the flow rate. This component describes the change in airway pressure caused by the restriction of airway resistance on gas flow. The greater the airway resistance, the higher the airway pressure during gas flow.

[0093] Nonlinear correction term , It is the nonlinear correction coefficient, which controls the nonlinear effect of flow on pressure changes. It is the square term of gas flow rate, indicating that the effect of gas flow rate on pressure is nonlinear, that is, when the flow rate increases, the pressure increase increases. represents the cyclical variation of respiration, where f is the respiratory frequency. This sinusoidal function simulates the cyclical variation of gas flow, reflecting the patient's respiratory cycle (inhalation and exhalation). This component accounts for the nonlinear effect of gas flow on airway pressure, particularly the variation in flow during the respiratory cycle. As flow increases, pressure increases more rapidly, and the pressure variation is cyclical with varying respiratory frequency. This correction term simulates the natural fluctuations in airway pressure that occur during normal breathing.

[0094] Describes how the lungs respond to gas flow. The higher the lung compliance, the smaller the effect of changes in gas flow on pressure. It represents the linear effect of airway resistance on airway pressure. The greater the airway resistance, the more obstructed the gas flow and the higher the pressure. The periodic changes of airway pressure are simulated by nonlinear correction coefficients and periodic functions, especially the effect of flow changes on airway pressure during breathing.

[0095] In summary, the core function of this model is to accurately simulate changes in airway pressure during a patient's breathing by accounting for the nonlinear effects of lung compliance, airway resistance, and flow rate. Lung compliance determines how gas flow affects airway pressure, airway resistance determines the linear effect of flow rate changes on pressure, and the nonlinear correction term accurately simulates the cyclical, nonlinear effect of flow rate changes on pressure. This model facilitates precise control of anesthetic gas delivery, ensuring that anesthetic gas delivery can be adjusted according to the patient's respiratory dynamics under different respiratory states.

[0096] In some embodiments, the nonlinear correction coefficient is determined by the following steps:

[0097] Obtain basic parameters under different body posture characteristics through clinical big data training; the basic parameters include real-time respiratory waveforms;

[0098] Dynamically correcting the real-time respiratory waveform using a Kalman filter to obtain correction parameters;

[0099] The correction parameter is updated online based on the recursive least square method to obtain the nonlinear correction coefficient.

[0100] Specifically, clinical big data modeling can be used to extract respiratory waveform data corresponding to different body characteristics (such as age, height, weight, and lung function) from a large number of clinical cases to form a preliminary basic model parameter library. These parameters can help determine the patient's respiratory response characteristics.

[0101] Dynamic correction of real-time respiratory data (using a Kalman filter): The respiratory waveform monitored during actual surgery may contain noise or fluctuations. A Kalman filter can be used to smooth and correct these real-time data to extract more accurate and stable respiratory characteristic parameters.

[0102] Online self-learning optimization (recursive least squares) uses the recursive least squares method (RLS) to update correction parameters in real time, continuously optimizing the model to adapt to changes in the patient's current state. Ultimately, it outputs nonlinear correction coefficients to more accurately reflect the patient's current respiratory dynamics.

[0103] In some embodiments, the concentration prediction module 202 is further configured to:

[0104] Obtaining the ratio of the airway pressure of the anesthetic gas to the standard pressure to obtain a pressure normalization factor;

[0105] Performing weighted summation on the concentrations of each type of anesthetic gas to obtain a mixed concentration;

[0106] The gas mixture concentration prediction model is constructed according to the pressure normalization factor and the mixture concentration.

[0107] In some embodiments, the gas mixture concentration prediction model is expressed as:

[0108]

[0109] in, It is predicted Anesthetic gas mixture concentration at all times, is the concentration of the i-th type of anesthetic gas at time t, is the weight of the anesthetic gas of type i, are the first correction parameter, the second correction parameter and the third correction parameter, is the standard pressure.

[0110] In specific implementation, this formula is used to predict the anesthetic gas mixture concentration at the future time t+Δt , is the core prediction link in the control system, providing the target value for the next control action (such as flow regulation).

[0111] Sum of weighted concentrations of each gas Indicates the concentration of various anesthetic gases at the current moment According to the set weight Perform a weighted blend. The mixing ratio coefficient of each gas, The sum of the values ​​is 1. Without considering external influencing factors (such as pressure changes, time decay, etc.), a "theoretically ideal" anesthetic mixture concentration is obtained.

[0112] Dynamic correction items Used to correct the deviation from the ideal mixed concentration, time decay factor The concentration of anesthetic gas in the body is not constant and will decay over time due to natural processes such as volatilization and absorption. This controls the decay rate. The larger the value, the faster the concentration changes. The closer to the start of the surgery, the larger the correction; the farther away, the smaller the correction.

[0113] Pressure normalization factor , is the current patient airway pressure. is the standard airway pressure, used for normalization. Is a nonlinear exponent that adjusts the effect of pressure on concentration. γ=1: Linear correction. γ>1: Pressure has a significant effect, simulating patients with poor lung compliance. γ<1: Pressure has a minimal effect.

[0114] It is the overall correction strength factor, which is used to amplify or weaken the weight of the entire correction term. It is the core adjustment parameter of the prediction model and the optimal value can be obtained by fitting clinical data.

[0115] In summary, the present invention can estimate the anesthetic gas mixture concentration at the next moment in advance, provide feedforward information to the controller, automatically adjust the predicted value according to the patient's airway pressure and time changes, reduce the gap between the corrected ideal concentration and the actual physiological absorption / response, and provide accurate input basis for subsequent optimization objective functions, flow regulation, etc.

[0116] In some embodiments, the parameter control module 203 is further configured to:

[0117] Obtain target anesthetic gas mixture concentration and reference pressure;

[0118] Obtaining a concentration deviation of the anesthetic gas according to a difference between the predicted mixed concentration of the anesthetic gas and the target mixed concentration of the anesthetic gas;

[0119] The pressure deviation of the anesthetic gas is obtained according to the difference between the current airway pressure of the anesthetic gas and the reference pressure.

[0120] In some embodiments, the control objective function can be expressed as:

[0121]

[0122] Among them, J is the function value of the control objective function, is the predicted anesthetic gas mixture concentration, is the target anesthetic gas mixture concentration, is the reference pressure, are the first weight, the second weight, and the third weight.

[0123] In practice, J represents the overall performance evaluation index of the control system. A smaller value indicates better control. The control system will minimize J through an optimization algorithm to achieve optimal control.

[0124] Squared deviation of anesthetic concentration Ensure accurate control of anesthetic gas concentration to prevent overdose or underdose, ensuring safe and effective anesthesia effects. is the predicted gas mixture concentration (from the aforementioned prediction model), It is the target anesthetic concentration set by the doctor. This item measures the error between the system output concentration and the target concentration. The smaller the error, the better. It is the first weight, indicating how much the system values ​​concentration accuracy.

[0125] Flow regulation smoothness item Avoid frequent or drastic adjustments to gas output, extend equipment life, reduce system shock, and improve patient comfort. It is the rate of change of gas flow (i.e. "regulation speed"), which measures the smoothness of the system's flow regulation. The more drastic the flow change, the larger the integral value. The penalty factor used to adjust the rate.

[0126] Pressure deviation Control pressure fluctuations, protect lung tissue, and avoid lung damage caused by high pressure or hypoventilation caused by low pressure. P is the current airway pressure (output by the respiratory dynamics model), The desired or reference pressure (set by the doctor or historical average) indicates the degree to which the actual airway pressure deviates from the normal reference value. Indicates the degree to which the control system focuses on maintaining pressure stability.

[0127] In summary, The control object is the anesthetic concentration, which is used to improve the accuracy of anesthesia control. The control object is the output flow stability, which is used to reduce the regulation mutation and improve the system stability. The control object is airway pressure, which is used to ensure patient breathing safety and avoid pressure injuries or insufficient ventilation. Each item is weighted and combined to form a unified optimization target for the control system.

[0128] The control objective function J, the core of the adaptive control system, comprehensively considers the accuracy of anesthetic concentration, the smoothness of system regulation, and the safety of the respiratory system. In actual operation, the control strategy can be continuously adjusted through algorithms (such as model predictive control (MPC), gradient descent, and genetic algorithms) to minimize J, thereby achieving intelligent, precise, and dynamic anesthesia management.

[0129] In some embodiments, the adaptive compensation module 204 is further configured to:

[0130] obtaining a reference factor representing a reference gain;

[0131] determining a rate of change of the airway pressure of the anesthetic gas based on the current airway pressure of the anesthetic gas;

[0132] The adaptive compensation factor is determined according to the concentration deviation of the anesthetic gas, the airway pressure change rate, and the reference factor.

[0133] In some embodiments, the adaptive compensation factor may be expressed as:

[0134]

[0135] in, is the adaptive compensation factor, is the reference factor, concentration deviation , represents the deviation between the predicted anesthetic gas mixture concentration and the target anesthetic gas mixture concentration, are the first dynamic adjustment parameter, the second dynamic adjustment parameter and the third dynamic adjustment parameter, is the reference factor of the reference gain.

[0136] This formula is used to dynamically calculate the adaptive compensation factor , will be used in the control system to adjust the response intensity of gas output, making the control more flexible, stable and with individual adaptability. It is the baseline factor, which represents the basic control strength of the control system under the default / static conditions and is set by experience or system calibration.

[0137] Concentration deviation , is the predicted gas concentration (from the prediction model), is to set the target concentration, Indicates the deviation between the system prediction value and the target value, and is used to dynamically correct the control strength.

[0138] Error nonlinear amplification function , It is a hyperbolic tangent function with an output range of (-1,1) and a nonlinear saturation characteristic. It is the error amplification factor, which makes the system more sensitive or more blunt in response to concentration deviations. It dynamically adjusts the intensity and controls the amplification factor. Specifically, when the concentration deviation is small, the correction force is small; when the concentration deviation increases, the system increases the response force, but is limited by the tanh factor to prevent over-adjustment. This achieves a dynamic and smooth response enhancement mechanism.

[0139] Pressure change penalty factor , It is the rate of change of airway pressure, indicating the instability of the respiratory system. This is the third dynamic adjustment parameter, controlling the sensitivity to pressure changes. If airway pressure fluctuates dramatically, the penalty factor approaches 0, suppressing the control system output. If pressure remains stable, the penalty term approaches 1, and the system operates normally. If the patient's respiratory state becomes unstable, the control gain is automatically reduced to avoid overreaction or risk-inducing system failure.

[0140] In summary, the present invention can automatically adjust control intensity based on actual concentration deviations. The larger the error, the faster the response. When the patient's pressure fluctuates significantly, the control output is automatically slowed down to prevent risks. Using tanh and exponential functions, sudden changes or oscillations are avoided, and the adaptability needs of different patients can be adjusted through various parameters.

[0141] In some embodiments, the flow correction module 205 is further configured to:

[0142] determining a concentration deviation rate of change of the anesthetic gas;

[0143] Obtain the integral adjustment coefficient and the differential adjustment coefficient;

[0144] According to the concentration deviation change rate and concentration deviation of the anesthetic gas, and the adaptive compensation factor, the current flow rate of each type of the anesthetic gas is corrected to obtain the flow correction value of each type of the anesthetic gas.

[0145] In some embodiments, the flow rate correction value of each type of anesthetic gas can be expressed as:

[0146]

[0147] in, is the correction amount of the i-th anesthetic gas, is the current flow rate of the i-th anesthetic gas, is the integral adjustment coefficient, is the differential adjustment coefficient.

[0148] Specifically, this formula is used to dynamically calculate the correction amount of the i-th anesthetic gas , to achieve more precise anesthetic concentration control and respond to changes in patient status. It is a typical "proportional + integral + differential + adaptive gain" control structure (P+I+D+K).

[0149] Current reference flow × adaptive gain term: , is the flow rate of the 𝑖th anesthetic gas originally set by the system (such as isoflurane, nitrous oxide, etc.), Adaptive gain coefficient (derived from the aforementioned dynamic gain formula) automatically amplifies or weakens the original flow output based on system status (such as concentration deviation and airway pressure changes), reflecting the "intelligent regulation" mechanism.

[0150] Integral adjustment item (I item): , is the error in the mixed anesthetic concentration, is the integral adjustment coefficient. When the system error persists for a long time but is small, the integral term can gradually "fill in the gap" and eliminate the steady-state error. It also acts as a slow correction.

[0151] Differential adjustment item (D item): , is the rate of change of concentration deviation, is the differential control coefficient. This predictive control factor adjusts the system response: when concentration deviation is rapidly increasing or decreasing, control action is taken in advance. This reduces overshoot, suppresses oscillation, and improves response sensitivity.

[0152] The control function is real-time adaptive adjustment, responding based on the patient's current physiological state. The control function is to compensate for long-term errors and improve stability. The control function is to quickly respond to error trends and reduce delay and overshoot.

[0153] In summary, the present invention can accurately respond to the target concentration to avoid excessive / shallow anesthesia. Each gas can be independently corrected to adapt to the needs of different patients. It has the ability to quickly adapt to sudden changes (such as the awakening period and surgical stimulation) and forms a control closed loop through "error → gain → output → concentration → error".

[0154] See also Figure 3 , provides a flow chart of a method for controlling anesthetic gas output for an anesthesia machine of the present invention, comprising the following steps:

[0155] Step 301: Acquire the patient's respiratory parameters and gas state parameters, and construct a respiratory dynamics model including nonlinear correction;

[0156] Step 302: constructing a gas mixture concentration prediction model with dynamic pressure compensation based on the airway pressure of the anesthetic gas output by the respiratory dynamics model;

[0157] Step 303: constructing a control objective function based on the concentration deviation, flow rate change rate, and pressure deviation of the anesthetic gas to control the concentration, flow rate, and pressure of the anesthetic gas to approach their respective target values;

[0158] Step 304: determining an adaptive compensation factor based on the concentration deviation and pressure change rate of the anesthetic gas;

[0159] Step 305: Calculate the flow rate correction values ​​of various anesthetic gases;

[0160] Step 306: Output the anesthetic gas according to the flow rate correction values ​​of the various anesthetic gases.

[0161] For the specific implementation and beneficial effects of the above steps 301-306, please refer to the description of the above modules 201-206, which will not be repeated here.

[0162] See also Figure 4 , Figure 4 Schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0163] Collect the patient's respiratory parameters and gas state parameters, and build a respiratory dynamics model including nonlinear correction;

[0164] Constructing a gas mixture concentration prediction model with dynamic pressure compensation according to the airway pressure of the anesthetic gas output by the respiratory dynamics model;

[0165] constructing a control objective function based on the concentration deviation, flow rate change rate, and pressure deviation of the anesthetic gas to control the concentration, flow rate, and pressure of the anesthetic gas to approach respective target values;

[0166] determining an adaptive compensation factor based on a concentration deviation and a pressure change rate of the anesthetic gas;

[0167] Calculating flow correction values ​​of various anesthetic gases;

[0168] Anesthetic gas is output according to the flow correction value of each type of anesthetic gas.

[0169] See also Figure 5 , Figure 5 Schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 5 As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented:

[0170] Collect the patient's respiratory parameters and gas state parameters, and build a respiratory dynamics model including nonlinear correction;

[0171] Constructing a gas mixture concentration prediction model with dynamic pressure compensation according to the airway pressure of the anesthetic gas output by the respiratory dynamics model;

[0172] constructing a control objective function based on the concentration deviation, flow rate change rate, and pressure deviation of the anesthetic gas to control the concentration, flow rate, and pressure of the anesthetic gas to approach respective target values;

[0173] determining an adaptive compensation factor based on a concentration deviation and a pressure change rate of the anesthetic gas;

[0174] Calculating flow correction values ​​of various anesthetic gases;

[0175] Anesthetic gas is output according to the flow correction value of each type of anesthetic gas.

[0176] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0177] Those skilled in the art will appreciate that embodiments of the present invention may be provided as systems, methods, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0178] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0179] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system that is implemented in the process. Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.

[0180] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0181] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0182] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An anesthetic gas output control system for an anesthesia machine, characterized in that: The system comprises: The data acquisition module is used to collect the patient's respiratory parameters and gas state parameters and build a respiratory dynamics model including nonlinear correction; A concentration prediction module, configured to construct a gas mixture concentration prediction model with dynamic pressure compensation according to the airway pressure of the anesthetic gas output by the respiratory dynamics model; a parameter control module, configured to construct a control objective function based on the concentration deviation, flow rate change rate, and pressure deviation of the anesthetic gas, and control the concentration, flow rate, and pressure of the anesthetic gas to approach respective target values; an adaptive compensation module, configured to determine an adaptive compensation factor based on a concentration deviation and a pressure change rate of the anesthetic gas; A flow correction module, used for calculating the flow correction value of each type of anesthetic gas; A gas output module, configured to output anesthetic gas according to the flow correction value of each type of anesthetic gas; The data acquisition module is also used for: Obtaining real-time measurements of the patient's lung compliance, airway resistance, and respiratory rate; Synchronously collect the real-time flow rate, airway pressure and gas concentration of each gas output by the anesthesia machine; Constructing the respiratory dynamics model based on the real-time measured values ​​of the patient's lung compliance, airway resistance, and respiratory rate, as well as the real-time flow rate, airway pressure, and gas concentration of each gas output by the anesthesia machine; The expression of the respiratory dynamics model is: Where t refers to the continuous time during anesthesia, is the function of airway pressure changing with time t, C is lung compliance, is the function of gas flow rate changing with time t, R is the airway resistance, is the nonlinear correction coefficient, f is the respiratory rate, and T is the current time.

2. The anesthetic gas output control system for an anesthesia machine according to claim 1, characterized in that: The nonlinear correction coefficient is determined by the following steps: Obtain basic parameters under different body posture characteristics through clinical big data training; the basic parameters include real-time respiratory waveforms; Dynamically correcting the real-time respiratory waveform using a Kalman filter to obtain correction parameters; The correction parameter is updated online based on the recursive least square method to obtain the nonlinear correction coefficient.

3. The anesthetic gas output control system for an anesthesia machine according to claim 2, characterized in that: The concentration prediction module is also used for: Obtaining the ratio of the airway pressure of the anesthetic gas to the standard pressure to obtain a pressure normalization factor; performing weighted summation on the concentrations of each type of anesthetic gas to obtain a mixed concentration; The gas mixture concentration prediction model is constructed according to the pressure normalization factor and the mixture concentration.

4. The anesthetic gas output control system for an anesthesia machine according to claim 3, characterized in that: The expression of the gas mixture concentration prediction model is: in, It is predicted Anesthetic gas mixture concentration at all times, is the concentration of the i-th type of anesthetic gas at time t, is the weight of the anesthetic gas of type i, are the first correction parameter, the second correction parameter and the third correction parameter, is the standard pressure.

5. The anesthetic gas output control system for an anesthesia machine according to claim 4, characterized in that: The parameter control module is also used for: Obtain target anesthetic gas mixture concentration and pressure target value; Obtaining a concentration deviation of the anesthetic gas according to a difference between the predicted mixed concentration of the anesthetic gas and the target mixed concentration of the anesthetic gas; The pressure deviation of the anesthetic gas is obtained according to the difference between the current airway pressure of the anesthetic gas and the pressure target value.

6. The anesthetic gas output control system for an anesthesia machine according to claim 5, characterized in that: The adaptive compensation module is further configured to: obtaining a reference factor representing a reference gain; determining a rate of change of the airway pressure of the anesthetic gas based on the current airway pressure of the anesthetic gas; The adaptive compensation factor is determined according to the concentration deviation of the anesthetic gas, the airway pressure change rate, and the reference factor.

7. The anesthetic gas output control system for an anesthesia machine according to claim 6, characterized in that: The flow correction module is also used for: determining a concentration deviation rate of change of the anesthetic gas; Obtain integral adjustment coefficient and differential adjustment coefficient; According to the concentration deviation change rate and concentration deviation of the anesthetic gas, and the adaptive compensation factor, the current flow rate of each type of the anesthetic gas is corrected to obtain the flow correction value of each type of the anesthetic gas.

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