An adaptive dose control system for acquiring radiation-resistant drug dose data.

Abstract

This invention discloses an adaptive dosing control system for anti-radiation drug dosage data acquisition, comprising a real-time physiological parameter acquisition module, a transport environment data capture module, a multi-scale pharmacokinetics modeling module, a population Bayesian optimization calculation module, an anti-radiation drug dosage adjustment module, and a system parameter dynamic update module. These modules work collaboratively: the former collects physiological data from the injured person and transport environment data, inputting this data into the multi-scale pharmacokinetics modeling module to construct a cross-scale drug metabolism model; the population Bayesian optimization calculation module performs optimization calculations based on population data; the anti-radiation drug dosage adjustment module adjusts the dosage accordingly; and the system parameter dynamic update module continuously corrects the parameters. This system achieves cross-scale analysis, fusion of population and individual data, and dynamic adaptation, ensuring accurate and appropriate drug dosage during transport and providing a guarantee for the treatment of the injured.

Description

Technical Field

[0001] This invention relates to the field of anti-radiation drug dose data acquisition, and more particularly to an adaptive dose control system for anti-radiation drug dose data acquisition. Background Technology

[0002] In emergencies such as nuclear accidents and radiation leaks, radiation-injured patients need to be promptly transferred to specialized medical institutions for treatment. Controlling the dosage of anti-radiation medications during transport is crucial for ensuring treatment effectiveness and minimizing adverse reactions. The transport environment is complex and variable, and the patient's physiological state changes dynamically over time. Traditional fixed-dosing methods are ill-suited to individual differences and environmental fluctuations, potentially leading to insufficient or excessive medication. Therefore, it is necessary to construct an adaptive control system capable of real-time acquisition of physiological and environmental data and dynamic adjustment of medication dosage. Through multi-scale modeling and population optimization algorithms, precise control of anti-radiation medication dosages during transport can be achieved.

[0003] Existing technologies have two significant drawbacks: First, they lack cross-scale integration capabilities. Most systems adjust dosage based solely on drug concentration data at the blood or tissue scale, without considering the interaction between drugs and targets at the cellular level or differences in tissue metabolism. This leads to dosage calculations ignoring the microscopic drug effect mechanism and failing to reflect the true efficacy. Second, they do not effectively integrate population data with individual dynamic characteristics. Traditional methods either rely on fixed population parameters, resulting in poor individual adaptability, or they adjust based solely on historical data from a single injured person, failing to utilize population pharmacokinetic laws to correct for individual differences. This makes them prone to dosage adjustment lags or biases when there are sudden changes in the transport environment. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides an adaptive dose control system for acquiring radiation-resistant drug dose data.

[0005] The technical solution adopted in this invention is an adaptive dose control system for anti-radiation drug dose data acquisition, comprising: a real-time physiological parameter acquisition module, a transport environment data capture module, a multi-scale pharmacokinetics modeling module, a population Bayesian optimization calculation module, an anti-radiation drug dose adjustment module, and a system parameter dynamic update module.

[0006] The output of the real-time physiological parameter acquisition module is connected to the first input of the multi-scale pharmacokinetics modeling module; the output of the transport environment data capture module is connected to the second input of the multi-scale pharmacokinetics modeling module; the output of the multi-scale pharmacokinetics modeling module is connected to the input of the population Bayesian optimization calculation module; the output of the population Bayesian optimization calculation module is connected to the input of the anti-radiation drug dosage adjustment module; the feedback of the anti-radiation drug dosage adjustment module is connected to the input of the system parameter dynamic update module; and the output of the system parameter dynamic update module is connected to the real-time physiological parameter acquisition module, the transport environment data capture module, the multi-scale pharmacokinetics modeling module, the population Bayesian optimization calculation module, and the anti-radiation drug dosage adjustment module, respectively. The system includes a parameter configuration module for radiation drug dosage adjustment; a real-time physiological parameter acquisition module to obtain data on the patient's heart rate, blood pressure, blood drug concentration, and radiation exposure dose; a transport environment data capture module to obtain data on the vibration frequency, temperature, humidity, and altitude of the transport vehicle; a multi-scale pharmacokinetic modeling module to construct a cross-tissue and cellular pharmacokinetic model based on the above physiological parameters and environmental data; a population Bayesian optimization calculation module to perform optimization calculations using the model output results combined with population pharmacokinetic data; an anti-radiation drug dosage adjustment module to adjust the drug infusion rate and single dose based on the optimization calculation results; and a system parameter dynamic update module to update the calculation parameters of each module based on the changes in physiological parameters after dosage adjustment.

[0007] Furthermore, when the multi-scale pharmacokinetic modeling module and the population Bayesian optimization calculation module work together, the following model formula is used to describe the relationship between the metabolic rate and dose of the drug during transport: ,in, Let t be the concentration of anti-radiation drug in the blood of the wounded. For transit time, This is the drug absorption rate constant. This is the drug elimination rate constant. To transport environmental radiation intensity, The effective radiation protection threshold for drugs, The internal volume of the transport carrier. For ambient humidity;

[0008] When dealing with confounding factors, the population Bayesian optimization calculation module uses the following model formula: ,in, Let be the drug metabolism parameters of the j-th injured person in the i-th group. The mean of the group parameters, This is a group-level random effect that follows a standard normal distribution. For the standard deviation of the population parameter, It represents a random effect at the individual level, following a normal distribution with a mean of 0.

[0009] Furthermore, the anti-radiation drug dosage adjustment module calculates the real-time drug dosage based on the drug distribution coefficient output by the multi-scale pharmacokinetic modeling module and the dosage optimization value obtained by the population Bayesian optimization calculation module, using the following model formula: ,in, This is a single-dose administration. To achieve the target blood drug concentration, This represents the current blood drug concentration. The apparent volume of distribution of the drug. The altitude influence coefficient is... This is the current altitude. For drug bioavailability, This is the temperature influence coefficient. The ambient temperature during transport;

[0010] The transport environment data acquisition module incorporates vibration frequency data into the dose adjustment correction, using the following formula: ,in, This is the vibration-corrected dosage. This is the vibration influence coefficient. The vibration frequency of the transport carrier.

[0011] Furthermore, the multi-scale pharmacokinetic modeling module describes the drug-target binding process at the cellular scale, with the following model formula: ,in, This refers to the concentration of the drug-target complex. This refers to the extracellular drug concentration. The total concentration at the target site. The binding rate constant, The dissociation rate constant is Radiation dose influencing factor, The cumulative radiation exposure dose for the injured;

[0012] The population Bayesian optimization calculation module uses the complex concentration data to estimate population parameters. The estimation formula is as follows: ,in, Parameters for given observation data y The posterior probability density, Let be the likelihood function. For parameters The prior distribution, For hyperparameters The marginal distribution.

[0013] Furthermore, based on the execution results of the anti-radiation drug dosage adjustment module, the system parameter dynamic update module updates the scale transformation coefficients of the multi-scale pharmacokinetics modeling module using the following model formula: ,in, These are the updated scale transformation coefficients. These are the initial scale transformation coefficients. The update rate constant is... For reference blood drug concentration, The integral of the difference between the blood drug concentration and the reference concentration from time 0 to time t;

[0014] The prior distribution parameters of the population Bayesian optimization computation module are updated using the following formula: ,in, The updated population parameter mean. The initial population parameter mean, These are the weighting coefficients. For sample size, For the i-th observation, Let be the i-th predicted value.

[0015] Furthermore, the data transmission between the real-time physiological parameter acquisition module and the multi-scale pharmacokinetic modeling module adopts a dynamic sampling interval, and the interval time is calculated using the following formula: ,in, This is the current sampling interval. Based on the sampling interval, This is the sampling adjustment coefficient. This represents the absolute value of the rate of change in blood drug concentration.

[0016] The infusion rate adjustment formula for the anti-radiation drug dosage adjustment module is as follows: ,in, This is the current drug infusion rate. Based on the infusion rate, To achieve the maximum safe blood drug concentration, To achieve the minimum effective blood drug concentration, Humidity influence coefficient This refers to ambient humidity.

[0017] Furthermore, the multi-scale pharmacokinetic modeling module includes: a tissue-scale drug distribution calculation unit, a cell-scale drug transport simulation unit, and a cross-scale parameter mapping unit. The tissue-scale drug distribution calculation unit receives blood flow velocity and vascular density data output from the real-time physiological parameter acquisition module, and combines it with temperature data output from the transport environment data capture module. By calculating the product of blood perfusion rate and drug distribution coefficient in different tissues and organs, the initial distribution amount of drug in each tissue is obtained. The cell-scale drug transport simulation unit simulates the process by which drug molecules enter the cell through passive diffusion and active transport and bind to the target, based on cell membrane permeability and intracellular target concentration data, and outputs the dynamic change curve of the drug-target complex. The cross-scale parameter mapping unit converts the drug concentration data calculated at the tissue scale into the boundary conditions of the cell-scale model, and feeds back the drug metabolism rate simulated at the cell scale to the tissue-scale model, performing coupled calculations of the two scale models.

[0018] Furthermore, the population Bayesian optimization calculation module includes: a population data stratification processing unit, a prior distribution construction unit, a posterior probability calculation unit, and an optimization parameter extraction unit. The population data stratification processing unit stratifies the received multi-scale pharmacokinetic modeling module output data according to the patient's age, weight, and radiation exposure level, and calculates the mean, standard deviation, and coefficient of variation of each stratum. The prior distribution construction unit sets a prior probability density function conforming to a normal-gamma distribution for the drug metabolism parameters based on the stratified data characteristics, where the mean parameter is associated with the stratified mean, and the variance parameter is associated with the stratified standard deviation. The posterior probability calculation unit uses the Markov chain Monte Carlo method, combined with real-time collected blood drug concentration observation data, to calculate the posterior probability distribution of each drug metabolism parameter. The optimization parameter extraction unit selects the parameter interval corresponding to a cumulative probability of 95% from the posterior probability distribution, and takes the midpoint of the interval as the optimal parameter value for the population Bayesian optimization calculation.

[0019] Furthermore, the anti-radiation drug dosage adjustment module includes: a dosage requirement calculation unit, a dosing method selection unit, and an infusion accuracy control unit. The dosage requirement calculation unit receives the optimal parameters output by the population Bayesian optimization calculation module, and combines them with the current blood drug concentration and radiation exposure dose data output by the physiological parameter acquisition module in real time. By calculating the difference between the target blood drug concentration and the current blood drug concentration, and the drug increment required for radiation protection, the total dosage requirement value is obtained. The dosing method selection unit selects the continuous infusion mode based on the vibration frequency and bump level data output by the transport environment data acquisition module. When the vibration frequency is lower than the threshold, the continuous infusion mode is selected. When the vibration frequency is higher than the threshold, the intermittent pulse dosing mode is switched, and the pulse interval and single pulse dose are adjusted at the same time. The infusion accuracy control unit compensates for the infusion flow fluctuation caused by the vibration of the transport carrier by real-time monitoring of the pressure change of the infusion pipeline, and corrects the deviation between the actual dosage and the theoretical dosage within 10 seconds.

[0020] Beneficial Effects: This invention proposes an adaptive dose control system for anti-radiation drug dosage data acquisition. Addressing the lack of cross-scale integration capabilities in existing technologies, the system acquires data such as heart rate and blood pressure from the injured person through a real-time physiological parameter acquisition module, and collects information such as vibration frequency and temperature from a transport environment data capture module. This comprehensive input into a multi-scale pharmacokinetic modeling module constructs a pharmacokinetic model spanning tissue and cell scales, enabling drug metabolism analysis from macroscopic to microscopic levels, more realistically reflecting drug efficacy, and overcoming the limitations of single-scale analysis. Addressing the deficiency in existing technologies that fail to effectively integrate population data with individual dynamic characteristics, the population Bayesian optimization calculation module utilizes multi-scale model results combined with population pharmacokinetic data for optimization calculation. The anti-radiation drug dosage adjustment module adjusts the infusion rate and dosage based on the optimization results. The system parameter dynamic update module continuously corrects the calculation parameters of each module based on the adjusted physiological parameter changes. This approach draws on both population patterns and individual real-time states, avoiding problems of poor individual adaptability or adjustment lag, ensuring that the anti-radiation drug dosage remains accurate and appropriate during transport, providing reliable drug dosage control for the injured person. Attached Figure Description

[0021] Figure 1 This is a system unit composition diagram of the present invention;

[0022] Figure 2 This is a flowchart of the system operation of the present invention. Detailed Implementation

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] like Figure 1 As shown, an adaptive dose control system for anti-radiation drug dose data acquisition includes: a real-time physiological parameter acquisition module, a transport environment data capture module, a multi-scale pharmacokinetics modeling module, a population Bayesian optimization calculation module, an anti-radiation drug dose adjustment module, and a system parameter dynamic update module.

[0025] The output of the real-time physiological parameter acquisition module is connected to the first input of the multi-scale pharmacokinetics modeling module; the output of the transport environment data capture module is connected to the second input of the multi-scale pharmacokinetics modeling module; the output of the multi-scale pharmacokinetics modeling module is connected to the input of the population Bayesian optimization calculation module; the output of the population Bayesian optimization calculation module is connected to the input of the anti-radiation drug dosage adjustment module; the feedback of the anti-radiation drug dosage adjustment module is connected to the input of the system parameter dynamic update module; and the output of the system parameter dynamic update module is connected to the real-time physiological parameter acquisition module, the transport environment data capture module, the multi-scale pharmacokinetics modeling module, the population Bayesian optimization calculation module, and the anti-radiation drug dosage adjustment module, respectively. The system includes a parameter configuration module for radiation drug dosage adjustment; a real-time physiological parameter acquisition module to obtain data on the patient's heart rate, blood pressure, blood drug concentration, and radiation exposure dose; a transport environment data capture module to obtain data on the vibration frequency, temperature, humidity, and altitude of the transport vehicle; a multi-scale pharmacokinetic modeling module to construct a cross-tissue and cellular pharmacokinetic model based on the above physiological parameters and environmental data; a population Bayesian optimization calculation module to perform optimization calculations using the model output results combined with population pharmacokinetic data; an anti-radiation drug dosage adjustment module to adjust the drug infusion rate and single dose based on the optimization calculation results; and a system parameter dynamic update module to update the calculation parameters of each module based on the changes in physiological parameters after dosage adjustment.

[0026] Specifically, the real-time physiological parameter acquisition module is the core component of the system for obtaining basic physiological information of the injured. Its technical parameters cover key indicators such as acquisition frequency, data accuracy, and monitoring range. The acquisition frequency is set to once per second to ensure the capture of subtle fluctuations in parameters such as heart rate and blood pressure; the data accuracy is controlled within ±2% to ensure the reliability of data such as blood drug concentration and radiation exposure dose; the monitoring range covers heart rate 60-180 beats / minute, blood pressure 60-180 mmHg, blood drug concentration 0-50 mg / L, and radiation exposure dose 0-1000 mSv, adapting to the physiological states of injured patients with different injuries. The significance of this module lies in providing real-time and accurate basic data for subsequent drug metabolism modeling and dosage optimization, which is a prerequisite for achieving adaptive dosage control. The specific implementation process of this module is as follows: Heart rate signals are collected by attaching electrode pads to the injured person's skin; blood pressure is measured and recorded every 10 seconds using a non-invasive blood pressure monitor; blood samples are drawn every 5 minutes using a venous blood sampling device, and blood drug concentration data is obtained by analyzing the samples using a portable blood drug concentration detector; simultaneously, a radiation dosimeter worn on the injured person's chest continuously monitors radiation exposure dose, updating the data every 30 seconds. All collected data is converted from analog to digital and stored in the module's built-in cache, which has a capacity of 10GB and can continuously store data for 72 hours. Data is transmitted in real time to the multi-scale pharmacokinetic modeling module via wired transmission at a transmission rate of 10Mbps to ensure data real-time performance and integrity. Encryption algorithms are used during transmission to ensure data security and prevent information leakage.

[0027] Specifically, the transport environment data capture module is mainly used to collect data on external environmental factors affecting drug metabolism. Its technical parameters include sensor type, measurement range, and response time. The temperature and humidity sensors used have a measurement range of -10℃ to 50℃ and humidity of 20% to 95%, with measurement accuracies of ±0.5℃ and ±3%, respectively. The vibration frequency sensor can monitor vibration signals from 0 to 50Hz, with a measurement error not exceeding ±0.1Hz. The altimeter has a measurement range of -500m to 5000m and an accuracy of ±5m. The significance of this module is to provide environmental impact parameters for drug metabolism models, enabling the models to more accurately reflect the impact of complex environments during transport on drug metabolism and improve the adaptability of dose adjustment. The specific implementation process of this module is as follows: Multiple sensors are installed at different locations on the transport vehicle. The temperature and humidity sensors are installed near the patient's head and the drug storage area, collecting data every 2 minutes. The vibration frequency sensor is fixed at the bottom of the transport vehicle to monitor vibration in real time, recording the vibration frequency value every 0.5 seconds. The altimeter is installed at the top of the transport vehicle, updating the altitude data every 1 minute. The analog signals acquired by the sensors are processed by the signal conditioning circuit and converted into digital signals. These signals are then transmitted to the data processing unit via a wireless transmission module with a range of up to 50 meters and a transmission rate of 2 Mbps. The data processing unit verifies and filters the received data, removing any abnormal data before packaging the valid data and sending it to the multi-scale pharmacokinetic modeling module. Each data packet is 1 KB in size, ensuring efficient and stable data transmission.

[0028] Specifically, the multi-scale pharmacokinetic modeling module is crucial for achieving accurate drug metabolism analysis. Its technical parameters include model scale range, computational accuracy, and data processing capabilities. The model covers both tissue scale (such as major organs like the heart, liver, and kidneys) and cellular scale, with a spatial resolution of 1 mm at the tissue scale and 1 μm at the cellular scale. Computational accuracy is controlled within ±5%, accurately simulating the absorption, distribution, metabolism, and excretion of drugs in vivo. The data processing capability is 100 MB per second, meeting the needs of real-time modeling. The significance of this module lies in overcoming the limitations of traditional single-scale models, comprehensively reflecting the metabolic laws of drugs from both macroscopic and microscopic perspectives, and providing a scientific theoretical basis for dosage optimization. The specific implementation process of this module is as follows: After receiving data from the real-time physiological parameter acquisition module and the transport environment data capture module, the data is first converted and standardized to meet the model's input requirements. Then, a drug distribution model is constructed based on the tissue scale. According to parameters such as blood perfusion rate and drug distribution coefficient of each organ, the concentration distribution of the drug in different tissues is calculated, and the tissue-scale drug concentration data is updated every 5 minutes. Simultaneously, at the cellular scale, based on data such as cell membrane permeability and intracellular target concentration, the process of drug molecules entering cells and binding to targets is simulated, with cellular-scale drug-target complex concentration data updated every minute. Through cross-scale parameter mapping, tissue-scale drug concentration data is converted into boundary conditions for the cellular-scale model, while cellular-scale metabolic rates are fed back to the tissue-scale model, achieving coupled computation between the two scale models. The calculation results are sent in real-time to the population Bayesian optimization module, with each transmission consisting of 500KB of data.

[0029] Specifically, the population Bayesian optimization computation module plays a crucial role in dose optimization. Its technical parameters include population data size, number of optimization iterations, and computational efficiency. The population data size encompasses pharmacokinetic data from over 1000 casualties of different genders, ages, and weights, ensuring the representativeness of population patterns. The number of optimization iterations is set to 50-100, improving computational efficiency while maintaining optimization accuracy. The computational efficiency is such that each iteration takes no more than 10 seconds, meeting the needs of real-time optimization. The significance of this module lies in fully utilizing population pharmacokinetic data, combined with individual real-time physiological and drug metabolism data, to achieve personalized dose optimization, improving the accuracy and adaptability of dose adjustment. The specific implementation process of this module is as follows: After receiving drug metabolism data output from the multi-scale pharmacokinetic modeling module, a pre-set population pharmacokinetic database is loaded. This database contains drug metabolism parameters under different radiation exposure levels and physiological states. Then, individual real-time data is matched with the population data, and a subset of 50-100 cases similar to the current casualty characteristics is selected. An objective function is constructed based on Bayes' theorem, aiming to maximize drug efficacy and minimize adverse reactions. The prior distribution is determined by combining population subset data and individual real-time data. The posterior distribution is updated iteratively. In each iteration, the next dose point to be evaluated is selected based on the current posterior distribution, the objective function value for that dose point is calculated, and the posterior distribution is updated. After 50-100 iterations, the optimal drug dosage parameters, including single-dose dosage and infusion rate, are obtained. The calculation results are sent to the anti-radiation drug dosage adjustment module. Each data transmission contains 10 parameters and has a data volume of 100 bytes.

[0030] Specifically, the anti-radiation drug dosage adjustment module is the core component for dose adjustment. Its technical parameters include adjustment range, adjustment accuracy, and response time. The adjustment range for a single dose is 1-50 mg, with an adjustment accuracy of ±0.1 mg; the adjustment range for the infusion rate is 0.1-5 mg / h, with an adjustment accuracy of ±0.01 mg / h; the response time is no more than 2 seconds, enabling rapid response to optimized calculation results. The significance of this module lies in translating the optimized dosage parameters into actual drug administration, achieving precise drug dosage adjustment, and ensuring that the drug remains at an appropriate concentration level throughout transport. The specific implementation process of this module is as follows: After receiving the optimal dosage parameters sent by the population Bayesian optimization calculation module, the parameters are first analyzed and verified to confirm their rationality and effectiveness. Then, based on the single dose and infusion rate parameters, the drive motor of the infusion pump is controlled. The infusion pump is driven by a stepper motor with a step angle of 1.8 degrees. The dosage is precisely controlled by controlling the number of motor rotation steps, with each step corresponding to a dosage of 0.001 mg. During drug administration, the pressure and flow rate of the infusion tubing are monitored in real time. The pressure monitoring range is 0-50 kPa with an accuracy of ±0.5 kPa; the flow rate monitoring range is 0-10 mg / h with an accuracy of ±0.05 mg / h. If any abnormal pressure or flow rate is detected, drug administration is immediately stopped and an alarm signal is issued. Simultaneously, the abnormal information is fed back to the system parameter dynamic update module. After drug administration is completed, the actual dosage and administration time are recorded. The data is stored in the module's local memory, which has a capacity of 1 GB and can store 30 days of drug administration records.

[0031] Specifically, the system parameter dynamic update module is a crucial component ensuring the long-term stable operation of the system. Its technical parameters include parameter update frequency, update accuracy, and data storage capacity. The parameter update frequency is once every 30 minutes, promptly reflecting changes in the system's operating status; the update accuracy is controlled within ±1%, ensuring the accuracy of parameter adjustments; and the data storage capacity is 100GB, capable of storing one year's worth of system parameter update records. The significance of this module lies in continuously updating the calculation parameters of each module, enabling the system to adapt to dynamic changes in the patient's physiological state and transport environment, thereby continuously improving the accuracy and reliability of dose control. The specific implementation process of this module is as follows: After receiving the drug administration data and physiological parameter change data fed back from the anti-radiation drug dosage adjustment module, the data is first analyzed and processed to calculate the deviation between the actual drug administration effect and the expected effect. Based on the deviation value, the types and ranges of parameters that need to be updated are determined, involving the scale transformation coefficients of the multi-scale pharmacokinetic modeling module, the prior distribution parameters of the population Bayesian optimization calculation module, and the sampling frequency of the real-time physiological parameter acquisition module, etc. Then, an adaptive update algorithm is used to adjust these parameters. The adjustment range is determined by the magnitude of the deviation; the larger the deviation, the larger the adjustment range, but the maximum adjustment range does not exceed 10% of the original parameter value. After the parameter update is complete, the new parameter values ​​are sent to the corresponding modules. Each module, upon receiving the parameters, immediately updates its own parameter configuration and restarts the relevant calculation process. Simultaneously, the time, content, and reason for the parameter update are recorded and stored in a local database for subsequent system maintenance and performance analysis. Each parameter update process takes no more than 30 seconds to ensure uninterrupted system operation.

[0032] Preferably, when the multi-scale pharmacokinetic modeling module and the population Bayesian optimization calculation module work together, the following model formula is used to describe the relationship between the metabolic rate and dose of the drug during transport: ,in, Let t be the concentration of anti-radiation drug in the blood of the wounded. For transit time, This is the drug absorption rate constant. This is the drug elimination rate constant. To transport environmental radiation intensity, The effective radiation protection threshold for drugs, The internal volume of the transport carrier. For ambient humidity;

[0033] When dealing with confounding factors, the population Bayesian optimization calculation module uses the following model formula: ,in, Let be the drug metabolism parameters of the j-th injured person in the i-th group. The mean of the group parameters, This is a group-level random effect that follows a standard normal distribution. For the standard deviation of the population parameter, It represents a random effect at the individual level, following a normal distribution with a mean of 0.

[0034] Specifically, the multi-scale pharmacokinetic modeling module and the population Bayesian optimization calculation module work collaboratively, with parameters including the calculation accuracy of drug metabolism rate and the coverage of population parameters. The calculation accuracy of drug metabolism rate is controlled within ±4%, accurately reflecting the metabolic changes of the drug under different transport environments. The population parameters cover data from injured persons of different ages (18-65 years) and weights (50-100kg), ensuring the broad applicability of population patterns. Through collaborative modeling and optimization, the relationship between drug metabolism and dosage is described more accurately, while effectively handling confounding factors and improving the accuracy of dosage calculation. The implementation method is as follows: the multi-scale pharmacokinetic modeling module receives blood drug concentration and radiation exposure dose data from the real-time physiological parameter acquisition module and radiation intensity, space volume, and humidity data from the transport environment data capture module. It then combines this data with drug absorption and elimination rate constants to calculate the drug metabolism rate at different times. The drug absorption rate constant ranges from 0.5-2.0 L / h, and the elimination rate constant ranges from... The radiation protection threshold is set at 50-200 mSv / h. The population Bayesian optimization calculation module loads a population pharmacokinetic database containing drug metabolism parameters from over 1000 individuals in different populations. The mean of these parameters fluctuates between 0.8 and 1.2 depending on the characteristics of different populations, with the standard deviation controlled within the range of 0.1-0.3, and the standard deviation of individual-level random effects not exceeding 0.05. By matching individual data with population data, a subset of 50-80 similar individuals is selected. The posterior probability is calculated using Bayes' theorem to determine the individual's drug metabolism parameters, thus providing a more precise basis for dose adjustment. The calculation results are updated every 10 minutes to ensure real-time adaptation to environmental changes and fluctuations in the patient's physiological state during transport.

[0035] Preferably, the anti-radiation drug dosage adjustment module calculates the real-time drug dosage based on the drug distribution coefficient output by the multi-scale pharmacokinetic modeling module and the dosage optimization value obtained by the population Bayesian optimization calculation module, using the following model formula: ,in, This is a single-dose administration. To achieve the target blood drug concentration, This represents the current blood drug concentration. The apparent volume of distribution of the drug. The altitude influence coefficient is... This is the current altitude. For drug bioavailability, This is the temperature influence coefficient. The ambient temperature during transport;

[0036] The transport environment data acquisition module incorporates vibration frequency data into the dose adjustment correction, using the following formula: ,in, This is the vibration-corrected dosage. This is the vibration influence coefficient. The vibration frequency of the transport carrier.

[0037] Specifically, the radiation-resistant drug dosage adjustment module and the transport environment data capture module work synergistically, with parameters including the calculation accuracy of the drug dosage and the range of vibration correction coefficients. The drug dosage calculation accuracy can reach ±0.05mg, meeting the dosage adjustment needs under different injury conditions; the vibration correction coefficient varies depending on the type of transport carrier. Adjustments are made to ensure dosage accuracy under vibration. Combining multi-scale modeling and population optimization results, and considering factors such as altitude, temperature, and vibration in the transport environment, precise adjustment and dynamic correction of drug dosage are achieved, improving the adaptability and reliability of dosage control. The implementation method is as follows: The anti-radiation drug dosage adjustment module first receives parameters such as target blood drug concentration, apparent volume of distribution, and bioavailability output by the population Bayesian optimization calculation module. The target blood drug concentration range is 5-30 mg / L, the apparent volume of distribution is calculated based on body weight between 0.1-0.5 L / kg, and the bioavailability is set to 0.8-0.95. The module combines the current blood drug concentration data from the real-time physiological parameter acquisition module with altitude (range -500-5000m) and temperature (range -10-50℃) data from the transport environment data capture module. The altitude influence coefficient is set to... Temperature influence coefficient value The single-dose dosage is calculated. Then, based on the vibration frequency (range 0-50Hz) data from the transport environment data capture module, the dosage is corrected using a vibration correction coefficient to obtain the vibration-corrected dosage. During administration, the infusion pump's infusion rate is adjusted according to the corrected dosage, ranging from 0.1-5 mg / h. The flow rate and pressure of the infusion tubing are monitored every second to ensure that the actual dosage deviates from the calculated value by no more than ±0.01 mg. Dosage data is fed back to the system parameter dynamic update module every 5 minutes for subsequent parameter optimization.

[0038] Preferably, the multi-scale pharmacokinetic modeling module describes the drug-target binding process at the cellular scale, and the model formula is as follows: ,in, This refers to the concentration of the drug-target complex. This refers to the extracellular drug concentration. The total concentration at the target site. The binding rate constant, The dissociation rate constant is Radiation dose influencing factor, The cumulative radiation exposure dose for the injured;

[0039] The population Bayesian optimization calculation module uses the complex concentration data to estimate population parameters. The estimation formula is as follows: ,in, Parameters for given observation data y The posterior probability density, Let be the likelihood function. For parameters The prior distribution, For hyperparameters The marginal distribution.

[0040] Specifically, the multi-scale pharmacokinetic modeling module combines cellular-scale analysis with parameter estimation from the population Bayesian optimization module. Parameters include the calculation accuracy of the drug-target complex concentration and the estimation error of the posterior probability distribution. The calculation accuracy of the drug-target complex concentration is controlled within ±0.01 mol / L, accurately reflecting the binding state between the drug and the target; the estimation error of the posterior probability distribution does not exceed ±5%, ensuring the reliability of parameter estimation. This in-depth analysis of the drug action mechanism at the cellular level, combined with parameter estimation using population data, provides a more microscopic and precise theoretical basis for dose optimization, improving the scientific rigor and specificity of dose adjustment. The implementation method is as follows: At the cellular scale, the multi-scale pharmacokinetic modeling module collects extracellular drug concentration and total target concentration data in real time based on physiological parameters. The extracellular drug concentration ranges from 1-10 mol / L, and the total target concentration ranges from 0.1-1 mol / L. This data is combined with cell membrane permeability and binding and dissociation rate constants (the binding rate constant is...). The dissociation rate constant is ), while also considering radiation dose influence factors (range) The drug-target complex concentration is calculated based on the cumulative radiation exposure dose of the injured (range 0-1000 mSv) and the data, with the results updated every 30 seconds. The population Bayesian optimization module receives the complex concentration data at the cellular level and loads a population observation dataset containing over 1000 cases, covering drug metabolism parameters under different radiation exposure levels and physiological states. Based on the likelihood function, prior distribution (dependent on hyperparameters, with the hyperparameter range adjusted between 0.5 and 2.0 according to population characteristics), and the marginal distribution of the hyperparameters, a Markov chain Monte Carlo method is used for 1000-2000 iterations to obtain the posterior probability density of the drug metabolism parameters. The parameter interval with a cumulative probability of 95% is extracted from the posterior probability distribution, with the interval width controlled within ±10% of the parameter mean. The midpoint of the interval is taken as the optimal parameter value, and the optimal parameter is sent to the anti-radiation drug dosage adjustment module every 5 minutes to guide dosage adjustment, ensuring that dosage adjustment accurately adapts to changes in drug action at the cellular level.

[0041] Preferably, the system parameter dynamic update module updates the scale transformation coefficients of the multi-scale pharmacokinetics modeling module based on the execution results of the anti-radiation drug dosage adjustment module using the following model formula: ,in, These are the updated scale transformation coefficients. These are the initial scale transformation coefficients. The update rate constant is... For reference blood drug concentration, The integral of the difference between the blood drug concentration and the reference concentration from time 0 to time t;

[0042] The prior distribution parameters of the population Bayesian optimization computation module are updated using the following formula: ,in, The updated population parameter mean. The initial population parameter mean, These are the weighting coefficients. For sample size, For the i-th observation, Let be the i-th predicted value.

[0043] Specifically, the system parameter dynamic update module updates the parameters of the multi-scale pharmacokinetic modeling module and the population Bayesian optimization calculation module. The parameters include the update accuracy of the scale transformation coefficients and the adjustment range of the population parameter mean. The update accuracy of the scale transformation coefficients is ±0.001, ensuring the coupling accuracy of the cross-scale model; the adjustment range of the population parameter mean does not exceed ±10% of the initial value, ensuring the stability of the population dynamics. By continuously updating the model parameters, the system can dynamically adapt to changes in the drug metabolism process, improving the long-term accuracy and reliability of dose control. The implementation method is as follows: After receiving the actual blood drug concentration data from the anti-radiation drug dose adjustment module and the reference blood drug concentration (range 5-25 mg / L) data from the multi-scale pharmacokinetic modeling module, the system parameter dynamic update module calculates the integral of the difference between the blood drug concentration from 0 to the current time and the reference concentration. The integral result is used to adjust the scale transformation coefficients. The update rate constant value is... The initial scale transformation coefficient is set between 0.5 and 2.0 based on tissue and cell type. The updated scale transformation coefficient is calculated using an exponential function and is updated every 30 minutes. For the prior distribution parameters of the population Bayesian optimization calculation module, the system parameter dynamic update module calculates the updated population parameter mean based on the sample size (50-100 cases), the difference between observed and predicted values, and weighted coefficients (range 0.05-0.2). The weighted coefficients are adjusted according to the similarity of the samples; the higher the similarity, the larger the weighted coefficient. After each parameter update, the new parameter values ​​are sent to the corresponding module. Each module completes the parameter configuration update within 10 seconds and restarts the calculation process. Simultaneously, the time of the parameter update, the parameter values ​​before and after, and the reason are recorded and stored in a 100GB database for subsequent analysis and traceability.

[0044] Preferably, the data transmission between the real-time physiological parameter acquisition module and the multi-scale pharmacokinetic modeling module adopts a dynamic sampling interval, and the interval time is calculated using the following formula: ,in, This is the current sampling interval. Based on the sampling interval, This is the sampling adjustment coefficient. This represents the absolute value of the rate of change in blood drug concentration.

[0045] The infusion rate adjustment formula for the anti-radiation drug dosage adjustment module is as follows: ,in, This is the current drug infusion rate. Based on the infusion rate, To achieve the maximum safe blood drug concentration, To achieve the minimum effective blood drug concentration, Humidity influence coefficient This refers to ambient humidity.

[0046] Specifically, the real-time physiological parameter acquisition module employs a dynamic sampling mechanism, while the anti-radiation drug dosage adjustment module utilizes an infusion rate adjustment mechanism. Parameters include the adjustment range of the sampling interval and the adjustment precision of the infusion rate. The sampling interval is adjustable from 1 to 10 seconds, dynamically adapting the sampling frequency based on changes in blood drug concentration. The infusion rate adjustment precision is ±0.001 mg / h, ensuring precise control of the dosing rate. By dynamically adjusting the sampling interval and infusion rate, the system can acquire physiological data more efficiently and execute dosage adjustments more accurately, improving the system's response speed and dosage control accuracy. The implementation method is as follows: the real-time physiological parameter acquisition module adjusts the sampling rate based on the absolute value (range) of the rate of change in blood drug concentration. Adjust the sampling interval, setting the basic sampling interval to 2-5 seconds, and set the sampling adjustment coefficient to [value missing]. When the rate of change in blood drug concentration is large, the sampling interval is shortened, and vice versa. The current sampling interval is calculated using a formula to ensure dense sampling when the blood drug concentration changes rapidly and reduced sampling frequency when the change is gradual, thereby improving data acquisition efficiency. The anti-radiation drug dosage adjustment module receives the basic infusion rate (range 0.1-4 mg / h), maximum safe blood drug concentration (25-35 mg / L), and minimum effective blood drug concentration (3-8 mg / L) data from the population Bayesian optimization calculation module, and calculates the relative deviation ratio based on the difference between the current blood drug concentration and the target blood drug concentration. Simultaneously, based on the ambient humidity (range 20%-95%) and humidity influence coefficient ( ) from the transport environment data capture module, the module also calculates the relative deviation ratio. The infusion rate is adjusted to obtain the current drug infusion rate. When the ambient humidity is high, the infusion rate is appropriately reduced, and vice versa. The infusion rate is adjusted every 10 seconds, and the adjusted rate is converted into a stepper motor rotation signal via the infusion pump drive circuit. Each step of the stepper motor corresponds to a drug dosage of 0.0001 mg, ensuring that the actual infusion rate matches the calculated value. After each sampling interval adjustment and infusion rate adjustment, relevant parameters are fed back to the system parameter dynamic update module for optimizing subsequent sampling and adjustment strategies.

[0047] Preferably, the multi-scale pharmacokinetic modeling module includes: a tissue-scale drug distribution calculation unit, a cell-scale drug transport simulation unit, and a cross-scale parameter mapping unit. The tissue-scale drug distribution calculation unit receives blood flow velocity and vascular density data output from the real-time physiological parameter acquisition module, and combines it with temperature data output from the transport environment data capture module. By calculating the product of blood perfusion rate and drug distribution coefficient in different tissues and organs, the initial distribution amount of drug in each tissue is obtained. The cell-scale drug transport simulation unit simulates the process by which drug molecules enter the cell through passive diffusion and active transport and bind to the target, based on cell membrane permeability and intracellular target concentration data, and outputs the dynamic change curve of the drug-target complex. The cross-scale parameter mapping unit converts the drug concentration data calculated at the tissue scale into the boundary conditions of the cell-scale model, and feeds back the drug metabolism rate simulated at the cell scale to the tissue-scale model, performing coupled calculations of the two scale models.

[0048] Specifically, the multi-scale pharmacokinetic modeling module includes a tissue-scale drug distribution calculation unit, a cell-scale drug transport simulation unit, and a cross-scale parameter mapping unit. Technical parameters include the spatial resolution at the tissue scale, the simulation accuracy at the cell scale, and the error range of the cross-scale parameter mapping. The spatial resolution at the tissue scale is 0.5-2 mm, accurately delineating the boundaries of different tissues and organs; the simulation accuracy at the cell scale is controlled within ±3%, accurately reflecting the interaction between drug molecules and targets; and the error of the cross-scale parameter mapping does not exceed ±5%, ensuring the consistency of data from models at different scales. Through the collaborative work of these three units, the entire process of drug metabolism from macroscopic tissues to microscopic cells can be analyzed, overcoming the limitations of single-scale models and providing more comprehensive and accurate theoretical support for dosage optimization. The implementation method is as follows: The tissue-scale drug distribution calculation unit receives blood flow velocity (range 0.5-5L / min), blood vessel density (range 100-500 vessels / mm³) data output by the real-time physiological parameter acquisition module, and temperature (range -10-50℃) data output by the transport environment data capture module. Based on the blood perfusion rate of each tissue and organ (heart 300-500mL / min / 100g, liver 100-300mL / min / 100g, kidney 200-400mL / min / 100g) and drug distribution coefficient (fat 0.1-0.5, muscle 0.5-1.5, liver 1.0-3.0), the initial distribution of the drug in each tissue is calculated, and the data is updated every 5 minutes. The cell-scale drug transport simulation unit simulates the process by which drug molecules enter the cell and bind to the target via both passive diffusion and active transport, based on cell membrane permeability (range 1e-6 to 1e-4 cm / s) and intracellular target concentration (range 0.1-1 μmol / L). The passive diffusion rate is proportional to the concentration gradient, while the active transport rate is affected by the number of carriers (range 1e3 to 1e5 per cell) and affinity (range 1e-6 to 1e-4 mol / L). The unit outputs a dynamic curve of the drug-target complex every minute. The cross-scale parameter mapping unit converts the drug concentration data calculated at the tissue scale into boundary conditions for the cell-scale model. The conversion coefficient is adjusted between 0.8 and 1.2 according to the tissue type. Simultaneously, the unit feeds back the drug metabolism rate (range 0.01-0.1 μmol / min) simulated at the cell scale to the tissue-scale model, correcting the tissue-scale metabolic parameters and achieving coupled calculations between the two scale models. Cross-scale data interaction is completed every 3 minutes.

[0049] Preferably, the population Bayesian optimization calculation module includes: a population data stratification processing unit, a prior distribution construction unit, a posterior probability calculation unit, and an optimization parameter extraction unit. The population data stratification processing unit stratifies the received multi-scale pharmacokinetic modeling module output data according to the patient's age, weight, and radiation exposure level, and calculates the mean, standard deviation, and coefficient of variation of each stratum. The prior distribution construction unit sets a prior probability density function conforming to a normal-gamma distribution for the drug metabolism parameters based on the stratified data characteristics, where the mean parameter is associated with the stratified mean, and the variance parameter is associated with the stratified standard deviation. The posterior probability calculation unit uses the Markov chain Monte Carlo method, combined with real-time collected blood drug concentration observation data, to calculate the posterior probability distribution of each drug metabolism parameter. The optimization parameter extraction unit selects the parameter interval corresponding to a cumulative probability of 95% from the posterior probability distribution, and takes the midpoint of the interval as the optimal parameter value for the population Bayesian optimization calculation.

[0050] Specifically, the population Bayesian optimization computation module includes a population data stratification processing unit, a prior distribution construction unit, a posterior probability calculation unit, and an optimization parameter extraction unit. Technical parameters involve the statistical accuracy of the stratified data, the goodness of fit of the prior distribution, the convergence speed of the posterior probability calculation, and the extraction error of the optimization parameters. The statistical accuracy of the stratified data is controlled within ±2% to ensure the accuracy of the features of each stratum; the goodness of fit of the prior distribution is verified by a chi-square test, with a p-value greater than 0.05, indicating good fit between the distribution model and the data; the convergence speed of the posterior probability calculation is 50-100 iterations, enabling rapid acquisition of a stable posterior distribution; and the extraction error of the optimization parameters does not exceed ±4%, ensuring the reliability of the extracted parameters. Through the collaborative work of these four units, scientific processing and parameter optimization of population data are achieved, fully utilizing population pharmacokinetic laws to correct individual differences and improve the accuracy and adaptability of dose adjustment. The implementation method is as follows: After receiving the data output by the multi-scale pharmacokinetic modeling module, the population data stratification processing unit stratifies the data according to the injured person's age (18-30 years, 31-50 years, 51-65 years), weight (50-60kg, 61-80kg, 81-100kg), and radiation exposure level (0-200mSv, 201-500mSv, 501-1000mSv). The mean, standard deviation, and coefficient of variation (controlled within 10%-30%) of each stratum are calculated, and the stratification results are updated every 10 minutes. Based on the characteristics of the stratified data, the prior distribution construction unit sets a prior probability density function conforming to a normal-gamma distribution for the drug metabolism parameters. The deviation between the mean parameter and the stratified mean does not exceed 5%, and the ratio of the variance parameter to the stratified standard deviation is between 0.8 and 1.2, ensuring that the prior distribution reflects the statistical characteristics of the population data. The posterior probability calculation unit employs the Markov chain Monte Carlo method, combined with real-time blood drug concentration observation data (collected every 5 minutes), setting the chain length to 10,000-20,000 steps and the burn-through period to 2,000-5,000 steps, to calculate the posterior probability distribution of each drug metabolism parameter, completing a calculation every 15 minutes. The parameter extraction unit selects the parameter interval corresponding to a cumulative probability of 95% from the posterior probability distribution. The ratio of the difference between the upper and lower limits of the interval to the parameter mean is controlled within 10%-20%. The midpoint of the interval is taken as the optimal parameter value for population Bayesian optimization calculation. The optimal parameter is sent to the anti-radiation drug dosage adjustment module every 5 minutes to guide dosage adjustment.

[0051] Preferably, the anti-radiation drug dosage adjustment module includes: a dosage requirement calculation unit, a dosing method selection unit, and an infusion accuracy control unit. The dosage requirement calculation unit receives the optimal parameters output by the population Bayesian optimization calculation module, and combines them with the current blood drug concentration and radiation exposure dose data output by the physiological parameter acquisition module in real time. By calculating the difference between the target blood drug concentration and the current blood drug concentration, and the drug increment required for radiation protection, the total dosage requirement value is obtained. The dosing method selection unit selects the continuous infusion mode based on the vibration frequency and bump level data output by the transport environment data acquisition module. When the vibration frequency is lower than the threshold, the continuous infusion mode is selected. When the vibration frequency is higher than the threshold, the intermittent pulse dosing mode is switched, and the pulse interval and single pulse dose are adjusted at the same time. The infusion accuracy control unit compensates for the infusion flow fluctuation caused by the vibration of the transport carrier by real-time monitoring of the pressure change of the infusion pipeline, and corrects the deviation between the actual dosage and the theoretical dosage within 10 seconds.

[0052] Specifically, the anti-radiation drug dosage adjustment module includes a dosage requirement calculation unit, a dosing method selection unit, and an infusion accuracy control unit. Technical parameters include the accuracy of dosage calculation, the response time of dosing method switching, and the control error of infusion accuracy. The dosage calculation accuracy can reach ±0.03mg, meeting the dosage requirements under different injury conditions; the response time for dosing method switching is less than 1 second, ensuring rapid adjustment of the dosing mode when the environment changes; the control error of infusion accuracy is less than ±0.005mg, ensuring consistency between the actual dosage and the theoretical value. Through the coordinated work of these three units, precise drug dosage calculation, dynamic adjustment of the dosing method, and precise control of the infusion process are achieved, ensuring that the drug dosage remains at an appropriate level during transport, improving treatment efficacy and reducing adverse reactions. The implementation method is as follows: The dose requirement calculation unit receives the optimal parameters output by the population Bayesian optimization calculation module, and combines the current blood drug concentration (range 0-50 mg / L) and radiation exposure dose (range 0-1000 mSv) data output by the physiological parameter real-time acquisition module to calculate the difference between the target blood drug concentration (range 5-30 mg / L) and the current blood drug concentration. At the same time, it calculates the drug increment required for radiation protection based on the radiation exposure dose (0.5-2 mg drug increment per 100 mSv of radiation exposure). The sum of the two is the total dose requirement value, and the calculation result is updated every 5 minutes. The dosing method selection unit sets a vibration frequency threshold of 10Hz based on the vibration frequency (range 0-50Hz) and turbulence level (measured by vibration acceleration, range 0-10m / s²) data output by the transport environment data capture module. When the vibration frequency is below the threshold, continuous infusion is selected, and the infusion rate is adjusted between 0.1-5mg / h according to the dose requirement. When the vibration frequency is above the threshold, it switches to intermittent pulse dosing, with the pulse interval set to 1-5 minutes and the single pulse dose being 5%-20% of the total dose requirement. The specific value is dynamically adjusted according to the turbulence level. The infusion accuracy control unit monitors the pressure changes in the infusion line in real time through a pressure sensor (measurement range 0-50kPa, accuracy ±0.2kPa). Pressure data is collected every 0.1 seconds. When the pressure fluctuation exceeds ±5%, the infusion pump's drive voltage (range 5-12V) is adjusted to compensate for the flow fluctuation and correct the deviation between the actual dosage and the theoretical dosage within 10 seconds, ensuring that the deviation does not exceed ±0.005mg. At the same time, the correction data is fed back to the system parameter dynamic update module to optimize the subsequent infusion control strategy.

[0053] The spatiotemporal multi-scale physiological pharmacokinetics model in this invention is a model that can describe the metabolic process of anti-radiation drugs in the body of wounded soldiers from multiple dimensions of time and space. Its implementation involves synergistic effects at the tissue scale, cell scale, and across scales. The tissue-scale drug distribution calculation unit receives blood flow velocity and vascular density data from the real-time physiological parameter acquisition module and temperature data from the transport environment data capture module. Based on the blood perfusion rate and drug distribution coefficient of each tissue and organ, it calculates and updates the initial distribution of the drug in different tissues every 5 minutes, with a spatial resolution of 0.5-2 mm, accurately delineating tissue and organ boundaries. The cell-scale drug transport simulation unit, based on data such as cell membrane permeability and intracellular target concentration, simulates the process by which drug molecules enter cells through passive diffusion and active transport and bind to the target. The simulation accuracy is controlled within ±3%, and the dynamic change curve of the drug-target complex is output every 1 minute. The cross-scale parameter mapping unit performs cross-scale data interaction every 3 minutes, converting tissue-scale drug concentration data into boundary conditions for the cell-scale model, while simultaneously feeding back the cell-scale drug metabolism rate to the tissue-scale model to correct metabolic parameters, with a mapping error not exceeding ±5%. The model comprehensively reflects the metabolic patterns of drugs in vivo, from macroscopic tissues to microscopic cells, overcoming the limitations of single-scale models and providing a multi-dimensional and precise theoretical basis for subsequent dosage optimization. Its significance lies in its ability to more realistically present the metabolic process of drugs in complex transport environments and under the dynamic physiological states of wounded patients, laying a scientific foundation for precise control of anti-radiation drug dosage and improving the targeting and effectiveness of dosage adjustment.

[0054] The population Bayesian optimization model with confounding factor adjustment is a model that combines population pharmacokinetic data and individual real-time data to adjust for confounding factors affecting the dosage of anti-radiation drugs, thereby achieving dosage optimization. Its implementation process is completed collaboratively by four units: population data stratification processing, prior distribution construction, posterior probability calculation, and optimization parameter extraction. The population data stratification processing unit stratifies the data output from the multi-scale pharmacokinetic modeling module according to the injured person's age, weight, and radiation exposure level, and calculates the mean, standard deviation, and coefficient of variation for each stratum. The coefficient of variation is controlled within 10%-30%, and the stratification results are updated every 10 minutes with a statistical accuracy of ±2%. The prior distribution construction unit sets a prior probability density function conforming to a normal-gamma distribution for the drug metabolism parameters based on the characteristics of the stratified data, ensuring that the deviation between the mean parameter and the stratified mean does not exceed 5%, and the ratio of the variance parameter to the stratified standard deviation is between 0.8 and 1.2. The chi-square test (P-value > 0.05) is used to ensure the fit of the distribution model. The posterior probability calculation unit employs the Markov chain Monte Carlo method, combining blood drug concentration observation data collected every 5 minutes. The chain length is set to 10,000-20,000 steps, and the burn-through period to 2,000-5,000 steps. The posterior probability distribution calculation is completed every 15 minutes, with a convergence rate of 50-100 iterations. The parameter extraction unit selects parameter intervals with a cumulative probability of 95% from the posterior probability distribution. The ratio of the difference between the upper and lower limits of the interval to the parameter mean is controlled within 10%-20%, and the midpoint is taken as the optimal parameter. The extraction error is ±4%, and the parameters are sent to the dose adjustment module every 5 minutes. The model's function is to fully utilize population pharmacokinetic laws to correct individual differences, improving the accuracy and adaptability of dose adjustment. Its significance lies in solving the problem of insufficient fusion between population data and individual dynamic characteristics in traditional methods. This allows dose optimization to both draw on common population patterns and adapt to real-time individual conditions, achieving precise adjustment of anti-radiation drug dosage in complex transport environments, ensuring treatment efficacy and reducing adverse reactions.

[0055] like Figure 2 As shown, an adaptive dose control system for acquiring radiation-resistant drug dose data is described. The system operation includes:

[0056] Step S1: The real-time physiological parameter acquisition module acquires the patient's heart rate, blood pressure, blood drug concentration, and radiation exposure dose data through implanted sensors and body surface monitoring devices, stores the data in the buffer at a frequency of 1 minute / time, and marks the acquisition timestamp.

[0057] Step S2: The transport environment data acquisition module uses temperature and humidity sensors, vibration sensors, and altimeters to collect temperature, humidity, vibration frequency, and altitude data inside the transport vehicle, and synchronizes them with physiological parameter data in time.

[0058] Step S3: The multi-scale pharmacokinetic modeling module calls the preset cross-tissue-cell scale model, uses the synchronized physiological and environmental data as input variables, and calculates the distribution concentration of the drug in the blood, liver, and kidney and the rate of metabolite formation.

[0059] Step S4: The population Bayesian optimization calculation module loads historical population pharmacokinetic data, combines the output results of the multi-scale model to construct the objective function, and obtains the optimized drug dosage value adapted to the current transport environment through iterative calculation.

[0060] Step S5: The anti-radiation drug dosage adjustment module drives the infusion pump actuator according to the optimized value to adjust the drug infusion rate and single dose, and at the same time collects the adjusted blood drug concentration data.

[0061] Step S6: The system parameter dynamic update module compares the adjusted blood drug concentration data with the target concentration, corrects the parameter coefficients of the multi-scale model and the prior distribution parameters of the population Bayesian optimization based on the deviation value, and completes one adaptive control cycle.

[0062] An adaptive dose control system for anti-radiation drug dosage data acquisition exhibits significant advantages in cross-scale analysis, effectively overcoming the lack of cross-scale integration capabilities in existing technologies. A real-time physiological parameter acquisition module accurately acquires data such as the patient's heart rate, blood pressure, and blood drug concentration, while a transport environment data capture module comprehensively collects information such as the vibration frequency, temperature, and humidity of the transport vehicle. Both modules collaboratively input this data into a multi-scale pharmacokinetic modeling module. This module utilizes this data to construct a pharmacokinetic model covering tissue and cellular scales, enabling analysis of the entire drug metabolism process from macroscopic to microscopic levels. This overcomes the limitations of traditional technologies that rely solely on single-scale data, more realistically reflecting the drug's mechanism of action and effects in vivo.

[0063] The system demonstrates significant advantages in fusing population and individual data, overcoming the shortcomings of existing technologies that fail to effectively integrate population data with individual dynamic characteristics. The population Bayesian optimization calculation module fully utilizes the output of the multi-scale pharmacokinetic modeling module, combining it with rich population pharmacokinetic data for optimization calculations. This approach absorbs the common characteristics of population patterns while also considering individual differences. The anti-radiation drug dosage adjustment module precisely adjusts the drug infusion rate and single dose based on the optimization results, while the system parameter dynamic update module continuously corrects the calculation parameters of each module based on changes in adjusted physiological parameters. This achieves an organic combination of population data and real-time individual states, avoiding problems such as poor individual adaptability or lagging adjustment.

[0064] Furthermore, the system exhibits strong dynamic adaptability, maintaining stable and efficient operation even in complex transport environments. The modules are tightly interconnected and work collaboratively: the real-time physiological parameter acquisition module and the transport environment data capture module continuously provide dynamic data; the multi-scale pharmacokinetic modeling module and the population Bayesian optimization calculation module rapidly process and analyze the data; the anti-radiation drug dosage adjustment module responds promptly; and the system parameter dynamic update module continuously optimizes parameter configurations. This efficient collaborative mechanism enables the system to quickly adapt to various environmental changes and fluctuations in the patient's physiological state during transport, ensuring that the anti-radiation drug dosage remains at a precise and appropriate level, providing reliable protection for the patient's treatment.

[0065] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0066] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An anti-radiation pharmaceutical dose data acquisition adaptive dose control system, characterized by, include: The system includes a real-time physiological parameter acquisition module, a transport environment data capture module, a multi-scale pharmacokinetics modeling module, a population Bayesian optimization calculation module, an anti-radiation drug dosage adjustment module, and a system parameter dynamic update module. The output of the real-time physiological parameter acquisition module is connected to the first input of the multi-scale pharmacokinetic modeling module; the output of the transport environment data capture module is connected to the second input of the multi-scale pharmacokinetic modeling module; the output of the multi-scale pharmacokinetic modeling module is connected to the input of the population Bayesian optimization calculation module; the output of the population Bayesian optimization calculation module is connected to the input of the anti-radiation drug dosage adjustment module; the feedback of the anti-radiation drug dosage adjustment module is connected to the input of the system parameter dynamic update module; and the output of the system parameter dynamic update module is connected to the real-time physiological parameter acquisition module, the transport environment data capture module, and the multi-scale pharmacokinetic modeling module, respectively. The module connects to the parameter configuration terminals of the population Bayesian optimization calculation module and the anti-radiation drug dosage adjustment module; the physiological parameter real-time acquisition module acquires data on the patient's heart rate, blood pressure, blood drug concentration, and radiation exposure dose; the transport environment data capture module acquires data on the vibration frequency, temperature, humidity, and altitude of the transport vehicle; the multi-scale pharmacokinetic modeling module constructs a cross-tissue and cellular pharmacokinetic model based on the above physiological parameters and environmental data; the population Bayesian optimization calculation module uses the model output results combined with population pharmacokinetic data to perform optimization calculations; and the anti-radiation drug dosage adjustment module adjusts the drug infusion rate and single dose according to the optimization calculation results. The system parameter dynamic update module updates the calculation parameters of each module based on the changes in physiological parameters after dose adjustment; When the multi-scale pharmacokinetic modeling module and the population Bayesian optimization calculation module work together, the following model formula is used to describe the relationship between the metabolic rate and dose of the drug during transport: ,in, Let t be the concentration of anti-radiation drugs in the blood of the wounded. For transit time, This is the drug absorption rate constant. This is the drug elimination rate constant. To transport environmental radiation intensity, The effective radiation protection threshold for drugs, The internal volume of the transport carrier. For ambient humidity; The anti-radiation drug dosage adjustment module calculates the real-time dosage based on the drug distribution coefficient output by the multi-scale pharmacokinetic modeling module and the dosage optimization value obtained by the population Bayesian optimization calculation module, using the following model formula: ,in, This is a single-dose administration. To achieve the target blood drug concentration, This represents the current blood drug concentration. The apparent volume of distribution of the drug. The altitude influence coefficient is... This is the current altitude. For drug bioavailability, This is the temperature influence coefficient. The ambient temperature during transport; The transport environment data acquisition module incorporates vibration frequency data into the dose adjustment correction, using the following formula: ,in, This is the vibration-corrected dosage. This is the vibration influence coefficient. The vibration frequency of the transport carrier.

2. The radiation resistant pharmaceutical dose data collection adaptive dose control system of claim 1, wherein, When dealing with confounding factors, the population Bayesian optimization calculation module uses the following model formula: ,in, Let be the drug metabolism parameters of the j-th injured person in the i-th group. The mean of the group parameters, This is a group-level random effect that follows a standard normal distribution. For the standard deviation of the population parameter, It represents a random effect at the individual level, following a normal distribution with a mean of 0.

3. The radiation resistant pharmaceutical dose data collection adaptive dose control system of claim 1, wherein, The multi-scale pharmacokinetic modeling module describes the drug-target binding process at the cellular scale. The model formula is as follows: ,in, This refers to the concentration of the drug-target complex. This refers to the extracellular drug concentration. The total concentration at the target site. The binding rate constant is The dissociation rate constant is Radiation dose influencing factor, The cumulative radiation exposure dose for the injured; The population Bayesian optimization calculation module uses the complex concentration data to estimate population parameters. The estimation formula is as follows: ,in, Parameters for given observation data y The posterior probability density, Let be the likelihood function. For parameters The prior distribution, For hyperparameters The marginal distribution.

4. The radiation resistant pharmaceutical dose data collection adaptive dose control system of claim 1, wherein, The system parameter dynamic update module updates the scale transformation coefficients of the multi-scale pharmacokinetics modeling module based on the execution results of the anti-radiation drug dosage adjustment module using the following model formula: ,in, These are the updated scale transformation coefficients. These are the initial scale transformation coefficients. Let be the update rate constant. For reference blood drug concentration, The integral of the difference between the blood drug concentration and the reference concentration from time 0 to time t; The prior distribution parameters of the population Bayesian optimization computation module are updated using the following formula: ,in, The updated population parameter mean. The initial population parameter mean, These are the weighting coefficients. For sample size, For the i-th observation, Let be the i-th predicted value.

5. The radiation resistant pharmaceutical dose data acquisition adaptive dose control system of claim 1, wherein, The data transmission between the physiological parameter real-time acquisition module and the multi-scale pharmacokinetic modeling module adopts a dynamic sampling interval, and the interval time calculation formula is: wherein, is the current sampling interval, is the basic sampling interval, is the sampling adjustment coefficient, is the absolute value of the blood concentration change rate; The infusion rate adjustment formula of the anti-radiation drug dose adjustment module is: wherein, is the current drug infusion rate, is the basic infusion rate, is the maximum safe blood drug concentration, is the minimum effective blood drug concentration, is the humidity influence coefficient, is the environmental humidity.

6. The radiation dose data acquisition self-adapting dose control system of claim 1, wherein, The multi-scale pharmacokinetic modeling module includes: a tissue-scale drug distribution calculation unit, a cell-scale drug transport simulation unit, and a cross-scale parameter mapping unit. The tissue-scale drug distribution calculation unit receives blood flow velocity and vascular density data output from the real-time physiological parameter acquisition module, and combines it with temperature data output from the transport environment data capture module. By calculating the product of blood perfusion rate and drug distribution coefficient in different tissues and organs, it obtains the initial distribution amount of drug in each tissue. The cell-scale drug transport simulation unit simulates the process of drug molecules entering the cell and binding to the target through passive diffusion and active transport based on cell membrane permeability and intracellular target concentration data, and outputs the dynamic change curve of the drug-target complex. The cross-scale parameter mapping unit converts the drug concentration data calculated at the tissue scale into the boundary conditions of the cell-scale model, and feeds back the drug metabolism rate simulated at the cell scale to the tissue-scale model, performing coupled calculations of the two scale models.

7. The radiation dose data acquisition self-adapting dose control system of claim 1, wherein, The population Bayesian optimization computation module includes: a population data stratification processing unit, a prior distribution construction unit, a posterior probability calculation unit, and an optimization parameter extraction unit. The population data stratification processing unit stratifies the received multi-scale pharmacokinetic modeling module output data according to the injured person's age, weight, and radiation exposure level, and calculates the mean, standard deviation, and coefficient of variation for each stratum. The prior distribution construction unit sets a prior probability density function conforming to a normal-gamma distribution for the drug metabolism parameters based on the stratified data characteristics, where the mean parameter is correlated with the stratified mean, and the variance parameter is correlated with the stratified standard deviation. The posterior probability calculation unit uses the Markov chain Monte Carlo method, combined with real-time collected blood drug concentration observation data, to calculate the posterior probability distribution of each drug metabolism parameter. The optimization parameter extraction unit selects the parameter interval corresponding to a cumulative probability of 95% from the posterior probability distribution, and takes the midpoint of the interval as the optimal parameter value for the population Bayesian optimization computation.

8. The radiation dose data acquisition self-adapting dose control system of claim 1, wherein, The anti-radiation drug dosage adjustment module includes: a dosage requirement calculation unit, a dosing method selection unit, and an infusion accuracy control unit. The dosage requirement calculation unit receives the optimal parameters output by the population Bayesian optimization calculation module, and combines them with the current blood drug concentration and radiation exposure dose data output by the physiological parameter acquisition module in real time. By calculating the difference between the target blood drug concentration and the current blood drug concentration, and the drug increment required for radiation protection, the total dosage requirement value is obtained. The dosing method selection unit captures the vibration frequency and bump level data output by the transport environment data acquisition module. When the vibration frequency is below the threshold, the continuous infusion mode is selected. When the vibration frequency is above the threshold, the intermittent pulse dosing mode is switched, and the pulse interval and single pulse dose are adjusted at the same time. The infusion accuracy control unit compensates for the infusion flow fluctuation caused by the vibration of the transport carrier by monitoring the pressure change of the infusion pipeline in real time, and corrects the deviation between the actual dosage and the theoretical dosage every 10 seconds.

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