Disclosure of Invention
The invention provides an anesthetic gas output control system, an anesthetic gas output control method, electronic equipment and a non-transitory computer readable storage medium for an anesthetic machine, which can improve the accuracy of anesthetic gas output control aiming at the technical problems existing in the prior art.
The technical scheme for solving the technical problems is as follows:
the invention provides an anesthetic gas output control system for an anesthetic machine, which comprises:
The data acquisition module is used for acquiring respiratory parameters and gas state parameters of a patient and constructing a respiratory dynamics model containing nonlinear correction;
the concentration prediction module is used for constructing a gas mixing concentration prediction model with pressure dynamic compensation according to the airway pressure of the anesthetic gas output by the respiratory dynamics model;
The parameter control module is used for constructing a control objective function based on the concentration deviation, the flow rate change rate and the pressure deviation of the anesthetic gas and controlling the concentration, the flow rate and the pressure of the anesthetic gas to approach respective target values;
an adaptive compensation module for determining an adaptive compensation factor based on the concentration deviation and the pressure change rate of the anesthetic gas;
The flow correction module is used for calculating flow correction values of various anesthetic gases;
and the gas output module is used for outputting the anesthetic gas according to the flow correction values of various anesthetic gases.
Optionally, the data acquisition module is further configured to:
Acquiring real-time measurements of the patient's lung compliance, airway resistance, and respiratory rate;
synchronously collecting the real-time flow, airway pressure and gas concentration of each gas output by the anesthesia machine;
and constructing the respiratory dynamics model according to the real-time measurement values of the lung compliance, the airway resistance and the respiratory frequency of the patient and the real-time flow, the airway pressure and the gas concentration of each gas output by the anesthesia machine.
Optionally, the expression of the respiratory dynamics model is:
Wherein, theIs the pressure, C is the lung compliance,Is the gas flow, R is the airway resistance,Is a nonlinear correction factor and f is the respiratory rate.
Optionally, the nonlinear correction factor is determined by:
basic parameters under different morphological characteristics are obtained through clinical big data training, wherein the basic parameters comprise real-time respiration waveforms;
dynamically correcting the real-time respiratory waveform by adopting a Kalman filter to obtain correction parameters;
and updating the correction parameters on line based on a recursive least square method to obtain the nonlinear correction coefficient.
Optionally, the concentration prediction module is further configured to:
acquiring the ratio of the airway pressure of anesthetic gas to the standard pressure to obtain a pressure normalization factor;
carrying out weighted summation on the concentration of each type of anesthetic gas to obtain mixed concentration;
and constructing the gas mixed concentration prediction model according to the pressure normalization factor and the mixed concentration.
Optionally, the expression of the gas mixture concentration prediction model is:
Wherein, theIs predictedThe mixed concentration of the anesthetic gases is changed at the moment,Is the concentration of the i-th anesthetic gas at time t,Is the weight of the class i anesthetic gas,The first correction parameter, the second correction parameter and the third correction parameter,Is a standard pressure.
Optionally, the parameter control module is further configured to:
acquiring the mixed concentration of target anesthetic gas and the reference pressure;
obtaining the concentration deviation of the anesthetic gas according to the predicted difference value between the mixed concentration of the anesthetic gas and the target mixed concentration of the anesthetic gas;
and obtaining the pressure deviation of the anesthetic gas according to the difference value between the current airway pressure of the anesthetic gas and the reference pressure.
Optionally, the adaptive compensation module is further configured to:
acquiring a reference factor representing a reference gain;
Determining the airway pressure change rate of the anesthetic gas according to the current airway pressure of the anesthetic gas;
The adaptive compensation factor is determined based on the concentration deviation of the anesthetic gas, the airway pressure change rate, and the baseline factor.
Optionally, the flow correction module is further configured to:
determining a concentration deviation rate of change of the anesthetic gas;
Acquiring an integral adjustment coefficient and a differential adjustment coefficient;
and correcting the current flow of each anesthetic gas according to the concentration deviation change rate and the concentration deviation of the anesthetic gas and the self-adaptive compensation factor to obtain flow correction values of each anesthetic gas.
The invention also provides an anesthetic gas output control method for the anesthetic machine, which comprises the following steps:
Collecting respiratory parameters and gas state parameters of a patient, and constructing a respiratory dynamics model containing nonlinear correction;
constructing a gas mixing concentration prediction model with pressure dynamic compensation according to the airway pressure of anesthetic gas output by the respiratory dynamics model;
constructing a control objective function based on the concentration deviation, the flow rate change rate and the pressure deviation of the anesthetic gas, and controlling the concentration, the flow rate and the pressure of the anesthetic gas to approach respective target values;
Determining an adaptive compensation factor based on the concentration deviation and the pressure change rate of the anesthetic gas;
calculating flow correction values of various anesthetic gases;
And outputting the anesthetic gas according to the flow correction value of each anesthetic gas.
In addition, in order to achieve the aim, the invention also provides electronic equipment which comprises a memory and a processor, wherein the memory is used for storing a computer software program, and the processor is used for reading and executing the computer software program so as to further realize the anesthetic gas output control method for the anesthetic machine.
In addition, in order to achieve the above object, the present invention also proposes a non-transitory computer-readable storage medium having stored therein a computer software program which, when executed by a processor, implements an anesthetic gas output control method for an anesthetic machine as described above.
The beneficial effects of the invention are as follows:
(1) According to the invention, the accurate respiratory dynamics model is established by monitoring the respiratory parameters of the patient in real time, so that the output anesthetic gas concentration meets the requirements of the patient, and the gas mixing prediction model is adopted, so that the future gas concentration can be dynamically predicted according to factors such as gas flow, pressure, concentration and the like, the concentration overshoot or deficiency is avoided, and the control precision is improved.
(2) The invention introduces an adaptive compensation factor, and can dynamically adjust according to the real-time breathing state of the patient instead of adopting fixed parameter control. By combining an integral-differential regulation mechanism, the device can automatically adapt to physiological states of different patients and meet personalized anesthesia requirements.
(3) The invention can quickly respond to the change of respiratory parameters through a dynamic compensation mechanism, avoid concentration deviation accumulation and ensure that the concentration of the output anesthetic gas is always in an optimal range.
In summary, the invention realizes the intelligent management of anesthetic gas through the technologies of precise control, self-adaptive adjustment, multi-objective optimization, dynamic compensation and the like, improves the control precision, adaptability and safety, reduces the gas waste and environmental pollution, and has wide clinical application prospect.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the description of the present invention, the term "for example" is used to mean "serving as an example, instance, or illustration. Any embodiment described as "for example" in this disclosure is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the invention. In the following description, details are set forth for purposes of explanation. It will be apparent to one of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known structures and processes have not been described in detail so as not to obscure the description of the invention with unnecessary detail. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
Referring to fig. 1, fig. 1 is a schematic diagram of an anesthetic gas output control method for an anesthetic machine according to the present invention. As shown in fig. 1, the terminal and the server are connected through a network, for example, a wired or wireless network connection. The terminal may include, but is not limited to, mobile terminals such as mobile phones and tablets, and fixed terminals such as computers, inquiry machines and advertising machines, where applications of various network platforms are installed. The server provides various business services for the user, including a service push server, a user recommendation server and the like.
It should be noted that, the scenario diagram of the anesthetic gas output control method for an anesthetic machine shown in fig. 1 is only an example, and the terminal, the server and the application scenario described in the embodiment of the present invention are for more clearly describing the technical solution of the embodiment of the present invention, and do not generate limitation on the technical solution provided by the embodiment of the present invention, and as a person of ordinary skill in the art can know that, with the evolution of the system and the appearance of a new service scenario, the technical solution provided by the embodiment of the present invention is applicable to similar technical problems.
Wherein the terminal may be configured to:
Collecting respiratory parameters and gas state parameters of a patient, and constructing a respiratory dynamics model containing nonlinear correction;
constructing a gas mixing concentration prediction model with pressure dynamic compensation according to the airway pressure of anesthetic gas output by the respiratory dynamics model;
constructing a control objective function based on the concentration deviation, the flow rate change rate and the pressure deviation of the anesthetic gas, and controlling the concentration, the flow rate and the pressure of the anesthetic gas to approach respective target values;
Determining an adaptive compensation factor based on the concentration deviation and the pressure change rate of the anesthetic gas;
calculating flow correction values of various anesthetic gases;
And outputting the anesthetic gas according to the flow correction value of each anesthetic gas.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an anesthetic gas output control system for an anesthetic machine according to the present invention.
As shown in fig. 2, an anesthetic gas output control system for an anesthetic machine according to an embodiment of the present invention includes:
The data acquisition module 201 is used for acquiring respiratory parameters and gas state parameters of a patient and constructing a respiratory dynamics model containing nonlinear correction;
The concentration prediction module 202 is configured to construct a gas mixing concentration prediction model with pressure dynamic compensation according to the airway pressure of the anesthetic gas output by the respiratory dynamics model;
a parameter control module 203, configured to construct a control objective function based on the concentration deviation, the flow rate change rate, and the pressure deviation of the anesthetic gas, and control the concentration, the flow rate, and the pressure of the anesthetic gas to approach respective target values;
an adaptive compensation module 204 for determining an adaptive compensation factor based on the concentration deviation and the pressure change rate of the anesthetic gas;
the flow correction module 205 is used for calculating flow correction values of various anesthetic gases;
and the gas output module 206 is used for outputting the anesthetic gas according to the flow correction values of various anesthetic gases.
In some embodiments, the data acquisition module 201 is further configured to:
Acquiring real-time measurements of the patient's lung compliance, airway resistance, and respiratory rate;
synchronously collecting the real-time flow, airway pressure and gas concentration of each gas output by the anesthesia machine;
and constructing the respiratory dynamics model according to the real-time measurement values of the lung compliance, the airway resistance and the respiratory frequency of the patient and the real-time flow, the airway pressure and the gas concentration of each gas output by the anesthesia machine.
In some embodiments, the expression of the respiratory kinetic model is:
Wherein, theIs the pressure, C is the lung compliance,Is the gas flow, R is the airway resistance,Is a nonlinear correction factor and f is the respiratory rate.
In particular, the method comprises the steps of,The airway pressure of the patient at time t is indicated. C represents the distensibility of the lung.Indicating the amount of gas flow per unit time. R represents the degree of obstruction of the gas flow.Reflecting the nonlinear effects of the gas flow. f represents the number of breaths per minute.
Lung compliance portionC is lung compliance, indicating the extent to which the lung can dilate under pressure. The greater the compliance, the greater the pulmonary distensibility and the lower the airway pressure.Is the gas flow rate, the volume of gas passing through the airway per unit time, typically in units of L/min. The integral term represents the response of the lung to gas flow, similar to expansion by an elastic volume.Indicating the effect of pulmonary compliance on airway pressure, the greater the compliance, the less the pressure response to flow.Is the integral of the gas flow, indicating the cumulative effect of the gas flow on pressure, i.e. taking into account the gradual pressure change of the gas flow on the expansion of the lungs.
Lung compliance portionMainly reflecting the effect of lung dilatation. By integration of the gas flow, the "absorption" process of the gas flow by the lungs can be seen. If the lung compliance C is greater, the airway pressure will be lower.
Airway resistance portionR is airway resistance, reflecting the resistance experienced by the gas as it flows, and is generally related to factors such as the shape, viscosity, etc. of the airway.Is the gas flow, which represents the amount of flow of gas through the airway.Is the product of airway resistance and flow, 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 linear with flow. This section describes the changes in airway pressure due to the restriction of gas flow by airway resistance. The greater the airway resistance, the higher the pressure within the airway as gas flows.
Nonlinear correction term,Is a nonlinear correction coefficient, and controls the nonlinear influence of flow on pressure change.Is a square term of the gas flow, and indicates that the influence of the gas flow on the pressure is nonlinear, i.e. the amplitude of the pressure increase increases when the flow increases.Represents the periodic variation of respiration, where f is the respiratory rate. This sinusoidal function is used to simulate the periodic variation of the gas flow, reflecting the patient's breathing cycle (inspiration and expiration). This section accounts for the nonlinear effects of gas flow on airway pressure, particularly the variation in flow during the respiratory cycle. As the flow increases, the pressure increases faster and the pressure changes are periodic with the breathing frequency. This correction term simulates the natural fluctuations in airway pressure of the patient during normal breathing.
It is described how the lungs react to the flow of gas. The higher the lung compliance, the less the effect of the change in gas flow on pressure.The linear effect of airway resistance on airway pressure is shown, with greater airway resistance, more obstructed gas flow, and higher pressure.The periodic variation of airway pressure, especially the effect of flow variation on airway pressure during breathing, is simulated by a nonlinear correction coefficient and a periodic function.
In summary, the core role of the model of the present invention is to accurately simulate airway pressure changes during patient breathing by taking into account the nonlinear effects of lung compliance, airway resistance and flow. The lung compliance determines how the gas flow affects the airway pressure, the airway resistance determines the linear effect of flow changes on pressure, and the nonlinear correction term accurately simulates the periodic nonlinear effect of flow changes on pressure. The model is beneficial to accurate control of anesthetic gas output, and ensures that the anesthetic gas output can be adjusted according to the respiratory dynamics of a patient under different respiratory states.
In some embodiments, the nonlinear correction factor is determined by:
basic parameters under different morphological characteristics are obtained through clinical big data training, wherein the basic parameters comprise real-time respiration waveforms;
dynamically correcting the real-time respiratory waveform by adopting a Kalman filter to obtain correction parameters;
and updating the correction parameters on line based on a recursive least square method to obtain the nonlinear correction coefficient.
Specifically, the respiration waveform data corresponding to different morphological characteristics (such as age, height, weight and lung function) can be extracted from a large number of clinical cases by using clinical big data modeling to form a preliminary basic model parameter library. These parameters may help determine the respiratory response characteristics of the patient.
Real-time respiratory data is dynamically corrected (using a kalman filter) and the actual intraoperatively monitored respiratory waveform may be noisy or fluctuating. These real-time data can be smoothed and corrected using a kalman filter to extract more accurate and stable respiratory characteristic parameters.
On-line self-learning optimization (recursive least square method) can use a recursive least square method (RLS) to update correction parameters in real time, continuously optimize a model and adapt to the current state change of a patient. And finally, outputting a nonlinear correction coefficient for more truly reflecting the current respiratory dynamics state of the patient.
In some embodiments, the concentration prediction module 202 is further configured to:
acquiring the ratio of the airway pressure of anesthetic gas to the standard pressure to obtain a pressure normalization factor;
carrying out weighted summation on the concentration of each type of anesthetic gas to obtain mixed concentration;
and constructing the gas mixed concentration prediction model according to the pressure normalization factor and the mixed concentration.
In some embodiments, the expression of the gas mixture concentration prediction model is:
Wherein, theIs predictedThe mixed concentration of the anesthetic gases is changed at the moment,Is the concentration of the i-th anesthetic gas at time t,Is the weight of the class i anesthetic gas,The first correction parameter, the second correction parameter and the third correction parameter,Is a standard pressure.
In a specific implementation, the formula is used for predicting the mixed concentration of anesthetic gases at the future time t+delta tThe method is a core prediction link in the control system, and provides a target value for the next control action (such as flow regulation).
Summation of weighted concentrations of gasesIndicating the current time and the concentration of various anesthetic gasesAccording to the set weightWeighted mixing is performed.The mixing proportion coefficient of each gas,The sum of (2) is 1. Irrespective of external influencing factors (such as pressure changes, time decay, etc.), a "theoretically ideal" anesthetic mixture concentration is obtained.
Dynamic correction termFor correcting deviations in the concentration of the ideal mixture, time-decay factorsThe concentration of anesthetic gas is not constant in the body, and can be attenuated by natural processes such as volatilization, absorption and the like along with time. Parameters (parameters)It is to control the decay rate, the larger the value, the faster the concentration change. The closer to the operation start time, the larger the correction amount, and the farther the correction amount is, the smaller the correction amount is.
Pressure normalization factor,Is the current patient airway pressure.Is the standard airway pressure for normalization.Is a nonlinear index, and adjusts the influence degree of pressure on concentration. Gamma=1, linear correction. Gamma >1, the pressure effect is significant, simulating a patient with poor lung compliance. Gamma <1, pressure effect is small.
The overall correction intensity factor is used for amplifying or weakening the weight of the overall correction term, is a core adjustment parameter of the prediction model, and can obtain an optimal value through clinical data fitting.
In summary, the invention can calculate the mixed concentration of anesthetic gas at the next moment in advance, provide feedforward information for the controller, automatically adjust the predicted value according to the airway pressure and time variation of the patient, reduce the gap between the corrected ideal concentration and the actual physiological absorption/response, and provide accurate input basis for the follow-up optimization objective function, flow regulation and the like.
In some embodiments, the parameter control module 203 is further configured to:
acquiring the mixed concentration of target anesthetic gas and the reference pressure;
obtaining the concentration deviation of the anesthetic gas according to the predicted difference value between the mixed concentration of the anesthetic gas and the target mixed concentration of the anesthetic gas;
and obtaining the pressure deviation of the anesthetic gas according to the difference value between the current airway pressure of the anesthetic gas and the reference pressure.
In some embodiments, the control objective function may be expressed as:
where J is the function value of the control objective function,Is the predicted concentration of the anesthetic gas mixture,Is the target anesthetic gas mixture concentration,Is the reference pressure of the fluid to be measured,Is a first weight, a second weight, and a third weight.
In a specific implementation, J represents an overall performance evaluation index of the control system, and the smaller the numerical value is, the better the control effect is. The control system will minimize J through an optimization algorithm to achieve optimal control.
Square term of anesthetic concentration deviationEnsures the accurate control of the concentration of the anesthetic gas, prevents excessive or insufficient concentration, ensures the safe and effective anesthetic effect,Is the predicted gas mixture concentration (from the predictive model described above),Is the target anesthetic concentration set by a doctor, and the error between the output concentration of the system and the target concentration is measured by the target anesthetic concentration, and the smaller the error is, the better the error is.Is the first weight, and represents the degree of importance of the system on the concentration accuracy.
Flow regulation stability termAvoiding frequent or severe regulation of gas output, prolonging the service life of equipment, reducing system oscillation and improving the comfort level of patients.Is the rate of change of the gas flow (i.e., the "regulation speed"), which measures the smoothness of the system regulation flow, the more severe the flow change, the greater the integral value,Penalty coefficients for adjusting the rate.
Pressure deviation termControl pressure fluctuation, protect lung tissue and avoid lung injury caused by high pressure or insufficient ventilation caused by low pressure. P is the current airway pressure (output by the respiratory dynamics model),A desired or reference pressure (physician set or historical average), representing the degree to which the actual airway pressure deviates from a normal reference value,Indicating how much importance is placed on maintaining pressure stability by the control system.
In conclusion, the method comprises the steps of,The control object of (1) is anesthesia concentration, which is used for improving anesthesia control accuracy.The control object of (1) is output flow stability, which is used for reducing regulation mutation and improving system stability.The control object of (1) is airway pressure, which is used for ensuring the respiratory safety of patients and avoiding crush injury or insufficient ventilation. Each item is weighted and combined through the weight, and finally a unified optimization target is formed for the control system to use.
The control objective function J is the core of the self-adaptive control system, and comprehensively considers the accuracy of anesthesia concentration, the stability of system adjustment and the safety of a respiratory system. In actual operation, the control strategy can be continuously adjusted through algorithms (such as Model Predictive Control (MPC), gradient descent, genetic algorithm, etc.) to minimize J, thereby realizing intelligent, accurate and dynamic anesthesia management.
In some embodiments, the adaptive compensation module 204 is further configured to:
acquiring a reference factor representing a reference gain;
Determining the airway pressure change rate of the anesthetic gas according to the current airway pressure of the anesthetic gas;
The adaptive compensation factor is determined based on the concentration deviation of the anesthetic gas, the airway pressure change rate, and the baseline factor.
In some embodiments, the adaptive compensation factor may be expressed as:
Wherein, theIs an adaptive compensation factor that is used to compensate the compensation factor,Is the reference factor, concentration deviationIndicating a deviation of the predicted anesthetic gas mixture concentration from the target anesthetic gas mixture concentration,The first dynamic adjustment parameter, the second dynamic adjustment parameter and the third dynamic adjustment parameter are respectively,Is a reference factor for the reference gain.
The formula is used for dynamically calculating the adaptive compensation factorThe method is used for adjusting the response intensity of the gas output in a control system, so that the control is more flexible and stable, and the method has individuation adaptability.Is a baseline factor representing the base control strength of the control system in a default/static condition, set by experience or system calibration.
Concentration deviation,Is the predicted gas concentration (from the predictive model),Is to set the target concentration at which the concentration is to be measured,And the deviation between the predicted value and the target value of the system is expressed and is used for dynamically correcting the control force.
Error nonlinear amplification function,Is a hyperbolic tangent function, has an output range of (-1, 1), and has nonlinear saturation characteristics.Is an error amplification factor that makes the system more sensitive or more passive to concentration deviations.The intensity is dynamically adjusted, and the magnification is controlled. Specifically, when the concentration deviation is smaller, the correction force is small, when the concentration deviation is increased, the response force is increased, but the system is limited by the tanh, so that excessive adjustment is prevented, and a dynamic and smooth response enhancement mechanism is realized.
Pressure change penalty factor,Is the rate of change of airway pressure, indicates the degree of instability of the respiratory system state,Is a third dynamic adjustment parameter that controls sensitivity to pressure changes. If the airway pressure fluctuates drastically, the penalty factor approaches 0, the control system output is suppressed, and if the pressure is stable, the penalty term approaches 1, and the system works normally. When the respiratory state of the patient is unstable, the control gain is automatically reduced, and the system overstress reaction or the induction risk is avoided.
In conclusion, the control force can be automatically adjusted according to the actual concentration deviation, the larger the error is, the faster the response is, the control output is automatically slowed down when the pressure fluctuation of the patient is large, and the risk is prevented. Using tanh and exponential functions, mutations or oscillations are avoided and the adaptation needs of different patients are adjusted by each parameter.
In some embodiments, the flow modification module 205 is further configured to:
determining a concentration deviation rate of change of the anesthetic gas;
Acquiring an integral adjustment coefficient and a differential adjustment coefficient;
and correcting the current flow of each anesthetic gas according to the concentration deviation change rate and the concentration deviation of the anesthetic gas and the self-adaptive compensation factor to obtain flow correction values of each anesthetic gas.
In some embodiments, the flow correction values for each type of anesthetic gas can be expressed as:
Wherein, theIs the correction amount of the ith anesthetic gas,Is the current flow of the ith anesthetic gas,Is the integral adjustment coefficient of the device,Is the differential adjustment coefficient.
Specifically, the formula is used for dynamically calculating the correction amount of the ith anesthetic gasTo achieve more accurate anesthesia concentration control in response to patient state changes. Is a typical "proportional + integral + differential + self gain-adapted control structure (p+i+d+k).
Current reference flow x adaptive gain term:, is the originally set flow rate of the ith anesthetic gas (such as isoflurane, nitrous oxide and the like) of the system,Adaptive gain coefficients (from the dynamic gain formulas described above). The system can automatically amplify or weaken the original flow output based on the system state (such as concentration deviation and airway pressure change), and an intelligent regulation mechanism is embodied.
Integral adjustment term (I term):, Is the error in the concentration of the mixed anesthesia,Is the integral adjustment coefficient. The concentration deviation is accumulated for a long time, and when the system error exists for a long time but the amplitude is not large, the integral term can gradually fill the deviation, so that the steady-state error is eliminated. And also has the function of slowly correcting deviation.
Differential adjustment term (D term):, Is the rate of change of the concentration deviation,Is the differential adjustment coefficient. The term predictively adjusts the system response by taking control action in advance as the concentration bias is increasing or decreasing rapidly. Reduces overshoot, suppresses oscillation, and improves response sensitivity.
Is a real-time adaptive adjustment that responds based on the current physiological state of the patient.The control function of (2) is to compensate the long-term error and improve the stability.The control function of (a) is to respond to the error trend rapidly, reduce delay and overshoot.
In summary, the invention can accurately respond to the target concentration, avoid the over-deep/over-shallow anesthesia, each gas can be independently corrected, adapt to different patient demands, have quick adaptability to sudden changes (such as a wake-up period and operation stimulation), and form a control closed loop through error, gain, output, concentration and error.
Referring to fig. 3, a flowchart of an anesthetic gas output control method for an anesthetic machine according to the present invention is provided, including the steps of:
step 301, collecting respiratory parameters and gas state parameters of a patient, and constructing a respiratory dynamics model containing nonlinear correction;
Step 302, constructing a gas mixing concentration prediction model with pressure dynamic compensation according to the airway pressure of anesthetic gas output by the respiratory dynamics model;
step 303, constructing a control objective function based on the concentration deviation, the flow rate change rate and the pressure deviation of the anesthetic gas, and controlling the concentration, the flow rate and the pressure of the anesthetic gas to approach respective target values;
Step 304, determining an adaptive compensation factor based on the concentration deviation and the pressure change rate of the anesthetic gas;
step 305, calculating flow correction values of various anesthetic gases;
and 306, outputting anesthetic gas according to the flow correction values of various anesthetic gases.
The specific embodiments and advantages of steps 301-306 are described in the above sections of modules 201-206, and are not repeated here.
Referring to fig. 4, fig. 4 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the invention. As shown in fig. 4, 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, wherein the processor 420 executes the computer program 411 to implement the following steps:
Collecting respiratory parameters and gas state parameters of a patient, and constructing a respiratory dynamics model containing nonlinear correction;
constructing a gas mixing concentration prediction model with pressure dynamic compensation according to the airway pressure of anesthetic gas output by the respiratory dynamics model;
constructing a control objective function based on the concentration deviation, the flow rate change rate and the pressure deviation of the anesthetic gas, and controlling the concentration, the flow rate and the pressure of the anesthetic gas to approach respective target values;
Determining an adaptive compensation factor based on the concentration deviation and the pressure change rate of the anesthetic gas;
calculating flow correction values of various anesthetic gases;
And outputting the anesthetic gas according to the flow correction value of each anesthetic gas.
Referring to fig. 5, fig. 5 is a schematic diagram of an embodiment of a computer readable storage medium according to an embodiment of the invention. As shown in fig. 5, the present embodiment provides a computer-readable storage medium 500 having stored thereon a computer program 411, which computer program 411, when executed by a processor, performs the steps of:
Collecting respiratory parameters and gas state parameters of a patient, and constructing a respiratory dynamics model containing nonlinear correction;
constructing a gas mixing concentration prediction model with pressure dynamic compensation according to the airway pressure of anesthetic gas output by the respiratory dynamics model;
constructing a control objective function based on the concentration deviation, the flow rate change rate and the pressure deviation of the anesthetic gas, and controlling the concentration, the flow rate and the pressure of the anesthetic gas to approach respective target values;
Determining an adaptive compensation factor based on the concentration deviation and the pressure change rate of the anesthetic gas;
calculating flow correction values of various anesthetic gases;
And outputting the anesthetic gas according to the flow correction value of each anesthetic gas.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a system, method, or computer program product. Accordingly, 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 embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.