Anesthesia medication management system with biological recognition function

Abstract

The invention belongs to the technical field of medical informatization, and provides an anesthesia medication management system with biological recognition, comprising: a biological recognition module verifies the identities of medical staff and patients through a vein recognition technology; the multi-source data acquisition module acquires vital sign data and equipment parameters of a patient in real time to obtain multi-source data; the intelligent drug management module authorizes to store and take drugs according to the biological recognition result and generates an electronic anesthesia prescription; the processing decision module receives the multi-source data and generates a medicine adjusting instruction through a preset rule and an algorithm in combination with an electronic anesthesia prescription; the execution control module carries out secondary confirmation on the medicine adjusting instruction through biological recognition and then sends the medicine adjusting instruction to anesthetic infusion equipment to be executed; according to the method, through the electroencephalogram double-frequency index, the entropy index and the hemodynamic parameters, in combination with the anesthesia depth evaluation result and the personalized drug metabolism and effect model, the medication scheme is dynamically adjusted, the optimal infusion rate is generated, the anesthesia depth control is more accurate, and the fluctuation range is narrowed.

Description

Technical Field

[0001] The present invention belongs to the field of medical information technology, and in particular to an anesthetic medication management system with biometric recognition. Background Art

[0002] In modern medical practice, the management and control of anesthetic drugs relies on the experience and manual operation of doctors or anesthesiologists. The depth of anesthesia is usually regulated by manually adjusting the infusion rate and monitoring the patient's vital signs. Traditional anesthesia management systems mostly use basic monitoring equipment to monitor parameters such as blood pressure, heart rate, and oxygen saturation in real time. However, there are still many shortcomings in terms of drug safety and precise control of anesthesia depth.

[0003] First, the existing anesthetic drug management lacks an effective identity authentication mechanism, and mostly uses methods such as password input or fingerprint recognition, which are prone to identity information leakage or misidentification. It cannot meet the high security requirements for accurate identity verification of medical staff and patients during anesthesia operations, and it is difficult to effectively avoid medication errors caused by unauthorized operations or confusion of patient identities. Secondly, the assessment of anesthesia depth relies on a single parameter, fails to comprehensively consider the synergistic effects of EEG characteristics and hemodynamic parameters, and cannot fully reflect the patient's true anesthesia state, resulting in delayed or excessive medication adjustments and a large fluctuation range in anesthesia depth, affecting anesthesia quality and patient safety.

[0004] In addition, the medication decision-making process relies on manual adjustments by medical staff based on experience, and lacks an intelligent decision-making mechanism based on optimization algorithms. This not only increases the workload of medical staff, but also has low decision-making efficiency and is difficult to adapt to the rapid changes in patient status during surgery. Moreover, the safety protection mechanism of the existing system is mostly post-alarm, lacking real-time dynamic monitoring and active intervention of parameters such as drug dosage and patient vital signs. When drug overdose or abnormal patient vital signs occur, the medication operation cannot be frozen in time, posing a major safety hazard.

[0005] To this end, technicians in this field have proposed an anesthetic drug management system with biometrics, aiming to achieve precise and stable anesthesia depth control, thereby improving anesthesia safety and management efficiency, reducing operational errors and potential risks, and providing patients with a safer and more comfortable medical experience. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention provides an anesthetic medication management system with biometric recognition to solve the problems raised in the background technology.

[0007] An anesthetic drug management system with biometric identification, comprising:

[0008] Biometric module, used to verify the identity of medical staff and patients through vein recognition technology and obtain biometric results;

[0009] Multi-source data acquisition module, used to obtain real-time vital sign data and equipment parameters of patient monitors, ventilators, anesthesia machines, and intelligent infusion pumps to obtain multi-source data;

[0010] An intelligent medication management module, which authorizes access to medications based on biometric identification results and generates electronic anesthesia prescriptions;

[0011] a processing and decision-making module, configured to receive the multi-source data, and generate a drug adjustment instruction based on the electronic anesthesia prescription using preset rules and algorithms;

[0012] The execution control module is used to send the drug adjustment instruction to the anesthetic drug infusion device for execution after secondary confirmation through biometric identification.

[0013] Preferably, the biometric recognition module includes a vein recognition sensor and a biometric database. The vein recognition sensor collects venous blood vessel images of medical staff and patients, and completes identity verification by matching them with vein feature templates stored in the biometric database.

[0014] Preferably, the vital signs data collected by the multi-source data acquisition module include heart rate, blood pressure, blood oxygen saturation, body temperature, electrocardiogram and bispectral index; the collected equipment parameters include tidal volume, respiratory rate, end-tidal carbon dioxide partial pressure of the ventilator, inspired oxygen concentration, anesthetic gas concentration of the anesthesia machine, and the current infusion rate and remaining drug volume of the intelligent infusion pump; the multi-source data are transmitted in real time through the HL7 or DICOM standard interface.

[0015] Preferably, the intelligent drug management module includes:

[0016] a drug access authorization unit, configured to control the unlocking, drug taking, and drug returning operations of the anesthetic drug storage cabinet based on the biometric identification result;

[0017] The prescription generation unit is used to generate an electronic anesthesia prescription containing the type of drug, initial dose and administration time based on the patient's electronic medical record, surgery type and medical staff permissions confirmed by biometrics.

[0018] Preferably, the processing decision module includes:

[0019] A data preprocessing unit, configured to perform filtering, noise reduction and normalization on the multi-source data;

[0020] Anesthesia depth assessment unit, used to comprehensively assess the patient's anesthesia depth based on the bispectral index, entropy index and hemodynamic parameters to obtain anesthesia depth assessment results;

[0021] a pharmacokinetic-pharmacodynamic modeling unit, used to establish a personalized drug metabolism and effect model based on the patient's vital signs data;

[0022] A decision algorithm unit is used to generate a drug adjustment instruction based on preset rules and algorithms, combined with the electronic anesthesia prescription, the anesthesia depth assessment result and the personalized drug metabolism and effect model.

[0023] Preferably, the anesthesia depth assessment unit further comprises:

[0024] The EEG features were extracted based on the bispectral index and entropy index, and the EEG anesthesia index (AEI) was calculated using the following formula: t :

[0025]

[0026] Among them, B t is the bispectral index, the lower threshold is 40, the effective interval is [40,100], then the effective interval span is 60, E t Entropy index, with a cutoff value of 90 and a range upper limit of 100, ω1 and ω2 are weight coefficients corresponding to the bispectral index and entropy index respectively;

[0027] The hemodynamic characteristics are extracted based on the hemodynamic parameters, and the circulatory stability index (CSI) is calculated using the following formula: t :

[0028]

[0029] Among them, MAP t is the current mean arterial pressure, MAP base is baseline mean arterial pressure, HRV t is the current heart rate variability, HRV base is the baseline heart rate variability, λ is the blood pressure deviation penalty coefficient;

[0030] According to the electroencephalographic anesthesia index AEI t and Circulation Stability Index (CSI) t , use the following formula to comprehensively assess the patient's anesthesia depth:

[0031] ADS t =α·AEI t +β·CSI t (α+β=1)

[0032] Among them, ADS t is the anesthesia depth assessment result, α and β are the weight coefficients corresponding to the EEG anesthesia index and circulatory stability index, respectively.

[0033] Preferably, the decision algorithm unit further includes:

[0034] Using the electronic anesthesia prescription as the initial benchmark, combined with the real-time anesthesia depth assessment results and the personalized drug metabolism and effect model, at each time step t, the optimal infusion rate sequence u(t), u(t+1), ..., u(t+N-1) is obtained by solving the following optimization problem:

[0035]

[0036] Among them, E target is the target anesthesia depth obtained by the electronic anesthesia prescription, ADS(t+k|t) is the future anesthesia depth predicted based on the information at time t, N is the prediction time domain, u(t) is the infusion rate at time t, represents the minimization of infusion rate variations, is the weight factor, k is the future time offset starting from time t;

[0037] After solving the optimization problem, the immediate drug adjustment instructions are output as follows:

[0038] Δu(t)=u * (t)-u(t-1)

[0039] Among them, u * (t) is the first control input in the optimization sequence, i.e., the value of the optimal infusion rate at time t, and Δu(t) is the drug adjustment instruction sent to the executive control module.

[0040] Preferably, the medication adjustment instruction includes:

[0041] When the anesthesia depth assessment result shows that the anesthesia is too deep and the current drug concentration exceeds the maintenance concentration predicted by the personalized drug metabolism and effect model, generating a drug reduction instruction;

[0042] When the anesthesia depth assessment result shows that the anesthesia is insufficient and the current drug concentration is lower than the maintenance concentration predicted by the personalized drug metabolism and effect model, generating a medication instruction;

[0043] When a patient is detected to have a drug allergy or drug resistance, a medication replacement order is generated containing a drug replacement plan.

[0044] Preferably, the decision algorithm unit also presets safety boundary parameters, including upper and lower limits of drug dosage, danger thresholds of patient vital signs and safety range of equipment operating parameters. When the multi-source data reaches the safety boundary parameters, an early warning signal is generated and the current medication operation is frozen.

[0045] Preferably, the execution control module establishes a two-way communication connection with an anesthetic drug infusion device such as an intravenous infusion pump, a micro-injection pump, an inhalation anesthesia machine, and further includes:

[0046] After receiving the medication adjustment instruction, the identity of the operating medical staff is verified through the biometric recognition module;

[0047] According to the verified drug adjustment instruction, the anesthetic drug infusion device is controlled to perform dosage adjustment, infusion rate change or drug switching operation, and the infusion status data of the intelligent infusion pump is synchronously updated.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The present invention uses biometric technology to double-verify the identities of medical staff and patients to avoid operational errors and identity confusion, monitor safety boundary parameters in real time, and automatically issue an alarm and freeze the operation when the threshold is reached to prevent drug overdose or abnormal patient vital signs; and through the bispectral index, entropy index and hemodynamic parameters, combined with the results of anesthesia depth assessment and personalized drug metabolism and effect model, dynamically adjust the medication plan to generate the optimal infusion rate, so as to make the anesthesia depth control more precise and reduce the fluctuation range.

[0050] 2. The present invention automatically generates electronic prescriptions through the intelligent drug management module to simplify the process; realizes automatic control of infusion equipment through the execution control module, enables the system to process data in real time and generate adjustment instructions, reduces manual operations of medical staff, improves work efficiency, and realizes active safety protection by setting safety boundary parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a block diagram of the anesthesia medication management system of the present invention;

[0052] Figure 2 This is a flowchart of the anesthesia depth assessment unit of the present invention. DETAILED DESCRIPTION

[0053] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0054] Example 1: As shown in the attached Figure 1 As shown, the present invention provides an anesthetic medication management system with biometric identification, comprising:

[0055] The biometric module verifies the identities of medical staff and patients through vein recognition technology, generating biometric results. The module includes a vein recognition sensor and a biometric database. The vein recognition sensor captures images of the medical staff and patients' veins and matches them with vein feature templates stored in the biometric database to complete identity verification. This ensures that only authorized personnel can perform operations, preventing unauthorized access to medications or incorrect operations. Accurately confirming patient identities prevents medication errors caused by identity confusion, improving medication safety and operational traceability.

[0056] The multi-source data acquisition module is used to obtain the vital signs data and equipment parameters of the patient monitor, ventilator, anesthesia machine and intelligent infusion pump in real time to obtain multi-source data; the vital signs data include heart rate, blood pressure, blood oxygen saturation, body temperature, electrocardiogram and bispectral index; the equipment parameters include tidal volume, respiratory rate, end-tidal carbon dioxide partial pressure of the ventilator, inspired oxygen concentration, anesthetic gas concentration of the anesthesia machine, and the current infusion rate and remaining drug volume of the intelligent infusion pump; the multi-source data is transmitted in real time through the HL7 or DICOM standard interface.

[0057] The intelligent drug management module is used to authorize access to drugs based on biometric identification results and generate electronic anesthetic prescriptions. It also includes a drug access authorization unit, which is used to control the unlocking, drug retrieval and return operations of the anesthetic storage cabinet based on the biometric identification results.

[0058] The prescription generation unit is used to generate an electronic anesthesia prescription containing the type of drug, initial dose and administration time based on the patient's electronic medical record, surgery type and medical staff permissions confirmed by biometrics.

[0059] The intelligent drug management module effectively implements the safe management of narcotic drugs and prevents drug abuse or misuse. It also improves prescription efficiency and standardization by automatically generating personalized electronic prescriptions, providing an initial benchmark for subsequent medication adjustments.

[0060] The processing and decision-making module is used to receive multi-source data, combine it with the electronic anesthesia prescription, and generate medication adjustment instructions through preset rules and algorithms; the processing and decision-making module also includes:

[0061] Data preprocessing unit, used for filtering, denoising and normalizing multi-source data;

[0062] Anesthesia depth assessment unit is used to comprehensively assess the patient's anesthesia depth based on the bispectral index, entropy index and hemodynamic parameters to obtain anesthesia depth assessment results; the anesthesia depth assessment results accurately reflect the patient's anesthesia status;

[0063] The pharmacokinetic-pharmacodynamic modeling unit is used to establish personalized drug metabolism and effect models based on patients' vital signs data. By predicting the concentration changes and effects of drugs in patients, it can achieve personalized simulation of drug metabolism and improve the accuracy of medication prediction.

[0064] The decision algorithm unit is used to generate drug adjustment instructions based on preset rules and algorithms, combined with electronic anesthesia prescriptions, anesthesia depth assessment results and personalized drug metabolism and effect models, and preset safety boundary parameters.

[0065] As attached Figure 2 As shown, the anesthetic depth assessment unit also includes:

[0066] The EEG features were extracted based on the bispectral index and entropy index, and the EEG anesthesia index (AEI) was calculated using the following formula: t :

[0067]

[0068] Among them, B t is the bispectral index, the lower threshold is 40, the effective interval is [40,100], then the effective interval span is 60, E t Entropy index, its cutoff value is 90, the upper limit of the range is 100, ω1 and ω2 are the weight coefficients corresponding to the bispectral index and entropy index respectively;

[0069] The hemodynamic characteristics were extracted based on the hemodynamic parameters, and the circulatory stability index (CSI) was calculated using the following formula: t :

[0070]

[0071] Among them, MAP t is the current mean arterial pressure, MAP base is baseline mean arterial pressure, HRV t is the current heart rate variability, HRV base is the baseline heart rate variability, λ is the blood pressure deviation penalty coefficient;

[0072] According to the AEI t and Circulation Stability Index (CSI) t , use the following formula to comprehensively assess the patient's anesthesia depth:

[0073] ADS t =α·AEI t +β·CSI t (α+β=1)

[0074] Among them, ADS tis the anesthesia depth assessment result, α and β are the weight coefficients corresponding to the EEG anesthesia index and circulatory stability index, respectively.

[0075] Taking the electronic anesthesia prescription as the initial benchmark, combined with the real-time anesthesia depth assessment results and the personalized drug metabolism and effect model, at each time step t, the optimal infusion rate sequence u(t),u(t+1),...,u(t+N-1) is obtained by solving the following optimization problem:

[0076]

[0077] Among them, E target is the target anesthesia depth obtained by electronic anesthesia prescription, ADS(t+k|t) is the future anesthesia depth predicted based on the information at time t, N is the prediction time domain, u(t) is the infusion rate at time t, represents the minimization of infusion rate variations, is the weight factor, k is the future time offset starting from time t;

[0078] After solving the optimization problem, the immediate drug adjustment instructions are output as follows:

[0079] Δu(t)=u * (t)-u(t-1)

[0080] Among them, u * (t) is the first control input in the optimization sequence, i.e., the value of the optimal infusion rate at time t, and Δu(t) is the drug adjustment instruction sent to the executive control module.

[0081] Medication adjustment instructions include:

[0082] When the anesthesia depth assessment results show that the anesthesia is too deep and the current drug concentration exceeds the maintenance concentration predicted by the personalized drug metabolism and effect model, a drug reduction instruction is generated;

[0083] When the anesthesia depth assessment result shows that anesthesia is insufficient and the current drug concentration is lower than the maintenance concentration predicted by the personalized drug metabolism and effect model, a dosing instruction is generated;

[0084] When a patient is detected to have a drug allergy or drug resistance, a medication replacement order is generated containing a drug replacement plan.

[0085] The decision algorithm unit also presets safety boundary parameters, including upper and lower limits of drug dosage, dangerous thresholds of patient vital signs, and safe range of equipment operating parameters. When multi-source data reaches the safety boundary parameters, an early warning signal is generated and the current medication operation is frozen.

[0086] By solving optimization problems, the optimal infusion rate is obtained, and instructions for adding, reducing, or changing medications are generated. At the same time, when the data reaches the safety boundary, an early warning is issued and the operation is frozen, achieving accurate and safe medication adjustments.

[0087] The execution control module is used to send the drug adjustment instruction to the anesthetic drug infusion device for execution after the drug adjustment instruction is confirmed by biometric identification. The execution control module establishes a two-way communication connection with the anesthetic drug infusion device such as the intravenous infusion pump, micro-injection pump, and inhalation anesthesia machine, and also includes:

[0088] After receiving the medication adjustment instruction, the identity of the operating medical staff is verified through the biometric module;

[0089] According to the verified drug adjustment instructions, the anesthetic drug infusion device is controlled to perform dosage adjustment, infusion rate change or drug switching operations, and the infusion status data of the intelligent infusion pump is updated synchronously.

[0090] The drug adjustment instruction is sent to the anesthetic drug infusion device for execution after biometric secondary confirmation, and the infusion status data is updated to ensure the accurate execution of the medication instruction. The secondary identity verification further guarantees the operation safety, synchronizes the device status in real time, and realizes the closed-loop control of the medication process.

[0091] Example 2: This example is basically the same as the previous example, except that it further includes a human-computer interaction module, which is configured as follows:

[0092] Displays patients' real-time vital signs, anesthesia depth assessment results, electronic anesthesia prescriptions, and medication adjustment suggestions;

[0093] Receive manual intervention instructions from medical staff after biometric verification, and their manual intervention instructions take precedence over medication adjustment instructions generated by the decision-making module;

[0094] Record all medication operation logs and biometric verification information to form a traceable electronic audit trail.

[0095] The human-computer interaction module integrates a visual interface with an anesthesia depth trend graph, a drug concentration-effect curve graph, and a safety boundary warning indicator light, and supports interactive operations through a touch screen or voice commands.

[0096] It is important to note that the construction and arrangement of the present application shown in a plurality of different exemplary embodiments are merely illustrative. Although only a few embodiments are described in detail in this disclosure, it will be readily understood by those who consult this disclosure that many modifications are possible without departing substantially from the novel teachings and advantages of the subject matter described in this application. Other substitutions, modifications, changes, and omissions may be made in the design, operating conditions, and arrangement of the exemplary embodiments without departing from the scope of the present invention. Therefore, the present invention is not limited to specific embodiments, but extends to a variety of modifications that still fall within the scope of the appended claims.

[0097] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment (i.e., those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention) may not be described.

[0098] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A biometric anesthesia medication management system, characterized in that: include: Biometric module, used to verify the identity of medical staff and patients through vein recognition technology and obtain biometric results; Multi-source data acquisition module, used to obtain real-time vital sign data and equipment parameters of patient monitors, ventilators, anesthesia machines, and intelligent infusion pumps to obtain multi-source data; An intelligent medication management module, which authorizes access to medications based on biometric identification results and generates electronic anesthesia prescriptions; a processing and decision-making module, configured to receive the multi-source data, and generate a drug adjustment instruction based on the electronic anesthesia prescription using preset rules and algorithms; The execution control module is used to send the drug adjustment instruction to the anesthetic drug infusion device for execution after secondary confirmation through biometric identification.

2. The biometric anesthesia medication management system according to claim 1, characterized in that: The biometric recognition module includes a vein recognition sensor and a biometric database. The vein recognition sensor collects venous blood vessel images of medical staff and patients and completes identity authentication by matching them with vein feature templates stored in the biometric database.

3. The biometric anesthesia medication management system according to claim 1, characterized in that: The vital sign data collected by the multi-source data acquisition module include heart rate, blood pressure, blood oxygen saturation, body temperature, electrocardiogram and bispectral index; the collected equipment parameters include tidal volume, respiratory rate, end-tidal carbon dioxide partial pressure of the ventilator, inspired oxygen concentration, anesthetic gas concentration of the anesthesia machine, and the current infusion rate and remaining drug volume of the intelligent infusion pump; the multi-source data are transmitted in real time through the HL7 or DICOM standard interface.

4. The anesthetic drug management system with biometric identification as claimed in claim 1, characterized in that: The intelligent drug management module includes: a drug access authorization unit, configured to control the unlocking, drug taking, and drug returning operations of the anesthetic drug storage cabinet based on the biometric identification result; The prescription generation unit is used to generate an electronic anesthesia prescription containing the type of drug, initial dose and administration time based on the patient's electronic medical record, surgery type and medical staff permissions confirmed by biometrics.

5. The anesthetic drug management system with biometric identification as claimed in claim 1, characterized in that: The processing decision module includes: A data preprocessing unit, configured to perform filtering, noise reduction and normalization on the multi-source data; Anesthesia depth assessment unit, used to comprehensively assess the patient's anesthesia depth based on the bispectral index, entropy index and hemodynamic parameters to obtain anesthesia depth assessment results; a pharmacokinetic-pharmacodynamic modeling unit, used to establish a personalized drug metabolism and effect model based on the patient's vital signs data; A decision algorithm unit is used to generate a drug adjustment instruction based on preset rules and algorithms, combined with the electronic anesthesia prescription, the anesthesia depth assessment result and the personalized drug metabolism and effect model.

6. The anesthetic drug management system with biometric identification as claimed in claim 5, characterized in that: The anesthesia depth assessment unit further comprises: The EEG features were extracted based on the bispectral index and entropy index, and the EEG anesthesia index (AEI) was calculated using the following formula: t : Among them, B t is the bispectral index, the lower threshold of which is 40 and the effective interval is [40,100]. The effective interval span is 60, E t Entropy index, with a cutoff value of 90 and a range upper limit of 100, ω1 and ω2 are weight coefficients corresponding to the bispectral index and entropy index respectively; The hemodynamic characteristics are extracted based on the hemodynamic parameters, and the circulatory stability index (CSI) is calculated using the following formula: t : Among them, MAP t is the current mean arterial pressure, MAP base is baseline mean arterial pressure, HRV t is the current heart rate variability, HRV base is the baseline heart rate variability, λ is the blood pressure deviation penalty coefficient; According to the electroencephalographic anesthesia index AEI t and Circulation Stability Index (CSI) t , use the following formula to comprehensively assess the patient's anesthesia depth: ADS t =a·AEI t +β·CSI t (a+b=1) Among them, ADS t is the anesthesia depth assessment result, α and β are the weight coefficients corresponding to the EEG anesthesia index and circulatory stability index, respectively.

7. The anesthetic drug management system with biometric identification as claimed in claim 5, characterized in that: The decision algorithm unit also includes: Using the electronic anesthesia prescription as the initial benchmark, combined with the real-time anesthesia depth assessment results and the personalized drug metabolism and effect model, at each time step t, the optimal infusion rate sequence u(t), u(t+1), ..., u(t+N-1) is obtained by solving the following optimization problem: Among them, E target is the target anesthesia depth obtained by the electronic anesthesia prescription, ADS(t+k|t) is the future anesthesia depth predicted based on the information at time t, N is the prediction time domain, u(t) is the infusion rate at time t, represents the minimization of infusion rate variations, is the weight factor, k is the future time offset starting from time t; After solving the optimization problem, the immediate drug adjustment instructions are output as follows: Δu(t)=u * (t)-u(t-1) Among them, u * (t) is the first control input in the optimization sequence, i.e., the value of the optimal infusion rate at time t, and Δu(t) is the drug adjustment instruction sent to the executive control module.

8. The anesthetic drug management system with biometric identification according to claim 7, characterized in that: The medication adjustment instructions include: When the anesthesia depth assessment result shows that the anesthesia is too deep and the current drug concentration exceeds the maintenance concentration predicted by the personalized drug metabolism and effect model, generating a drug reduction instruction; When the anesthesia depth assessment result shows that the anesthesia is insufficient and the current drug concentration is lower than the maintenance concentration predicted by the personalized drug metabolism and effect model, generating a medication instruction; When a patient is detected to have a drug allergy or drug resistance, a medication replacement order is generated containing a drug replacement plan.

9. The anesthetic medication management system with biometric identification according to claim 5, characterized in that: The decision algorithm unit also presets safety boundary parameters, including upper and lower limits of drug dosage, danger thresholds of patient vital signs, and safety ranges of equipment operating parameters. When the multi-source data reaches the safety boundary parameters, an early warning signal is generated and the current medication operation is frozen.

10. The anesthetic medication management system with biometric identification according to claim 1, characterized in that: The execution control module establishes a two-way communication connection with an anesthetic drug infusion device such as an intravenous infusion pump, a micro-injection pump, an inhalation anesthesia machine, and further includes: After receiving the medication adjustment instruction, the identity of the operating medical staff is verified through the biometric recognition module; According to the verified drug adjustment instruction, the anesthetic drug infusion device is controlled to perform dosage adjustment, infusion rate change or drug switching operation, and the infusion status data of the intelligent infusion pump is synchronously updated.

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