Inhalation type anesthetic drug administration concentration analysis and evaluation method

Abstract

The invention relates to the field of anesthetic concentration regulation and control, in particular to a method for analyzing and evaluating the administration concentration of an inhalation type anesthetic. The method comprises the following steps: firstly, according to characteristic data such as basic physiological data, previous anesthesia sensitivity and operation information of a patient, applying a fuzzy matching algorithm to obtain an initial administration concentration; physiological sign data such as blood pressure, heart rate, pulse oxyhemoglobin saturation and end-expiratory carbon dioxide partial pressure of the patient are monitored in real time in the anesthesia process and compared with a normal range, whether the physiological sign data are abnormal or not is judged, and an adjustment scheme of the administration concentration is determined according to the administration concentration-physiological sign correlation model; after the administration concentration is adjusted, the electroencephalogram double-frequency index is monitored to evaluate the anesthesia depth, the anesthesia depth matching degree is analyzed to judge the adjustment effect, and therefore closed-loop control over the anesthetic administration concentration is achieved. According to the method, the administration concentration can be accurately regulated and controlled, the concentration regulation mechanism is optimized, the safety and effectiveness of the anesthesia process are effectively improved, and a reliable guarantee is provided for clinical anesthesia.

Description

Technical Field

[0001] The present invention relates to the field of anesthetic drug concentration control, and particularly to a method for analyzing and evaluating the administration concentration of inhaled anesthetic drugs. Background Art

[0002] Inhalation anesthesia is a key method of clinical anesthesia due to its rapid onset and rapid metabolism. Precisely controlling the drug concentration is crucial to anesthesia safety and surgical success. A reasonable concentration ensures an appropriate depth of anesthesia and facilitates rapid postoperative recovery. Excessively high concentrations can cause respiratory depression, circulatory disturbances, and even be life-threatening, and can prolong recovery time. Excessively low concentrations can lead to inadequate anesthesia, causing intraoperative awareness and stimulating the body's stress response, compromising surgical safety.

[0003] However, the existing anesthetic drug concentration control still has some shortcomings in practical applications.

[0004] For example, the existing Chinese patent with publication number CN106730213A discloses a digital anesthesia control system, including: an automatic real-time data acquisition monitor for collecting blood pressure, heart rate, blood oxygen, body temperature, and pulse vital sign parameters of an anesthetized patient during surgery, and transmitting the collected information to a background monitoring system via a cloud server; a background monitoring system for comparing the pre-stored normal vital sign parameters of the anesthetized patient with the intraoperative vital sign parameter information transmitted by the automatic real-time data acquisition monitor, and after comparison, transmitting a control command signal to an anesthesia control device via a cloud server; at the same time, monitoring the anesthesia control device; an anesthesia control device for receiving the control command signal transmitted by the background monitoring system and controlling the concentration and flow of the anesthetic gas during surgery. This invention controls the concentration of the anesthetic gas and directly supplies it to the patient through a mask, and the atomizer can provide high and low micro-flow anesthetic doses.

[0005] The above patents have the following deficiencies: 1. A closed-loop control mechanism has not been formed: the control command signal is only transmitted to the anesthesia control device based on the vital signs parameters received by the background monitoring system to control the concentration and flow of the anesthetic drug gas during surgery, but there is no mention of readjusting the drug concentration based on the actual effect after drug administration, such as the patient's anesthesia depth, changes in physiological signs, etc., and a closed-loop control has not been formed.

[0006] 2. Unverified concentration adjustment results: There is a lack of relevant content to monitor and evaluate the effects of adjusting the anesthetic drug gas concentration, which is not conducive to the subsequent optimization and improvement of the concentration adjustment mechanism based on the evaluation results, and it is difficult to ensure the accuracy of the drug concentration and the stability of the anesthetic effect. Summary of the Invention

[0007] In response to the above problems, the present invention proposes a method for analyzing and evaluating the dosage concentration of inhaled anesthetic drugs. The specific technical solution is as follows: A method for analyzing and evaluating the dosage concentration of inhaled anesthetic drugs comprises the following steps: S1: Analyzing the matching degree between the patient and historical anesthesia cases based on the patient's characteristic data including basic physiological data combined with a fuzzy matching algorithm, and outputting the patient's initial dosage concentration.

[0008] S2: Real-time monitoring of the patient's physiological signs during anesthesia and comparison with the 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 a drug concentration-physiological sign correlation model established based on historical drug efficacy data, wherein the adjustment plan includes an adjustment trend and an adjustment amount.

[0010] S4: Monitor the patient's anesthesia depth after implementing the drug concentration adjustment. Analyze the consistency of the anesthesia depth based on the proportion of time the anesthesia depth is within the target range and the number and magnitude of deviations to evaluate the effect of the drug concentration adjustment.

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

[0012] 2. Accurate verification and optimization: The present invention verifies the results of drug concentration adjustment and evaluates the adjustment effect by analyzing the degree of consistency of anesthesia depth, which is conducive to the subsequent optimization of the concentration adjustment mechanism based on feedback, thereby improving the safety and effectiveness of anesthesia.

[0013] 3. Personalized drug administration design: This invention combines patient characteristic data with fuzzy matching of historical cases to provide personalized initial concentration and reduce deviation. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 Schematic diagram of the method of the present invention.

[0016] Figure 2Schematic diagram of the workflow of the present invention.

[0017] Figure 3 It is the overall framework structure diagram of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] See also Figure 1 、 Figure 2 and Figure 3 As shown, the present invention provides an inhalation anesthetic drug concentration analysis and evaluation method, which includes the following steps: S1: Analyze the matching degree between the patient and historical anesthesia cases based on the patient's characteristic data including basic physiological data combined with a fuzzy matching algorithm, and output the patient's initial drug concentration.

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

[0021] Extract the feature set and initial drug concentration of each individual in the historical anesthesia cases stored in the database.

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

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

[0024] It should be noted that the patient's previous anesthetic sensitivity is quantitatively assessed based on the dose-effect relationship of the anesthetic drug. By administering different doses of anesthetic drugs to patients and observing their physiological responses, such as loss of consciousness, degree of muscle relaxation, and pain response, a dose-effect curve is 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 from patient to patient. By accurately recording and analyzing this dosage 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, quantification is performed by setting deviation values corresponding to the consistent and inconsistent descriptions or representations. In one specific embodiment, when the sub-items in the feature set are non-numerical, the deviation values corresponding to the consistent and inconsistent descriptions or representations are set to 0 and 1, respectively.

[0027] It should be noted that the weight of each sub-item in the feature set is set according to its importance in evaluating the similarity of features between the patient and individual historical anesthesia cases.

[0028] In a specific embodiment, the relationship function between the sub-item deviation and weight and the matching degree in the feature set is: ,in represents the degree of matching between the patient and individuals in historical anesthesia cases, represents a natural constant, Represents the first Deviation of each sub-item and the preset deviation threshold, , Indicates the preset 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: comparing the matching degree of the patient with that of each individual in the historical anesthesia case to obtain the individual with the highest matching degree with the patient, and using the initial drug concentration of the individual as the initial drug concentration of the patient.

[0030] It should be noted that the patient's initial dosing concentration is the target dosing concentration or expected dosing concentration. After the dosing concentration is determined based on the patient's condition and surgical requirements, the dosing concentration is dynamically adjusted based on the patient's physiological signs during anesthesia to determine whether it needs to be adjusted, thereby forming a closed-loop control of the anesthetic drug dosing 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 deviations.

[0032] S2: Real-time monitoring of the patient's physiological signs during anesthesia and comparison with the 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 solution, the specific process of monitoring the patient's physiological signs data during anesthesia in step S2 is: monitoring the patient's blood pressure, heart rate and pulse oxygen saturation in real time during anesthesia through a sphygmomanometer, an electrocardiogram monitor, and a pulse oximeter, and obtaining the patient's end-tidal carbon dioxide partial pressure by monitoring the patient's carbon dioxide waveform during anesthesia.

[0034] It should be noted that blood pressure, heart rate, pulse oxygen saturation ( ) and end-tidal carbon dioxide partial pressure ( ) is the core monitoring indicator of the patient's physiological signs during anesthesia, because it can comprehensively reflect the state of circulation, respiration and nerve inhibition, and is directly related to the depth of anesthesia and the effect of drugs: (1) Blood pressure: When anesthesia is too deep, the inhibitory effect of drugs on the cardiovascular system is enhanced, resulting in a decrease in blood pressure; when anesthesia is too shallow, pain or stress response activates the sympathetic nerves, causing an increase in blood pressure. (2) Heart rate: When anesthesia is too shallow, sympathetic nerve excitement causes an increase in heart rate; when anesthesia is too deep, increased vagal tone or myocardial inhibition can cause a slowing of the heart rate. (3) : reflects oxygenation status. When anesthesia is too deep, respiratory depression or airway obstruction may cause Decline; When anesthesia is too shallow, if the patient retains spontaneous breathing and has adequate oxygen supply, Usually normal or increasing. (4) : Monitor ventilation efficiency. When deep anesthesia causes respiratory depression, Increase; if pain stimulation triggers hyperventilation when anesthesia is too shallow, Through the comprehensive monitoring and analysis of 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 solution, the specific analysis process for determining whether the patient's physiological signs are abnormal in step S2 is as follows: the measured values of the patient's physiological signs during anesthesia are recorded as , Indicates the The number of the physiological sign data item, , extract the normal range of each physiological sign data stored in the database and record it as .

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

[0037] The abnormal scores of each physiological sign data Substitute into the analysis formula Calculate the patient's physiological sign abnormality coefficient ,in Indicates the preset The weight of each physiological sign data.

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

[0039] It should be noted that the weight of each physiological sign data item represents its relative importance, and the weight of each physiological sign data item is set based on the degree to which its stability affects the patient's physiological state. The greater the impact on the patient's physiological state, the higher the weight. Furthermore, the specific weight distribution can be adjusted according to the specific circumstances of anesthesia and the individual characteristics of the patient.

[0040] It should be noted that, in a specific embodiment, the normal ranges and weights of various physiological sign data of the patient during anesthesia are specifically shown in Table 1.

[0041] Table 1. Normal range and weight setting of physiological sign data

[0042]

[0043] S3: Generate a drug concentration adjustment plan based on a drug concentration-physiological sign correlation model established based on historical drug efficacy data, wherein the adjustment plan includes an adjustment trend and an adjustment amount.

[0044] As a preferred embodiment, the specific process of analyzing the adjustment trend of the drug concentration in step S3 is: comparing the various physiological sign data of the patient during anesthesia with the numerical representations of their physiological signs when the anesthesia is too deep and too shallow, respectively, to judge whether the patient is anesthetized too deeply or too shallowly. If the anesthesia is too deep, the drug concentration is too high, and the adjustment trend of the drug concentration is to decrease. If the anesthesia is too shallow, the drug concentration is too low, and the adjustment trend of the drug concentration is to increase.

[0045] It should be noted that when the patient's anesthesia is too deep, the patient's various physiological signs data will show their numerical representations when the anesthesia is too deep; when the patient's anesthesia is too shallow, the patient's various physiological signs data will show their numerical representations when the anesthesia is too shallow.

[0046] It should be noted that, in a specific embodiment, the numerical representations of physiological signs when anesthesia is too deep and when anesthesia is too shallow are shown in Table 2.

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

[0048]

[0049] As a preferred solution, the specific process of analyzing the dosage concentration adjustment amount in step S3 is: obtaining the overshoot of each physiological sign of the patient based on the comparison result of the physiological sign data of the patient during anesthesia with its normal range.

[0050] Based on historical efficacy data and combined with machine learning algorithms, a correlation model between drug concentration and physiological signs is established to obtain a quantitative mapping relationship between the change in drug concentration and the change in each physiological sign. The overshoot of each physiological sign of the patient is substituted into the quantitative mapping relationship to obtain the adjustment amount of the patient's drug concentration.

[0051] It should be noted that when the physiological sign data exceeds the upper limit of its normal range, the overshoot of the physiological sign represents the upward adjustment amount, which is the result of subtracting the upper limit value from the actual measured value; when the physiological sign data is lower than the lower limit of its normal range, the overshoot of the physiological sign represents the downward adjustment amount, which is the result of subtracting the actual measured value from the lower limit value.

[0052] It should be noted that the process of establishing the association model of drug concentration-physiological signs is divided into four steps: first, integrating the drug concentration records and corresponding physiological characteristic change data in the 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 establish a nonlinear mapping equation between the change in drug concentration and the change in physiological signs; then, through model verification, the quantitative weight relationship between each physiological sign and the drug efficacy is determined; finally, the real-time monitored patient physiological sign overshoot is input into the trained model, and the personalized drug concentration adjustment amount is obtained by reverse calculation, thereby realizing precise dosage control based on physiological sign feedback.

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

[0054] S4: Monitor the patient's anesthesia depth after implementing the drug concentration adjustment. Analyze the consistency of the anesthesia depth based on the proportion of time the anesthesia depth is within the target range and the number and magnitude of deviations to evaluate the effect of the drug concentration adjustment.

[0055] As a preferred solution, the specific analysis process for monitoring the patient's anesthesia depth in step S4 is: monitoring the patient's bispectral index after implementing the drug concentration adjustment, and extracting the anesthesia depth value corresponding to each bispectral index range stored in the database, and screening to obtain the patient's anesthesia depth after the 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 multiple advantages. First, BIS can reflect the functional state of the cerebral cortex in real time. By analyzing different EEG characteristics, such as frequency and amplitude, it produces a numerical value to quantify the depth of anesthesia, providing an intuitive understanding of the patient's degree of brain inhibition. Second, BIS has a good correlation with the blood concentration of anesthetic drugs and can more accurately reflect the patient's response to anesthetic drugs, helping to adjust drug dosages in a timely manner to avoid excessive or shallow anesthesia. Third, BIS is not affected by factors such as surgical procedures and muscle relaxants, has high specificity and sensitivity, and can stably and reliably reflect changes in the depth of anesthesia. In addition, BIS monitoring is simple to operate, acquiring data through electrodes attached to the patient's head. It is non-invasive to the patient and is easily applicable in clinical practice.

[0057] As a preferred solution, the specific process of analyzing the degree of consistency of anesthesia depth in step S4 is: setting a target range for the patient's anesthesia depth, obtaining the time percentage, number of deviations, and average deviation amplitude of the patient's anesthesia depth within the target range, and substituting them into the relationship function between the preset time percentage, number of deviations, and amplitude of the anesthesia depth within the target range and the degree of consistency of anesthesia depth, and outputting the degree of consistency of the patient's anesthesia depth.

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

[0059] It should be noted that the calculation formula Obtain the time percentage of the patient's anesthesia depth within the target range .

[0060] It should be noted that the calculation formula Get the average deviation of the patient's anesthesia depth ,in Indicates the value of each deviation of the anesthesia depth. Indicates the boundary value of the target range of anesthesia depth (take the nearest boundary), Indicates the number of deviations in anesthesia depth.

[0061] It should be noted that, in a specific embodiment, the relationship function between the proportion of time that the anesthesia depth is within the target range, the number and amplitude of deviations, and the degree of consistency of the anesthesia depth can be: ,in Indicates the degree of consistency of anesthesia depth, Indicates the number of anesthesia depth deviations, Indicates the impact factor corresponding to the number of unit deviations of the preset anesthesia depth, Indicates the threshold of the preset anesthesia depth deviation amplitude, They respectively represent the proportion of time that the preset anesthesia depth is within the target range, the number of anesthesia depth deviations, and the weights of the anesthesia depth deviation amplitude.

[0062] It should be noted that the weights in the anesthesia depth consistency calculation formula are based on clinical experience and research data. Clinicians and researchers comprehensively consider the impact of these factors on anesthesia effectiveness and patient safety. For example, if it is believed that maintaining anesthesia depth within the target range for a long time is crucial for the quality and safety of anesthesia for patients, then the weights will be assigned to the target depth. A relatively large value; if it is found that the anesthetic depth deviation frequently affects the anesthetic effect and patient status significantly, the value will be appropriately increased. When the anesthesia depth deviates greatly, it may cause serious adverse consequences. By continuously adjusting these weights and combining them with actual clinical effect feedback, the weight settings are optimized to accurately measure the degree of anesthesia depth. In a specific embodiment, The values are 0.4, 0.3, and 0.3 respectively.

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

[0064] It should be noted that the mapping relationship between the anesthesia depth consistency and the drug concentration adjustment effect coefficient is positively correlated, that is, the greater the anesthesia depth consistency, 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 degree of consistency of anesthesia depth, which is conducive to the subsequent optimization of the concentration adjustment mechanism based on feedback, thereby improving the safety and effectiveness of anesthesia.

[0066] The above formulas are all dimensionless and numerically calculated, and the preset parameters in the formulas are set by technicians in this field according to actual conditions.

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

[0068] Those skilled in the art will appreciate that the modules and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0070] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection 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 in the scope of protection of the present invention.

Claims

1. A method for analyzing and evaluating the concentration of an inhaled anesthetic drug, characterized in that: The steps include: S1: Analyze the patient's matching degree with historical anesthesia cases based on the patient's characteristic data including basic physiological data combined with fuzzy matching algorithm, and output the patient's initial drug concentration; S2: Real-time monitoring of the patient's physiological signs during anesthesia and comparison with the 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: generating a drug concentration adjustment plan based on a drug concentration-physiological sign correlation model established based on historical drug efficacy data, wherein the adjustment plan includes an adjustment trend and an adjustment amount; S4: Monitor the patient's anesthesia depth after implementing the drug concentration adjustment. Analyze the consistency of the anesthesia depth based on the proportion of time the anesthesia depth is within the target range and the number and magnitude of deviations to evaluate the effect of the drug concentration adjustment.

2. The method for analyzing and evaluating the concentration of an inhaled anesthetic drug according to claim 1, wherein: The specific process of analyzing the matching degree between the patient and the historical anesthesia cases in step S1 is as follows: Acquiring characteristic data of the patient, including basic physiological data, previous anesthesia sensitivity, and surgical information, and constructing a characteristic set of the patient; Extract the feature set and initial drug concentration of each individual in the historical anesthesia cases stored in the database; The patient's feature set is compared with the feature sets of each individual in the historical anesthesia case to obtain the deviation of each sub-item in the feature set, and the weight of each sub-item in the feature set stored in the database is extracted. Combined with the relationship function between the sub-item deviation and weight in the set feature set and the matching degree, the matching degree between the patient and each individual in the historical anesthesia case is analyzed.

3. The method for analyzing and evaluating the concentration of an inhaled anesthetic drug according to claim 1, wherein: The specific analysis process for obtaining the patient's initial drug concentration in step S1 is as follows: The matching degree of the patient and each individual in the historical anesthesia case is compared to obtain the individual with the highest matching degree with the patient, and the initial drug concentration of this individual is used as the initial drug concentration of the patient.

4. The method for analyzing and evaluating the concentration of an inhaled anesthetic drug according to claim 1, wherein: The specific process of monitoring the patient's physiological sign data during anesthesia in step S2 is: The patient's blood pressure, heart rate and pulse oxygen saturation are monitored in real time during anesthesia 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.

5. The method for analyzing and evaluating the concentration of an inhaled anesthetic drug according to claim 1, wherein: The specific analysis process of determining whether the patient's physiological signs are abnormal in step S2 is as follows: The measured values of the patient's physiological signs during anesthesia are recorded as , Indicates the The number of the physiological sign data item, , extract the normal range of each physiological sign data stored in the database and record it as ; By analyzing the formula Calculate the abnormal scores of each physiological sign data ; The abnormal scores of each physiological sign data Substitute into the analysis formula Calculate the patient's physiological sign abnormality coefficient ,in Indicates the preset The weight of each physiological sign data; The patient's physiological sign abnormality coefficient is compared with a preset physiological sign abnormality coefficient threshold. If the patient's physiological sign abnormality coefficient is greater than the preset physiological sign abnormality coefficient threshold, the patient's physiological sign is abnormal.

6. The method for analyzing and evaluating the concentration of an inhaled anesthetic drug according to claim 1, wherein: The specific process of analyzing the dosage concentration adjustment trend in step S3 is as follows: The data of various physiological signs of the patient during anesthesia are compared with the numerical representations of their physiological signs when the anesthesia is too deep and too shallow, respectively, to judge whether the patient is anesthetized too deeply or too shallowly. If the anesthesia is too deep, the drug concentration is too high, and the adjustment trend of the drug concentration is to decrease. If the anesthesia is too shallow, the drug concentration is too low, and the adjustment trend of the drug concentration is to increase.

7. The method for analyzing and evaluating the concentration of an inhaled anesthetic drug according to claim 1, wherein: The specific process of analyzing the dosage concentration adjustment amount in step S3 is as follows: According to the comparison results of the patient's physiological sign data during anesthesia and its normal range, the overshoot of each physiological sign of the patient is obtained; Based on historical efficacy data and combined with machine learning algorithms, a correlation model between drug concentration and physiological signs is established to obtain a quantitative mapping relationship between the change in drug concentration and the change in each physiological sign. The overshoot of each physiological sign of the patient is substituted into the quantitative mapping relationship to obtain the adjustment amount of the patient's drug concentration.

8. The method for analyzing and evaluating the concentration of an inhaled anesthetic drug according to claim 1, wherein: The specific analysis process of monitoring the patient's anesthesia depth in step S4 is: After the drug concentration adjustment is implemented, the patient's bispectral index is monitored, and the anesthesia depth values corresponding to each bispectral index range stored in the database are extracted to screen and obtain the patient's anesthesia depth after the drug concentration adjustment.

9. The method for analyzing and evaluating the concentration of an inhaled anesthetic drug according to claim 1, wherein: The specific process of analyzing the degree of anesthesia depth consistency in step S4 is as follows: Set the target range of the patient's anesthesia depth, obtain the time percentage, number of deviations, and average deviation amplitude of the patient's anesthesia depth within the target range, and substitute them into the relationship function between the preset time percentage, number and amplitude of deviations within the target range and the degree of consistency of the anesthesia depth, and output the degree of consistency of the patient's anesthesia depth.

10. The method for analyzing and evaluating the concentration of an inhaled anesthetic drug according to claim 1, wherein: The specific analysis process for evaluating the effect of drug concentration adjustment in step S4 is as follows: The target patient's anesthesia depth matching degree is substituted into the preset mapping relationship between the anesthesia depth matching degree and the drug administration concentration adjustment effect coefficient to obtain the target patient's drug administration concentration adjustment effect coefficient and provide feedback.

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