Method for evaluating the concentration of an inhaled anesthetic drug

Abstract

The present application relates to the field of narcotic drug concentration regulation, and relates to an inhaled narcotic drug administration concentration analysis and evaluation method. First, according to the basic physiological data, the past anesthesia sensitivity and the operation information and other characteristic data of the patient, the initial administration concentration is obtained by using the fuzzy matching algorithm; the physiological sign data such as blood pressure, heart rate, pulse blood oxygen saturation and end-tidal carbon dioxide partial pressure of the patient are monitored in real time during the anesthesia process and compared with the normal range, whether it is abnormal is judged and the adjustment scheme of the administration concentration is determined according to the administration concentration-physiological sign correlation model; the anesthesia depth is evaluated by monitoring the electroencephalogram double frequency index after adjusting the administration concentration, and the adjustment effect is judged by analyzing the anesthesia depth coincidence degree, so as to realize the closed-loop control of the administration concentration of the narcotic drug. The method can accurately regulate the administration concentration, optimize the concentration adjustment mechanism, effectively improve the safety and effectiveness of the anesthesia process, and provide reliable protection for clinical anesthesia.

Description

Technical Field

[0001] This invention relates to the field of anesthetic drug concentration control, and specifically to a method for analyzing and evaluating the concentration of inhaled anesthetic drugs. Background Technology

[0002] Inhalation anesthesia is an important clinical method due to its rapid onset and metabolism. Precise control of the drug concentration is crucial to anesthetic safety and surgical success. An appropriate concentration ensures suitable depth of anesthesia, facilitating rapid postoperative recovery. Excessive concentration can cause respiratory depression, circulatory disorders, and even endanger life, while also prolonging recovery time; insufficient concentration leads to inadequate anesthesia, intraoperative awareness, and stimulates the body's stress response, compromising surgical safety.

[0003] However, existing methods for controlling anesthetic drug concentrations still have some shortcomings in practical applications.

[0004] For example, Chinese Patent CN106730213A discloses a digital anesthesia control system, comprising: an automatic real-time acquisition monitor for collecting vital signs parameters of anesthetized patients during surgery, such as blood pressure, heart rate, blood oxygen, body temperature, and pulse, and transmitting the collected information to a background monitoring system via a cloud server; a background monitoring system that compares pre-stored normal vital signs parameters of anesthetized patients with the intraoperative vital signs parameters transmitted by the automatic real-time acquisition monitor, and then transmits control command signals to an anesthesia control device via the cloud server; simultaneously, it monitors the anesthesia control device; and an anesthesia control device that receives control command signals transmitted from the background monitoring system and controls the concentration and flow rate of anesthetic gas during surgery. This invention controls the concentration of anesthetic gas, which is directly administered to the patient through a mask, and the nebulizer can provide high and low micro-flow anesthetic doses.

[0005] The shortcomings of the above patent are: 1. No closed-loop control mechanism is formed: The control command signal is transmitted to the anesthesia control device based only on the vital sign parameters received by the background monitoring system to control the concentration and flow rate of anesthetic gas during the operation, but it does not mention the need to readjust the drug concentration based on the actual effect after drug administration, such as the patient's depth of anesthesia and changes in physiological signs, and thus no closed-loop control is formed.

[0006] 2. Unverified concentration adjustment results: The lack of relevant content on monitoring and evaluating the effects of adjusting the concentration of anesthetic gas makes it difficult to optimize and improve the concentration adjustment mechanism based on the evaluation results, and makes it difficult to ensure the accuracy of the drug concentration and the stability of the anesthetic effect. Summary of the Invention

[0007] To address the above problems, this invention proposes a method for analyzing and evaluating the concentration of inhaled anesthetic drugs. The specific technical solution is as follows: A method for analyzing and evaluating the concentration of inhaled anesthetic drugs includes the following steps: S1: Based on the patient's characteristic data, including basic physiological data, combined with a fuzzy matching algorithm, analyze the matching degree between the patient and historical anesthesia cases, and output the patient's initial drug concentration.

[0008] S2: Real-time monitoring of the patient's physiological signs during anesthesia and comparison with preset normal range to determine whether the patient's physiological signs are abnormal. If abnormal, the patient's initial drug concentration needs to be adjusted.

[0009] S3: Generate a drug concentration adjustment plan based on the correlation model between drug concentration and physiological signs established according to historical drug efficacy data. The adjustment plan includes adjustment trend and adjustment amount.

[0010] S4: After adjusting the drug concentration, monitor the patient's depth of anesthesia, analyze the consistency of the depth of anesthesia based on the percentage of time the depth of anesthesia is within the target range and the number and extent of deviations, and evaluate the effect of the drug concentration adjustment.

[0011] Compared with the prior art, the inhaled anesthetic drug administration concentration analysis and evaluation method of the present invention has the following beneficial effects: 1. Closed-loop dynamic control: The present invention determines whether the administration concentration needs to be adjusted by real-time monitoring of the patient's physiological signs and comparing them with a preset range, and generates an adjustment plan based on the correlation model of administration concentration and physiological signs, thereby forming a closed-loop control of the anesthetic drug administration concentration, which can more accurately adjust the administration concentration.

[0012] 2. Precise Validation and Optimization: This invention validates the results of drug concentration adjustment and evaluates the adjustment effect by analyzing the consistency with the depth of anesthesia. This facilitates continuous optimization of the concentration adjustment mechanism based on feedback, thereby improving the safety and effectiveness of anesthesia.

[0013] 3. Personalized drug delivery design: This invention combines patient characteristic data with fuzzy matching of historical cases to provide individualized initial concentrations and reduce bias. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0016] Figure 2This is a schematic diagram of the workflow of the present invention.

[0017] Figure 3 This is a diagram of the overall framework structure of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 , Figure 2 and Figure 3 As shown, the present invention provides a method for analyzing and evaluating the concentration of inhaled anesthetic drugs, including the following steps: S1: Based on the patient's characteristic data, including basic physiological data, combined with a fuzzy matching algorithm, analyze the matching degree between the patient and historical anesthesia cases, and output the patient's initial drug concentration.

[0020] As a preferred embodiment, the specific process of analyzing the matching degree between the patient and historical anesthesia cases in step S1 is as follows: obtaining the patient's characteristic data, which includes basic physiological data, past anesthesia sensitivity and surgical information, and constructing a characteristic set of the patient.

[0021] Extract the feature sets and initial drug concentrations of each individual from the historical anesthesia cases stored in the database.

[0022] The patient's feature set is compared with the feature sets of individuals in historical anesthesia cases to obtain the deviation of each item in the feature set, and the weight of each item in the feature set stored in the database is extracted. Combined with the set relationship function between the deviation and weight of the item in the feature set and the matching degree, the matching degree between the patient and individuals in historical anesthesia cases is analyzed.

[0023] It should be noted that the patient's basic physiological data includes weight, age, liver and kidney function, circulatory status, ASA classification, metabolic rate, and comorbidities.

[0024] It should be noted that the patient's prior anesthesia sensitivity was quantitatively assessed based on the dose-response relationship of the anesthetic drug. By administering different doses of anesthetic drugs to the patient and observing their physiological responses, such as loss of consciousness, degree of muscle relaxation, and pain response, a dose-response curve was plotted. Based on the curve, the drug dose required to produce a specific anesthetic effect can be determined; the smaller the dose, the more sensitive the patient is to the anesthetic drug. In one specific embodiment, propofol is commonly used for general anesthesia. The dose required to achieve loss of consciousness varies among different patients. By accurately recording and analyzing this dose data, the patient's sensitivity to propofol can be quantified to a certain extent.

[0025] It should be noted that the patient's surgical information includes the type of surgery and its duration.

[0026] It should be noted that if the sub-items in the feature set are numerical, the deviation of the sub-items is obtained by subtraction; if the sub-items in the feature set are non-numerical, the deviation values ​​corresponding to the consistency and inconsistency in describing or representing them are set and then quantified. In a specific embodiment, when the sub-items in the feature set are non-numerical, the deviation values ​​corresponding to the consistency and inconsistency in describing or representing them are set to 0 and 1, respectively.

[0027] It should be noted that the weights of each sub-item in the feature set are set according to their importance in assessing the similarity of features between the patient and individuals in historical anesthesia cases.

[0028] In one specific embodiment, the relationship function between the sub-item bias and weight in the feature set and the matching degree is: ,in This indicates the degree of match between the patient and individuals in a historical anesthesia case. Represents the natural constant. They represent the first in the feature set, respectively. The deviation of each sub-item and the preset deviation threshold, , Indicates the preset first The weight of each sub-item.

[0029] As a preferred embodiment, the specific analysis process for obtaining the patient's initial drug concentration in step S1 is as follows: the matching degree between the patient and each individual in historical anesthesia cases is compared to obtain the individual with the highest matching degree, and the initial drug concentration of that individual is used as the patient's initial drug concentration.

[0030] It should be noted that the initial drug concentration for the patient is the target drug concentration or the expected drug concentration. After determining the drug concentration based on the patient's condition and surgical requirements, the drug concentration is dynamically adjusted based on the patient's physiological signs during anesthesia, thus forming a closed-loop control of the anesthetic drug concentration.

[0031] In this embodiment, the present invention combines patient characteristic data with fuzzy matching of historical cases to provide individualized initial concentrations and reduce bias.

[0032] S2: Real-time monitoring of the patient's physiological signs during anesthesia and comparison with preset normal range to determine whether the patient's physiological signs are abnormal. If abnormal, the patient's initial drug concentration needs to be adjusted.

[0033] As a preferred embodiment, the specific process of monitoring the patient's physiological signs during anesthesia in step S2 is as follows: the patient's blood pressure, heart rate and pulse oximetry are monitored in real time using a sphygmomanometer, electrocardiogram monitor and pulse oximeter, and the patient's end-tidal carbon dioxide partial pressure is obtained by monitoring the patient's carbon dioxide waveform during anesthesia.

[0034] It should be noted that blood pressure, heart rate, and pulse oxygen saturation (POS) are all important parameters. ) and end-tidal carbon dioxide partial pressure ( (1) Blood pressure: When anesthesia is too deep, the inhibitory effect of the drug on the cardiovascular system is enhanced, leading to a decrease in blood pressure; when anesthesia is too shallow, pain or stress response activates the sympathetic nervous system, causing an increase in blood pressure. (2) Heart rate: When anesthesia is too shallow, sympathetic nerve excitation leads to an increase in heart rate; when anesthesia is too deep, increased vagal tone or myocardial inhibition can lead to a decrease in heart rate. (3) This reflects oxygenation status; excessively deep anesthesia may cause respiratory depression or airway obstruction. Decrease; if the anesthesia is too light but the patient retains spontaneous breathing and has adequate oxygen supply. Normal or rising. (4) Monitor ventilation efficiency; if deep anesthesia leads to respiratory depression, Increased risk; if pain triggers hyperventilation due to insufficient anesthesia. It may decrease. By comprehensively monitoring and analyzing these four indicators, the concentration of anesthetic drugs can be precisely controlled to maintain the patient's physiological stability during anesthesia.

[0035] As a preferred embodiment, the specific analytical process for determining whether the patient's physiological signs are abnormal in step S2 is as follows: the measured values ​​of various physiological signs of the patient during anesthesia are recorded as follows: , Indicates the first The numbering of each physiological sign data point Extract the normal range of various physiological signs data stored in the database and record it as... .

[0036] By analyzing the formula Calculate the abnormal scores of each physiological sign data. .

[0037] Abnormal scores of various physiological signs Substitute into the analysis formula Calculate the physiological sign abnormality coefficient of the patient ,in Indicates the preset first Weights of physiological signs data.

[0038] The patient's physiological sign abnormality coefficient is compared with the preset physiological sign abnormality coefficient threshold. If the patient's physiological sign abnormality coefficient is greater than the preset physiological sign abnormality coefficient threshold, then the patient's physiological signs are abnormal.

[0039] It should be noted that the weights of the various physiological signs represent their relative importance, and the weights of each physiological sign are set based on the degree to which their stability affects the patient's physiological state. A greater impact on the patient's physiological state results in a higher weight. Furthermore, the specific weight allocation can be adjusted according to the specific circumstances of anesthesia and the individual characteristics of the patient.

[0040] It should be noted that, in one specific embodiment, the normal range and weight of various physiological signs of the patient during anesthesia are detailed in Table 1.

[0041] Table 1. Normal range and weighting of physiological signs data

[0042]

[0043] S3: Generate a drug concentration adjustment plan based on the correlation model between drug concentration and physiological signs established according to historical drug efficacy data. The adjustment plan includes adjustment trend and adjustment amount.

[0044] As a preferred embodiment, the specific process of analyzing the trend of drug concentration adjustment in step S3 is as follows: the patient's various physiological signs during anesthesia are compared with the numerical representations of these physiological signs when the anesthesia is too deep or too shallow, to determine whether the anesthesia is too deep or too shallow. If the anesthesia is too deep, the drug concentration is too high, and the trend of drug concentration adjustment is to decrease. If the anesthesia is too shallow, the drug concentration is too low, and the trend of drug concentration adjustment is to increase.

[0045] It should be noted that when a patient is under deep anesthesia, all of the patient's physiological signs and data will show the numerical values ​​that indicate the deep anesthesia; when a patient is under shallow anesthesia, all of the patient's physiological signs and data will show the numerical values ​​that indicate the shallow anesthesia.

[0046] It should be noted that, in one specific embodiment, the physiological signs are numerically represented when the anesthesia is too deep and when the anesthesia is too shallow, as detailed in Table 2.

[0047] Table 2. Numerical representation of physiological signs when anesthesia is too deep or too shallow.

[0048]

[0049] As a preferred embodiment, the specific process of analyzing the drug concentration adjustment in step S3 is as follows: based on the comparison results of the patient's physiological signs data during anesthesia with their normal range, the overshoot of various physiological signs of the patient is obtained.

[0050] Based on historical pharmacodynamic data and machine learning algorithms, a correlation model between drug concentration and physiological signs was established to obtain the quantitative mapping relationship between changes in drug concentration and changes in various physiological signs. The overshoot of various physiological signs of patients was then substituted into this quantitative mapping relationship to obtain the adjustment amount of drug concentration for patients.

[0051] It should be noted that when physiological signs exceed the upper limit of their normal range, the overshoot of physiological signs represents the upward adjustment, which is the result of subtracting the upper limit from the measured value; when physiological signs are below the lower limit of their normal range, the overshoot of physiological signs represents the downward adjustment, which is the result of subtracting the measured value from the lower limit.

[0052] It should be noted that the establishment of the drug concentration-physiological sign correlation model consists of four steps: First, integrating drug concentration records and corresponding physiological characteristic change data from historical treatment data; second, using machine learning algorithms (such as regression models or neural networks) to extract features and mine patterns in the drug concentration-physiological sign data, and establishing a nonlinear mapping equation between changes in drug concentration and changes in physiological signs; then, determining the quantitative weight relationship between each physiological sign and drug efficacy through model validation; finally, inputting the overshoot of the patient's physiological signs monitored in real time into the trained model, and calculating inversely to obtain the personalized drug concentration adjustment amount, thereby achieving precise dose control based on physiological sign feedback.

[0053] In this embodiment, the present invention determines whether the drug concentration needs to be adjusted by real-time monitoring of the patient's physiological signs and comparing them with a preset range. Based on the correlation model between drug concentration and physiological signs, an adjustment plan is generated, thereby forming a closed-loop control of the anesthetic drug concentration, which can more accurately adjust the drug concentration.

[0054] S4: After adjusting the drug concentration, monitor the patient's depth of anesthesia, analyze the consistency of the depth of anesthesia based on the percentage of time the depth of anesthesia is within the target range and the number and extent of deviations, and evaluate the effect of the drug concentration adjustment.

[0055] As a preferred embodiment, the specific analysis process for monitoring the patient's anesthesia depth in step S4 is as follows: after adjusting the drug concentration, monitor the patient's bispectral index (BSE), extract the anesthesia depth values ​​corresponding to each BSE range stored in the database, and screen to obtain the patient's anesthesia depth after drug concentration adjustment.

[0056] It should be noted that the bispectral index (BIS) was chosen as an indicator for assessing the depth of anesthesia primarily because of its several advantages. First, BIS can reflect the functional state of the cerebral cortex in real time. By analyzing different characteristics of the electroencephalogram (EEG), such as frequency and amplitude, a comprehensive numerical value is derived to quantify the depth of anesthesia, providing a direct understanding of the patient's degree of brain inhibition. Second, BIS has a good correlation with the blood concentration of anesthetic drugs, accurately reflecting the patient's response to these drugs and helping to adjust the dosage in a timely manner to avoid excessive or insufficient anesthesia. Third, BIS is unaffected by surgical procedures or muscle relaxants, exhibiting high specificity and sensitivity, and can stably and reliably reflect changes in the depth of anesthesia. Furthermore, BIS monitoring is simple to perform; data can be obtained through electrodes attached to the patient's head, and it is non-invasive, making it easy to widely apply in clinical practice.

[0057] As a preferred embodiment, the specific process of analyzing the anesthesia depth concordance in step S4 is as follows: set the target range of the patient's anesthesia depth, obtain the proportion of time the patient's anesthesia depth is within the target range, the number of deviations and the average deviation, and substitute them into the preset relationship function between the proportion of time the anesthesia depth is within the target range, the number of deviations and the magnitude of deviations and the anesthesia depth concordance, and output the concordance of the patient's anesthesia depth.

[0058] It should be noted that the target range for the depth of anesthesia is set based on the patient's characteristics and the surgical situation.

[0059] It should be noted that, through the calculation formula The percentage of time spent obtaining the patient's depth of anesthesia within the target range .

[0060] It should be noted that, through the calculation formula The average deviation of the patient's depth of anesthesia ,in This indicates the values ​​representing deviations from the depth of anesthesia at different times. The boundary value representing the target range of anesthesia depth (taking the nearest boundary). This indicates the number of deviations from the depth of anesthesia.

[0061] It should be noted that, in a specific embodiment, the relationship function between the percentage of time the anesthesia depth is within the target range, the number and magnitude of deviations, and the degree of conformity with the anesthesia depth can be: ,in The degree of concordance indicating the depth of anesthesia. This indicates the number of deviations from the depth of anesthesia. This represents the influencing factor corresponding to the number of deviations from the preset unit of anesthesia depth. This indicates the threshold value representing the deviation from the preset depth of anesthesia. These represent the percentage of time the preset anesthesia depth is within the target range, the number of times the anesthesia depth deviates, and the weight of the magnitude of the anesthesia depth deviation, respectively.

[0062] It should be noted that the weighting of the factors in the formula for calculating the concordance of anesthesia depth is based on clinical experience and research data. Clinicians and researchers comprehensively consider the impact of these factors on anesthetic efficacy and patient safety. For example, if it is believed that maintaining the depth of anesthesia within the target range for an extended period is crucial for the quality and safety of patient anesthesia, then a weighting will be assigned... A relatively large value; if frequent deviations from the depth of anesthesia are found to significantly affect the anesthetic effect and the patient's condition, then the value will be appropriately increased. However, if the depth of anesthesia deviates significantly from the expected range, it may lead to serious adverse consequences, which could increase the risk of complications. By continuously adjusting these weights and incorporating feedback from actual clinical outcomes, the weight settings are optimized to accurately measure the concordance of anesthesia depth. In one specific embodiment, The values ​​are 0.4, 0.3, and 0.3, respectively.

[0063] As a preferred embodiment, the specific analysis process for evaluating the effect of drug concentration adjustment in step S4 is as follows: substituting the concordance of the target patient's anesthesia depth into the preset mapping relationship between the concordance of anesthesia depth and the drug concentration adjustment effect coefficient, obtaining the drug concentration adjustment effect coefficient of the target patient, and providing feedback.

[0064] It should be noted that the mapping relationship between the anesthesia depth matching degree and the drug concentration adjustment effect coefficient is positively correlated, that is, the greater the anesthesia depth matching degree, the greater the drug concentration adjustment effect coefficient.

[0065] In this embodiment, the present invention verifies the results of drug concentration adjustment and evaluates the adjustment effect by analyzing the anesthesia depth concordance. This facilitates the continuous optimization of the concentration adjustment mechanism based on feedback, thereby improving the safety and effectiveness of anesthesia.

[0066] The above formulas are all dimensionless calculations, and the preset parameters in the formulas should be set by those skilled in the art according to the actual situation.

[0067] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0068] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0069] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0070] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0071] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing and evaluating the concentration of inhaled anesthetic drugs, characterized in that, Includes the following steps: S1: Based on the patient's characteristic data, including basic physiological data, combined with a fuzzy matching algorithm, analyze the matching degree between the patient and historical anesthesia cases, and output the patient's initial drug concentration; S2: Real-time monitoring of the patient's physiological signs during anesthesia and comparison with preset normal range to determine whether the patient's physiological signs are abnormal. If abnormal, the patient's initial drug concentration needs to be adjusted. S3: Generate a drug concentration adjustment plan based on the correlation model between drug concentration and physiological signs established according to historical drug efficacy data. The adjustment plan includes adjustment trend and adjustment amount. S4: After adjusting the drug concentration, monitor the patient's depth of anesthesia, analyze the consistency of the depth of anesthesia based on the percentage of time the depth of anesthesia is within the target range and the number and extent of deviations, and evaluate the effect of the drug concentration adjustment. The specific process of analyzing the drug concentration adjustment in step S3 is as follows: Based on the comparison of the patient's physiological signs data during anesthesia with their normal range, the overshoot of various physiological signs of the patient is obtained; based on historical drug efficacy data and machine learning algorithms, a correlation model between drug concentration and physiological signs is established to obtain the quantitative mapping relationship between the change in drug concentration and the change in various physiological signs, and the overshoot of various physiological signs of the patient is substituted into the quantitative mapping relationship to obtain the adjustment amount of the patient's drug concentration.

2. The method for analyzing and evaluating the concentration of inhaled anesthetic drugs according to claim 1, characterized in that: The specific process of analyzing the matching degree between the patient and historical anesthesia cases in step S1 is as follows: Acquire patient feature data, including basic physiological data, past anesthesia sensitivity, and surgical information, and construct a patient feature set; Extract the feature sets and initial drug concentrations of each individual from historical anesthesia cases stored in the database; The patient's feature set is compared with the feature sets of individuals in historical anesthesia cases to obtain the deviation of each item in the feature set, and the weight of each item in the feature set stored in the database is extracted. Combined with the set relationship function between the deviation and weight of the item in the feature set and the matching degree, the matching degree between the patient and individuals in historical anesthesia cases is analyzed.

3. The method for analyzing and evaluating the concentration of inhaled anesthetic drugs according to claim 1, characterized in that: The specific analytical process for obtaining the initial drug concentration in step S1 is as follows: The patient was compared with individuals in historical anesthesia cases to identify the individual with the highest match, and the initial drug concentration of that individual was used as the initial drug concentration for the patient.

4. The method for analyzing and evaluating the concentration of inhaled anesthetic drugs according to claim 1, characterized in that: The specific process for monitoring the patient's physiological signs during anesthesia in step S2 is as follows: The patient's blood pressure, heart rate, and pulse oximetry were monitored in real time during anesthesia using a sphygmomanometer, electrocardiogram monitor, and pulse oximeter. The patient's end-tidal carbon dioxide partial pressure was obtained by monitoring the patient's carbon dioxide waveform during anesthesia.

5. The method for analyzing and evaluating the concentration of inhaled anesthetic drugs according to claim 1, characterized in that: The specific analytical process for determining whether the patient's physiological signs are abnormal in step S2 is as follows: The measured values ​​of various physiological signs of the patient during anesthesia were recorded as follows: , Indicates the first The numbering of each physiological sign data point Extract the normal range of various physiological signs data stored in the database and record it as... ; By analyzing the formula Calculate the abnormal scores of each physiological sign data. ; Abnormal scores of various physiological signs Substitute into the analysis formula Calculate the physiological sign abnormality coefficient of the patient ,in Indicates the preset first Weights of physiological signs data; The patient's physiological sign abnormality coefficient is compared with the preset physiological sign abnormality coefficient threshold. If the patient's physiological sign abnormality coefficient is greater than the preset physiological sign abnormality coefficient threshold, then the patient's physiological signs are abnormal.

6. The method for analyzing and evaluating the concentration of inhaled anesthetic drugs according to claim 1, characterized in that: The specific process for analyzing the trend of drug concentration adjustment in step S3 is as follows: The patient's physiological signs during anesthesia are compared with their numerical values ​​at the levels of excessively deep and shallow anesthesia to determine whether the anesthesia is too deep or too shallow. If the anesthesia is too deep, the drug concentration is too high, and the adjustment trend is to decrease the drug concentration. If the anesthesia is too shallow, the drug concentration is too low, and the adjustment trend is to increase the drug concentration.

7. The method for analyzing and evaluating the concentration of inhaled anesthetic drugs according to claim 1, characterized in that: The specific analytical process for monitoring the patient's depth of anesthesia in step S4 is as follows: After adjusting the drug concentration, the patient's bispectral index was monitored, and the anesthesia depth values ​​corresponding to each bispectral index range stored in the database were extracted to screen and obtain the anesthesia depth of the patient after drug concentration adjustment.

8. The method for analyzing and evaluating the concentration of inhaled anesthetic drugs according to claim 1, characterized in that: The specific process for analyzing the anesthesia depth concordance in step S4 is as follows: Set a target range for the patient's anesthesia depth, obtain the percentage of time the patient's anesthesia depth is within the target range, the number of deviations, and the average deviation, and substitute these into a preset relationship function between the percentage of time the patient's anesthesia depth is within the target range, the number of deviations, the magnitude of deviations, and the degree of conformity with the anesthesia depth, and output the degree of conformity with the patient's anesthesia depth.

9. The method for analyzing and evaluating the concentration of inhaled anesthetic drugs according to claim 1, characterized in that: The specific analytical process for evaluating the effect of drug concentration adjustment in step S4 is as follows: The concordance rate of the target patient's anesthesia depth is substituted into the preset mapping relationship between the concordance rate of anesthesia depth and the effect coefficient of drug concentration adjustment to obtain the effect coefficient of drug concentration adjustment for the target patient, and feedback is provided.

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