Microfluidic anesthesia bomb control method based on pressure feedback

Abstract

The invention provides a micro-fluidic anesthetic bomb control method based on pressure feedback, and relates to the field of anesthetic bomb control, the micro-fluidic anesthetic bomb control method comprises the steps that a micro-fluidic anesthetic bomb double-layer structure model is obtained, and the double-layer structure model comprises an inner anesthetic storage cavity model and an outer elastic adjustable layer material model; in the anesthetic release process, acquiring released anesthetic liquid drop volume data monitored by a photoelectric sensor in real time, and calculating the release dose according to a pre-established function relationship between the liquid drop volume and the anesthetic dose; and according to the verified and optimized final function relationship model, predicting the dosage of anesthetic released by the anesthetic bomb with specific outer layer material parameters under the current pressure condition. According to the method, online support vector regression is adopted as an incremental learning algorithm, model parameters are dynamically updated, self-adaptive adjustment is performed according to the reference value and the actual dose deviation, the anesthetic release accuracy and controllability are effectively improved, and reliable technical support is provided for clinical anesthetic administration.

Description

Technical Field

[0001] The invention relates to the field of information technology, and in particular to a microfluidic anesthetic bullet control method based on pressure feedback. Background Art

[0002] Microfluidic anesthetic bombs face the technical challenge of precise control of anesthetic doses in practical applications. Anesthesia bombs release anesthetics through a pressure feedback mechanism, but since the functional relationship between pressure and anesthetic release dose is not clear, it is difficult to achieve precise control of the dose. In addition, the elastic modulus of a single material limits the adjustment range of the pressure feedback intensity, further increasing the uncertainty of dose control.

[0003] The structural design of the anesthetic bomb also faces challenges. The anesthetic stored in the inner layer needs to be isolated from the outer layer material to avoid dose loss caused by diffusion and leakage. At the same time, the selection of the outer layer material needs to take into account both elastic modulus adjustment and biocompatibility, and changes in thickness and composition will affect the overall mechanical properties and stability of the anesthetic bomb.

[0004] The construction of a pressure feedback control loop is another key technical issue. The anesthetic release process is dynamic, and it is necessary to monitor pressure changes in real time and adjust material parameters in time to maintain the dose output within the target range. This requires the integration of pressure sensors, control algorithms, and actuators to build a real-time closed-loop control system. The response speed, stability, and robustness of the system directly affect the accuracy of anesthetic dose control.

[0005] In summary, the precise control of the dosage of microfluidic anesthetic bombs involves multiple technical fields such as materials, structure, and control. It is necessary to systematically analyze the pressure feedback mechanism, establish the quantitative relationship between material parameters and dosage output, optimize the structural design of anesthetic bombs, and build a real-time closed-loop control system to ultimately achieve precise control of the anesthesia process. This is of great significance to improving the anesthesia effect and reducing the risk of medication, but there are still many technical challenges to be overcome. Summary of the invention

[0006] The present invention provides a microfluidic anesthetic bomb control method based on pressure feedback, which mainly includes: Obtaining a double-layer structure model of a microfluidic anesthetic bomb, wherein the double-layer structure model includes an inner layer anesthetic storage cavity model and an outer layer elastic adjustable layer material model; For the material model of the outer elastic adjustable layer, a finite element analysis method is used to simulate the deformation and stress distribution of the anesthetic cavity under a preset pressure when the thickness of the outer layer material is discretely sampled within a preset range, and obtain an elastic modulus parameter set of the outer layer material, wherein the parameter set includes elastic modulus values ​​at different thicknesses; Obtain the pressure value of the microfluidic anesthetic bomb monitored in real time by the pressure sensor, and judge whether the current pressure reaches the preset pressure threshold according to the preset pressure threshold. If so, control the valve connecting the inner anesthetic cavity and the outer adjustable layer to open, and squeeze the inner anesthetic to release under the action of pressure according to the elastic modulus parameter set of the outer material; During the anesthetic release process, the volume data of the released anesthetic droplets monitored in real time by the photoelectric sensor is obtained, and the released dose is calculated according to the pre-established functional relationship between the droplet volume and the anesthetic dose; Based on the pressure change data monitored by the pressure sensor, the elastic modulus parameters of the outer material and the calculated anesthetic release dose data, a support vector regression algorithm is used to establish a functional relationship model between material parameters, pressure and dose output. The kernel function and parameters are selected through cross-validation, and the model parameters are trained and optimized. The performance of the functional relationship model is evaluated using a test data set, and the root mean square error and the coefficient of determination are calculated. If the root mean square error is greater than a preset error threshold or the coefficient of determination is less than a preset coefficient threshold, the support vector regression algorithm hyperparameters are adjusted and the model is retrained until a final model that meets the accuracy requirements is obtained; According to the final functional relationship model after verification and optimization, the dose of anesthetic released by the anesthetic bullet with specific outer material parameters under the current pressure conditions is predicted, and the elastic modulus of the anesthetic bullet is controlled by adjusting the thickness of the outer material to achieve precise control of the released dose. The predicted dose result is used as the reference value of the adaptive control; Real-time data of pressure and release dose are continuously collected, and online support vector regression is used as an incremental learning algorithm to merge the newly collected data with the original training data set. The parameters of the functional relationship model are dynamically updated and optimized, and the model parameters are gradually adjusted according to the deviation between the baseline value and the actual dose, thereby improving the generalization ability of the model and the prediction accuracy, and realizing adaptive control of anesthetic dose.

[0007] As a preferred solution of the microfluidic anesthetic bomb control method based on pressure feedback of the present invention, wherein: the double-layer structure model of the microfluidic anesthetic bomb is obtained, and the double-layer structure model includes an inner layer anesthetic storage cavity model and an outer layer elastic adjustable layer material model, including: Acquiring three-dimensional data of the inner layer anesthetic storage cavity model; According to the three-dimensional data, the inner layer anesthetic storage cavity model is parametrically modeled using SolidWorks software to obtain a three-dimensional solid model of the inner layer anesthetic storage cavity; Determining the three-dimensional size parameters of the material model of the outer elastic adjustable layer according to the three-dimensional solid model of the inner layer anesthetic storage cavity; According to the three-dimensional size parameters, the material model of the outer elastic adjustable layer is parametrically modeled using SolidWorks software to obtain a three-dimensional solid model of the outer elastic adjustable layer material; Assembling the three-dimensional solid model of the inner anesthetic storage cavity and the three-dimensional solid model of the outer elastic adjustable layer material to obtain a three-dimensional assembly model of the double-layer structure of the microfluidic anesthetic bomb; Using ANSYS software to perform structural statics analysis on the three-dimensional assembly model to obtain stress distribution and deformation; If the stress distribution and deformation conditions do not meet the preset conditions, the size parameters of the inner anesthetic storage cavity model and the outer elastic adjustable layer material model are adjusted according to the stress distribution and deformation conditions until the preset conditions are met.

[0008] As a preferred solution of the microfluidic anesthetic bomb control method based on pressure feedback described in the present invention, wherein: for the material model of the outer elastic adjustable layer, a finite element analysis method is used to simulate the deformation and stress distribution of the anesthetic cavity under the action of a preset pressure when the thickness of the outer layer material is discretely sampled within a preset range, and an elastic modulus parameter set of the outer layer material is obtained, and the parameter set includes elastic modulus values ​​at different thicknesses, including: Obtaining the physical properties of the material of the outer elastic adjustable layer, establishing a constitutive model of the material, and determining the parameters required by the material model; Constructing a three-dimensional geometric model of the anesthetic cavity, and meshing the geometric model using finite element analysis software; For a preset thickness range, a series of thickness values ​​are obtained, outer material parameters at different thicknesses are assigned to the geometric model, a preset pressure is set as a load condition, and a finite element solution is performed; According to the finite element analysis results, the deformation and stress distribution data under different thicknesses and preset pressures are obtained; According to the deformation and stress distribution data, elastic modulus values ​​of the outer layer material at different thicknesses are calculated to obtain an elastic modulus parameter set.

[0009] As a preferred solution of the microfluidic anesthetic bomb control method based on pressure feedback of the present invention, wherein: the pressure sensor real-time monitoring of the microfluidic anesthetic bomb under pressure value is obtained, and according to the preset pressure threshold, whether the current pressure reaches the preset pressure threshold; if so, the inner anesthetic cavity and the outer adjustable layer are controlled to open the valve connecting the outer anesthetic cavity, and according to the elastic modulus parameter set of the outer layer material, the inner anesthetic is squeezed under pressure to release, including: Obtaining the force value of the anesthetic bullet monitored in real time by the pressure sensor, comparing the value with a preset threshold, and determining whether the current pressure reaches the preset threshold; If the current pressure reaches a preset threshold, the inner and outer layer connecting valves are controlled to open; According to the anesthetic release rate predicted by the support vector regression model, a PID control algorithm is used to adjust the PWM duty cycle of the inner and outer layer connecting valves in real time to control the valve opening; The parameters of the PID controller are optimized using a genetic algorithm so that the valve control strategy can be adaptively adjusted according to the pressure change trend.

[0010] As a preferred solution of the microfluidic anesthetic bomb control method based on pressure feedback of the present invention, wherein: during the anesthetic release process, the volume data of the released anesthetic droplets monitored in real time by the photoelectric sensor is obtained, and the released dose is calculated according to the pre-established functional relationship between the droplet volume and the anesthetic dose, including: Obtain the values ​​of the proportional term, integral term and differential term, perform weighted summation on the three values, and obtain the control amount; According to the control amount, adjusting the working parameters of the anesthetic release device to change the anesthetic release rate; The support vector machine algorithm is used to update the preset functional relationship model between the droplet volume and the anesthetic dose in real time. If the functional relationship model is updated, the dose calculation accuracy is improved.

[0011] As a preferred solution of the microfluidic anesthetic bomb control method based on pressure feedback of the present invention, wherein: according to the pressure change data monitored by the pressure sensor, the elastic modulus parameters of the outer material and the anesthetic release dose data are calculated, a support vector regression algorithm is used to establish a material parameter, pressure and dose output function relationship model, and the kernel function and parameters are selected by cross-validation to train and optimize the model parameters, including: Acquire pressure change data monitored in real time by a pressure sensor, perform denoising and smoothing on the pressure change data, and extract at least one pressure change statistical feature as a model input; Obtain elastic modulus parameters of the outer layer material; Calculating the target anesthetic release dose under the current state according to a preset pharmacokinetic mathematical model in combination with the pressure change statistical characteristics and the elastic modulus parameter; The pressure change statistical characteristics, the elastic modulus parameters and the target anesthetic release dose are divided into a training set, a validation set and a test set, and a support vector regression model is constructed.

[0012] As a preferred solution of the microfluidic anesthetic bomb control method based on pressure feedback described in the present invention, wherein: the test data set is used to evaluate the performance of the functional relationship model, and the root mean square error and the determination coefficient are calculated; if the root mean square error is greater than the preset error threshold or the determination coefficient is less than the preset coefficient threshold, the support vector regression algorithm hyperparameters are adjusted, and the model is retrained until a final model that meets the accuracy requirements is obtained, including: Obtain a test data set, and use the root mean square error RMSE and the coefficient of determination R² as evaluation indicators to evaluate the performance of the support vector regression model; If the RMSE is greater than the preset error threshold, and the R² is less than the preset coefficient threshold, then according to the preset grid search rule, traverse the penalty coefficient C, kernel function type and kernel function parameter γ hyperparameter combination; For each of the hyperparameter combinations, a cross-validation method is used for evaluation, and the hyperparameter combination with the smallest RMSE on the validation set is obtained as the optimal hyperparameter; From the model set, the model with the smallest RMSE and the largest R² is selected and determined as the final functional relationship model.

[0013] As a preferred solution of the microfluidic anesthetic bullet control method based on pressure feedback described in the present invention, wherein: the dosage of anesthetic released by the anesthetic bullet with specific outer material parameters under the current pressure condition is predicted according to the final functional relationship model after verification and optimization, and the elastic modulus of the anesthetic bullet is controlled by adjusting the thickness of the outer material to achieve precise control of the released dosage, and the predicted dosage result is used as the reference value of the adaptive control, including: According to the outer material parameters of the anesthetic bomb and the current pressure conditions, the predicted anesthetic release dose is calculated using the optimized functional relationship model; By establishing a mapping relationship between material thickness and elastic modulus, the influence of adjusting material thickness on elastic modulus is determined; According to the predicted release dose, the elastic modulus of the material required to achieve the target release dose is calculated using the mapping relationship; The predicted release dose is used as the reference value of adaptive control, and a feedback control system based on PID control algorithm is established; The sensor is used to monitor the release rate and amount of the anesthetic bullet in real time, and the monitored value is compared with the reference value to calculate the deviation value; According to the deviation value, the PID control algorithm is used to calculate the adjustment amount of material thickness, and the material thickness is dynamically adjusted through the actuator so that the release dose continues to meet the requirements of precise control.

[0014] As a preferred solution of the microfluidic anesthetic bomb control method based on pressure feedback described in the present invention, wherein: the real-time data of pressure and release dose are continuously collected, online support vector regression is used as an incremental learning algorithm, the newly collected data is merged with the original training data set, the function relationship model parameters are dynamically updated and optimized, and the model parameters are gradually adjusted according to the deviation between the reference value and the actual dose, so as to improve the generalization ability and prediction accuracy of the model and realize the adaptive control of the anesthetic dose, including: Acquire data collected in real time by a pressure sensor and an anesthesia pump, and associate the pressure data with the dosage data and store them in a time series database; Adopting a gradient boosting decision tree algorithm, retraining the support vector regression model using the merged data set, and dynamically updating the parameters of the model; The optimized support vector regression model is applied to the adaptive control of the anesthesia pump. According to the patient's real-time physiological parameters and surgical requirements, the output dose of the anesthesia pump is dynamically adjusted through the PID control algorithm to achieve precise anesthesia management.

[0015] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: The present invention discloses a method for adaptively controlling the dosage of a microfluidic anesthetic bomb. In order to solve the problem of accurate release of anesthetics, a double-layer structure design is adopted, including an inner cavity for storing anesthetics and an outer elastic adjustable layer. The deformation and stress distribution of the outer material under different thicknesses and pressures are simulated by the finite element analysis method to obtain a set of elastic modulus parameters. Combined with the real-time monitoring data of the pressure sensor and the photoelectric sensor, a support vector regression algorithm is used to establish a functional relationship model of material parameters, pressure and dosage output. The model parameters are optimized by cross-validation to achieve accurate prediction of the anesthetic release dose. Online support vector regression is used as an incremental learning algorithm to dynamically update the model parameters, and adaptive adjustments are made according to the deviation between the baseline value and the actual dose, which effectively improves the accuracy and controllability of anesthetic release and provides reliable technical support for clinical anesthesia administration. Description of the drawings Figure 1 The present invention is a flow chart of a microfluidic anesthetic bomb control method based on pressure feedback.

[0016] Figure 2 It is a schematic diagram of the microfluidic anesthetic bomb control method based on pressure feedback of the present invention.

[0017] Figure 3 This is another schematic diagram of the microfluidic anesthetic bomb control method based on pressure feedback of the present invention. The following will describe the technical solutions in the embodiments of the present invention in detail in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention.

[0018] Embodiment 1: like Figure 1-3 The microfluidic anesthetic bomb control method based on pressure feedback in this embodiment may specifically include: S101. Obtain a double-layer structure model of a microfluidic anesthetic bomb, wherein the double-layer structure model includes an inner layer anesthetic storage cavity model and an outer layer elastic adjustable layer material model.

[0019] Acquiring three-dimensional data of the inner layer anesthetic storage cavity model; According to the three-dimensional data, the inner layer anesthetic storage cavity model is parametrically modeled using SolidWorks software to obtain a three-dimensional solid model of the inner layer anesthetic storage cavity; Determining the three-dimensional size parameters of the material model of the outer elastic adjustable layer according to the three-dimensional solid model of the inner layer anesthetic storage cavity; According to the three-dimensional size parameters, the material model of the outer elastic adjustable layer is parametrically modeled using SolidWorks software to obtain a three-dimensional solid model of the outer elastic adjustable layer material; Assembling the three-dimensional solid model of the inner anesthetic storage cavity and the three-dimensional solid model of the outer elastic adjustable layer material to obtain a three-dimensional assembly model of the double-layer structure of the microfluidic anesthetic bomb; Using ANSYS software to perform structural statics analysis on the three-dimensional assembly model to obtain stress distribution and deformation; If the stress distribution and deformation conditions do not meet the preset conditions, the size parameters of the inner anesthetic storage cavity model and the outer elastic adjustable layer material model are adjusted according to the stress distribution and deformation conditions until the preset conditions are met.

[0020] Exemplarily, according to the double-layer structure model of the microfluidic anesthetic bomb, the three-dimensional data of the inner layer anesthetic storage cavity model is obtained. Using the obtained three-dimensional data, the inner layer anesthetic storage cavity model is parametrically modeled in the SolidWorks software to obtain a three-dimensional solid model of the inner layer anesthetic storage cavity. According to the three-dimensional solid model of the inner layer anesthetic storage cavity model, the three-dimensional size parameters of the outer elastic adjustable layer material model are determined, including the thickness, length and width of the outer layer material. In the SolidWorks software, the outer elastic adjustable layer material model is parametrically modeled using the determined three-dimensional size parameters to obtain a three-dimensional solid model of the outer elastic adjustable layer material. In the assembly module of the SolidWorks software, the three-dimensional solid model of the inner layer anesthetic storage cavity is coaxially assembled with the three-dimensional solid model of the outer elastic adjustable layer material, and an interference check is performed to ensure that the assembled microfluidic anesthetic bomb double-layer structure model has no interference. The structural statics analysis of the three-dimensional assembly model of the double-layer structure of the microfluidic anesthetic bomb was performed using ANSYS finite element analysis software to obtain the stress distribution and deformation of the microfluidic anesthetic bomb when subjected to external force. According to the analysis results, the size parameters of the inner anesthetic storage cavity model and the outer elastic adjustable layer material model were optimized and adjusted to meet the strength and deformation requirements. The optimized inner anesthetic storage cavity model and the outer elastic adjustable layer material model were converted into STL format three-dimensional printing files in SolidWorks software for subsequent 3D printing manufacturing of microfluidic anesthetic bomb samples.

[0021] S102. For the material model of the outer elastic adjustable layer, a finite element analysis method is used to simulate the deformation and stress distribution of the anesthetic cavity under a preset pressure when the thickness of the outer layer material is discretely sampled within a preset range, so as to obtain a set of elastic modulus parameters of the outer layer material, wherein the parameter set includes elastic modulus values ​​at different thicknesses.

[0022] The physical properties of the outer elastic adjustable layer material are obtained, the material constitutive model is established, and the parameters required for the material model are determined; a three-dimensional geometric model of the anesthetic cavity is constructed, and the geometric model is meshed using finite element analysis software; a series of thickness values ​​are obtained for a preset thickness range, the outer material parameters at different thicknesses are assigned to the geometric model, a preset pressure is set as a load condition, and a finite element solution is performed; based on the finite element analysis results, deformation and stress distribution data at different thicknesses and preset pressures are obtained; based on the deformation and stress distribution data, the elastic modulus values ​​of the outer material at different thicknesses are calculated to obtain a set of elastic modulus parameters.

[0023] Exemplarily, according to the physical properties of the outer elastic adjustable layer material, a corresponding material constitutive model is established to determine the parameters required for the material model. The ANSYS finite element analysis software is used to construct a three-dimensional geometric model of the anesthetic cavity and divide the grid. For the preset thickness range, a discrete sampling method is used to obtain a series of thickness values, and the outer material parameters at different thicknesses are assigned to the geometric model. The preset pressure is set as the load condition, and the finite element solution is performed. The deformation and stress distribution data of the anesthetic cavity at different thicknesses and preset pressures are extracted from the finite element analysis results. According to the deformation and stress distribution data, MATLAB is used to calculate the elastic modulus values ​​of the outer material at different thicknesses to form an elastic modulus parameter set. The elastic modulus parameter set is used as the basis for the subsequent performance regulation of the outer material, and the performance optimization of the outer elastic adjustable layer is achieved by selecting the appropriate material thickness.

[0024] S103, obtaining the value of the pressure applied to the microfluidic anesthetic bomb monitored in real time by the pressure sensor, and judging whether the current pressure reaches the preset pressure threshold according to the preset pressure threshold; if so, controlling the valve connecting the inner anesthetic cavity and the outer adjustable layer to open, and squeezing the inner anesthetic to release it under the action of pressure according to the elastic modulus parameter set of the outer material.

[0025] The force value of the anesthetic bullet monitored in real time by the pressure sensor is obtained, and the value is compared with a preset threshold to determine whether the current pressure reaches the preset threshold; if the current pressure reaches the preset threshold, the inner and outer layer connecting valves are controlled to open; according to the anesthetic release rate predicted by the support vector regression model, a PID control algorithm is used to adjust the PWM duty cycle of the inner and outer layer connecting valves in real time to control the valve opening; the parameters of the PID controller are optimized using a genetic algorithm so that the valve control strategy can be adaptively adjusted according to the pressure change trend.

[0026] Exemplarily, the force value of the anesthetic bomb monitored in real time by the pressure sensor is obtained, and the value is compared with the preset threshold to determine whether the current pressure reaches the preset threshold. The pressure sensor uses a piezoresistive pressure sensor with a measurement range of 0-10MPa, which is installed between the outer layer and the inner layer of the anesthetic bomb, and the sampling frequency is 100Hz. If the current pressure reaches the preset threshold, the inner and outer layer connecting valves are controlled to open, and a connecting channel between the inner anesthetic cavity and the outer adjustable layer is established. The inner and outer layer connecting valves use solenoid valves, and their opening is controlled by PWM signals. According to the elastic modulus parameters of the outer material, the finite element analysis method is used to establish a numerical model of the force deformation of the outer material, and the deformation of the outer material and the pressure of the inner anesthetic cavity under the current pressure are calculated. Using the support vector regression (SVR) algorithm, the elastic modulus and pressure of the outer material are used as inputs, and the release amount of the inner anesthetic is used as output to establish a mathematical model between the three. By training the existing experimental data, the parameters of the SVR model are obtained to predict the anesthetic release rate under the current pressure. According to the anesthetic release rate predicted by the SVR model, the PID control algorithm is used to adjust the PWM duty cycle of the inner and outer layer connecting valves in real time to control the valve opening to ensure that the anesthetic can be released stably and continuously under pressure. The value changes of the pressure sensor are continuously monitored to determine whether the pressure effect is weakened. When the pressure drops below the preset threshold, the inner and outer layer connecting valves are controlled to close and the anesthetic release is stopped. During the use of the anesthetic bomb, the pressure changes and valve control parameters are continuously recorded to form feedback data. The genetic algorithm is used to optimize the parameters of the PID controller so that the valve control strategy can be adaptively adjusted according to the pressure change trend, thereby improving the accuracy and stability of anesthetic release.

[0027] S104, during the anesthetic release process, obtain the volume data of the released anesthetic droplets monitored in real time by the photoelectric sensor, and calculate the released dose according to the pre-established functional relationship between the droplet volume and the anesthetic dose.

[0028] The values ​​of the proportional term, the integral term and the differential term are obtained, and the three values ​​are weightedly summed to obtain the control amount; according to the control amount, the working parameters of the anesthetic release device are adjusted to change the anesthetic release rate; the support vector machine algorithm is used to update the preset droplet volume and anesthetic dosage functional relationship model in real time, and if the functional relationship model is updated, the dosage calculation accuracy is improved.

[0029] Exemplarily, (1) the values ​​of the proportional term, the integral term, and the differential term are calculated; (2) the three values ​​are weighted and summed to obtain the control quantity; (3) the working parameters of the anesthetic release device, such as the motor speed, valve opening, etc., are adjusted according to the control quantity to change the anesthetic release rate. The adjusted anesthetic release rate is monitored, the error between the actual release dose and the target dose is calculated, and the error value is fed back to the PID control algorithm for the next round of adjustment until the actual release dose is stabilized near the target dose. The support vector machine (SVM) algorithm is used to update and optimize the functional relationship model between the droplet volume and the anesthetic dose in real time to improve the dose calculation accuracy. The SVM algorithm continuously adjusts the model parameters by training the newly collected droplet volume and anesthetic dose data so that the model can more accurately reflect the relationship between the two. The monitoring data and calculation results of the anesthetic release process are transmitted to the central control system in real time for unified management and analysis.

[0030] S105. Based on the pressure change data monitored by the pressure sensor, the elastic modulus parameters of the outer material and the calculated anesthetic release dose data, a support vector regression algorithm is used to establish a relationship model between material parameters, pressure and dose output functions, and the kernel function and parameters are selected through cross-validation to train and optimize the model parameters.

[0031] The pressure change data monitored in real time by the pressure sensor is obtained, the pressure change data is denoised and smoothed, and at least one statistical feature of the pressure change is extracted as a model input; the elastic modulus parameter of the outer layer material is obtained; according to a preset pharmacokinetic mathematical model, the target anesthetic release dose under the current state is calculated by combining the statistical features of the pressure change and the elastic modulus parameter; the statistical features of the pressure change, the elastic modulus parameter and the target anesthetic release dose are divided into a training set, a validation set and a test set, and a support vector regression model is constructed.

[0032] Exemplarily, the pressure change data monitored in real time by the pressure sensor is obtained, and the necessary preprocessing is performed on the data, including denoising, smoothing, etc., and the statistical characteristics of the pressure change, such as mean, variance, peak value, etc., are extracted and used as one of the inputs of the model. The elastic modulus parameter of the outer material is obtained and used as another input of the model. According to the established pharmacokinetic mathematical model, the theoretical release dose of the anesthetic under the current state is calculated in combination with the pressure characteristics and material parameters. This mathematical model is usually based on the law of conservation of mass and the law of diffusion, and describes the release kinetic behavior of the drug inside the material. The pressure characteristics, material parameters and the calculated release dose are randomly divided into a training set, a validation set and a test set, and the ratio can be set to 8:1:1 to construct a support vector regression model. The grid search and 5-fold cross-validation method are used, and the mean square error and determination coefficient are used as evaluation indicators to select the kernel function type (such as linear kernel, Gaussian kernel) and kernel function parameters (such as regularization coefficient, kernel width) of the support vector regression model. Using the optimal kernel function and parameters, the support vector regression model is trained on the training set, and the parameters are adjusted on the validation set to establish a nonlinear function mapping relationship between pressure characteristics, material parameters and release dose. The generalization performance of the model is evaluated on the test set to ensure the prediction accuracy and robustness of the model. Considering the computing resources and real-time requirements of the embedded system, the trained support vector regression model is optimized as necessary, such as model compression, fixed point quantization, etc., to reduce the model size and calculation amount. The optimized model is transplanted into the embedded system, and the drug release dose is predicted based on the real-time collected pressure data and known material parameters, and the drug delivery system is adjusted in real time according to the predicted value to achieve closed-loop control.

[0033] S106. Use a test data set to evaluate the performance of the functional relationship model and calculate the root mean square error and the coefficient of determination; if the root mean square error is greater than a preset error threshold or the coefficient of determination is less than a preset coefficient threshold, adjust the support vector regression algorithm hyperparameters and retrain the model until a final model that meets the accuracy requirements is obtained.

[0034] A test data set is obtained, and the root mean square error RMSE and the coefficient of determination R² are used as evaluation indicators to evaluate the performance of the support vector regression model. If the RMSE is greater than the preset error threshold, and the R² is less than the preset coefficient threshold, then according to the preset grid search rules, the hyperparameter combinations of penalty coefficient C, kernel function type and kernel function parameter γ are traversed. For each of the hyperparameter combinations, a cross-validation method is used for evaluation, and the hyperparameter combination with the smallest RMSE on the validation set is obtained as the optimal hyperparameter. From the model set, the model with the smallest RMSE and the largest R² is selected and determined as the final functional relationship model.

[0035] Exemplarily, a test data set is obtained to evaluate the performance of the established support vector regression model. The root mean square error RMSE and the coefficient of determination R² are used as evaluation indicators, where RMSE measures the deviation between the predicted value and the true value, and R² measures the degree of fit of the model to the data. Calculate the RMSE and R² of the current model to determine whether the RMSE is greater than the preset error threshold and whether the R² is less than the preset coefficient threshold. If the conditions are met, it means that the model performance is not ideal and hyperparameter tuning is required; otherwise, the current model has reached the ideal performance level and can be used as the final functional relationship model, and jump to step 6. According to the preset grid search rules, traverse different hyperparameter combinations, including the penalty coefficient C, the kernel function type (such as linear kernel, Gaussian kernel, etc.) and the kernel function parameter γ. Each hyperparameter combination is evaluated by cross-validation, and the group with the smallest RMSE on the validation set is selected as the optimal hyperparameter. Using the optimal hyperparameter combination, retrain the support vector regression model on the training set to obtain a new candidate model. Add the model to the model set. Repeat steps 2 to 4 until the model performance requirements are met or the preset number of iterations is reached. From the model set, select the model with the smallest RMSE and the largest R² as the final functional relationship model. Deploy the final support vector regression model to the production environment. When new input data arrives, use the model to predict it and output the corresponding target value. At the same time, continue to monitor the prediction performance of the model in actual business scenarios, and update and optimize the model when necessary.

[0036] S107. According to the final functional relationship model after verification and optimization, the dosage of anesthetic released by the anesthetic bullet with specific outer material parameters under the current pressure conditions is predicted, and the elastic modulus of the anesthetic bullet is controlled by adjusting the thickness of the outer material to achieve precise control of the released dosage. The predicted dosage result is used as a reference value for adaptive control.

[0037] According to the outer material parameters of the anesthetic bomb and the current pressure conditions, the optimized functional relationship model is used to calculate the predicted anesthetic release dose. By establishing a mapping relationship between material thickness and elastic modulus, the influence of adjusting material thickness on elastic modulus is determined. According to the predicted release dose, the material elastic modulus required to achieve the target release dose is calculated using the mapping relationship. The predicted release dose is used as the reference value for adaptive control, and a feedback control system based on the PID control algorithm is established. The release rate and release amount of the anesthetic bomb are monitored in real time using sensors, and the monitored value is compared with the reference value to calculate the deviation value. According to the deviation value, the PID control algorithm is used to calculate the adjustment amount of the material thickness, and the material thickness is dynamically adjusted through the actuator so that the release dose continues to meet the requirements of precise control.

[0038] Exemplarily, according to the outer material parameters of the anesthetic bomb and the current pressure conditions, the optimized functional relationship model is used to calculate the predicted anesthetic release dose. By establishing a mapping relationship between material thickness and elastic modulus, the influence of adjusting material thickness on elastic modulus is determined, and according to the predicted release dose, the material elastic modulus required to achieve the target release dose is calculated using the mapping relationship. Compare the calculated target elastic modulus with the current elastic modulus of the material to determine whether the outer material thickness needs to be adjusted. If adjustment is required, the material thickness value corresponding to the target elastic modulus is calculated according to the material thickness-elastic modulus mapping relationship, and it is used as a reference for adjustment. The thickness of the outer material is adjusted by precision machining technology to achieve the calculated target thickness value, thereby obtaining the required target elastic modulus. The adjusted outer material is applied to the manufacturing process of the anesthetic bomb, and the release dose is precisely controlled by accurately controlling the thickness of the material during the molding process. The predicted release dose is used as the reference value for adaptive control, and a feedback control system based on the PID control algorithm is established. During the use of the anesthetic bomb, the release rate and release amount of the anesthetic bomb are monitored in real time by a sensor, and the monitored value is compared with the reference value to calculate the deviation value. According to the deviation value, the PID control algorithm is used to calculate the adjustment amount of the material thickness, and the material thickness is dynamically adjusted through the actuator so that the release dose continues to meet the requirements of precise control.

[0039] S108. Continuously collect real-time data on pressure and released dose, use online support vector regression as an incremental learning algorithm, merge the newly collected data with the original training data set, dynamically update and optimize the function relationship model parameters, gradually adjust the model parameters according to the deviation between the baseline value and the actual dose, improve the model generalization ability and prediction accuracy, and realize adaptive control of anesthetic dose.

[0040] The data collected by the pressure sensor and the anesthesia pump in real time are obtained, and the pressure data and the dosage data are associated and stored in a time series database; the gradient boosting decision tree algorithm is used to retrain the support vector regression model using the merged data set, and the parameters of the model are dynamically updated; the optimized support vector regression model is applied to the adaptive control of the anesthesia pump, and the output dosage of the anesthesia pump is dynamically adjusted through the PID control algorithm according to the patient's real-time physiological parameters and surgical requirements, so as to achieve precise anesthesia management.

[0041] Exemplarily, according to the data collected in real time by the pressure sensor and the anesthesia pump, the pressure data and the dose data are associated and stored in a time series database. According to the preset time interval, the latest batch of pressure-dose data is queried from the time series database and merged with the original training data set of the support vector regression model. The gradient boosting decision tree algorithm is used to retrain the support vector regression model using the merged data set, and the parameters of the optimization model are dynamically updated. The physiological parameters of the current patient, including heart rate, blood pressure, blood oxygen saturation, etc., are obtained in real time through medical monitoring instruments. The optimal anesthetic dose baseline value of the patient is calculated according to the support vector regression model. The actual output dose of the anesthesia pump is obtained in real time through the data interface of the anesthesia pump, and the deviation between it and the baseline value is calculated to determine whether the deviation exceeds the preset threshold. If the deviation exceeds the threshold, the relevant parameters of the support vector regression model are automatically adjusted according to the direction and size of the deviation to improve the generalization ability and prediction accuracy of the model. The optimized support vector regression model is applied to the adaptive control of the anesthesia pump. According to the patient's real-time physiological parameters and surgical requirements, the output dose of the anesthesia pump is dynamically adjusted through the PID control algorithm to achieve accurate anesthesia management. At the same time, a decision tree model is introduced to conduct real-time evaluation of the patient's physiological state, and the support vector regression model is used to assist in adjusting the anesthetic dose to improve the safety and effectiveness of anesthesia management.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A microfluidic anesthetic bomb control method based on pressure feedback, characterized in that: The method comprises: Obtaining a double-layer structure model of a microfluidic anesthetic bomb, wherein the double-layer structure model includes an inner layer anesthetic storage cavity model and an outer layer elastic adjustable layer material model; For the material model of the outer elastic adjustable layer, a finite element analysis method is used to simulate the deformation and stress distribution of the anesthetic cavity under a preset pressure when the thickness of the outer layer material is discretely sampled within a preset range, and obtain an elastic modulus parameter set of the outer layer material, wherein the parameter set includes elastic modulus values ​​at different thicknesses; Obtain the pressure value of the microfluidic anesthetic bomb monitored in real time by the pressure sensor, and judge whether the current pressure reaches the preset pressure threshold according to the preset pressure threshold. If so, control the valve connecting the inner anesthetic cavity and the outer adjustable layer to open, and squeeze the inner anesthetic to release under the action of pressure according to the elastic modulus parameter set of the outer material; During the anesthetic release process, the volume data of the released anesthetic droplets monitored in real time by the photoelectric sensor is obtained, and the released dose is calculated according to the pre-established functional relationship between the droplet volume and the anesthetic dose; Based on the pressure change data monitored by the pressure sensor, the elastic modulus parameters of the outer material and the calculated anesthetic release dose data, a support vector regression algorithm is used to establish a functional relationship model between material parameters, pressure and dose output. The kernel function and parameters are selected through cross-validation, and the model parameters are trained and optimized. The performance of the functional relationship model is evaluated using a test data set, and the root mean square error and the coefficient of determination are calculated. If the root mean square error is greater than a preset error threshold or the coefficient of determination is less than a preset coefficient threshold, the support vector regression algorithm hyperparameters are adjusted and the model is retrained until a final model that meets the accuracy requirements is obtained; According to the final functional relationship model after verification and optimization, the dose of anesthetic released by the anesthetic bullet with specific outer material parameters under the current pressure conditions is predicted, and the elastic modulus of the anesthetic bullet is controlled by adjusting the thickness of the outer material to achieve precise control of the released dose. The predicted dose result is used as the reference value of the adaptive control; Real-time data of pressure and release dose are continuously collected, and online support vector regression is used as an incremental learning algorithm to merge the newly collected data with the original training data set. The parameters of the functional relationship model are dynamically updated and optimized, and the model parameters are gradually adjusted according to the deviation between the baseline value and the actual dose, thereby improving the generalization ability of the model and the prediction accuracy, and realizing adaptive control of anesthetic dose.

2. The method according to claim 1, characterized in that The double-layer structure model of the microfluidic anesthetic bomb is obtained, and the double-layer structure model includes an inner layer anesthetic storage cavity model and an outer layer elastic adjustable layer material model, including: Acquiring three-dimensional data of the inner layer anesthetic storage cavity model; According to the three-dimensional data, the inner layer anesthetic storage cavity model is parametrically modeled using SolidWorks software to obtain a three-dimensional solid model of the inner layer anesthetic storage cavity; Determining the three-dimensional size parameters of the material model of the outer elastic adjustable layer according to the three-dimensional solid model of the inner layer anesthetic storage cavity; According to the three-dimensional size parameters, the material model of the outer elastic adjustable layer is parametrically modeled using SolidWorks software to obtain a three-dimensional solid model of the outer elastic adjustable layer material; Assembling the three-dimensional solid model of the inner anesthetic storage cavity and the three-dimensional solid model of the outer elastic adjustable layer material to obtain a three-dimensional assembly model of the double-layer structure of the microfluidic anesthetic bomb; Using ANSYS software to perform structural statics analysis on the three-dimensional assembly model to obtain stress distribution and deformation; If the stress distribution and deformation conditions do not meet the preset conditions, the size parameters of the inner anesthetic storage cavity model and the outer elastic adjustable layer material model are adjusted according to the stress distribution and deformation conditions until the preset conditions are met.

3. The method according to claim 2, characterized in that For the material model of the outer elastic adjustable layer, a finite element analysis method is used to simulate the deformation and stress distribution of the anesthetic cavity under a preset pressure when the thickness of the outer layer material is discretely sampled within a preset range, and a set of elastic modulus parameters of the outer layer material is obtained. The parameter set includes elastic modulus values ​​at different thicknesses, including: Obtaining the physical properties of the material of the outer elastic adjustable layer, establishing a constitutive model of the material, and determining the parameters required by the material model; Constructing a three-dimensional geometric model of the anesthetic cavity, and meshing the geometric model using finite element analysis software; For a preset thickness range, a series of thickness values ​​are obtained, outer material parameters at different thicknesses are assigned to the geometric model, a preset pressure is set as a load condition, and a finite element solution is performed; According to the finite element analysis results, the deformation and stress distribution data under different thicknesses and preset pressures are obtained; According to the deformation and stress distribution data, elastic modulus values ​​of the outer layer material at different thicknesses are calculated to obtain an elastic modulus parameter set.

4. The method according to claim 3, characterized in that The pressure sensor is used to obtain the pressure value of the microfluidic anesthetic bomb in real time, and judge whether the current pressure reaches the preset pressure threshold according to the preset pressure threshold; If yes, then the valve connecting the inner anesthetic cavity and the outer adjustable layer is controlled to open, and according to the elastic modulus parameter set of the outer layer material, the inner anesthetic is squeezed and released under pressure, including: Obtaining the force value of the anesthetic bullet monitored in real time by the pressure sensor, comparing the value with a preset threshold, and determining whether the current pressure reaches the preset threshold; If the current pressure reaches a preset threshold, the inner and outer layer connecting valves are controlled to open; According to the anesthetic release rate predicted by the support vector regression model, a PID control algorithm is used to adjust the PWM duty cycle of the inner and outer layer connecting valves in real time to control the valve opening; The parameters of the PID controller are optimized using a genetic algorithm so that the valve control strategy can be adaptively adjusted according to the pressure change trend.

5. The method according to claim 4, characterized in that In the process of releasing the anesthetic, the volume data of the released anesthetic droplets monitored in real time by the photoelectric sensor is obtained, and the released dose is calculated according to the pre-established functional relationship between the droplet volume and the anesthetic dose, including: Obtain the values ​​of the proportional term, integral term and differential term, perform weighted summation on the three values, and obtain the control amount; According to the control amount, adjusting the working parameters of the anesthetic release device to change the anesthetic release rate; The support vector machine algorithm is used to update the preset functional relationship model between the droplet volume and the anesthetic dose in real time. If the functional relationship model is updated, the dose calculation accuracy is improved.

6. The method according to claim 5, characterized in that The method comprises: monitoring the pressure change data of the pressure sensor, the elastic modulus parameters of the outer material and the calculated anesthetic release dose data, using a support vector regression algorithm to establish a material parameter, pressure and dose output function relationship model, selecting a kernel function and parameters through cross-validation, and training and optimizing the model parameters, including: Acquire pressure change data monitored in real time by a pressure sensor, perform denoising and smoothing on the pressure change data, and extract at least one pressure change statistical feature as a model input; Obtain elastic modulus parameters of the outer layer material; Calculating the target anesthetic release dose under the current state according to a preset pharmacokinetic mathematical model in combination with the pressure change statistical characteristics and the elastic modulus parameter; The pressure change statistical characteristics, the elastic modulus parameters and the target anesthetic release dose are divided into a training set, a validation set and a test set, and a support vector regression model is constructed.

7. The method according to claim 6, characterized in that The test data set is used to evaluate the performance of the functional relationship model, and the root mean square error and the coefficient of determination are calculated; if the root mean square error is greater than the preset error threshold or the coefficient of determination is less than the preset coefficient threshold, the support vector regression algorithm hyperparameters are adjusted and the model is retrained until a final model that meets the accuracy requirements is obtained, including: Obtain a test data set, and use the root mean square error RMSE and the coefficient of determination R² as evaluation indicators to evaluate the performance of the support vector regression model; If the RMSE is greater than the preset error threshold, and the R² is less than the preset coefficient threshold, then according to the preset grid search rule, traverse the penalty coefficient C, kernel function type and kernel function parameter γ hyperparameter combination; For each of the hyperparameter combinations, a cross-validation method is used for evaluation, and the hyperparameter combination with the smallest RMSE on the validation set is obtained as the optimal hyperparameter; From the model set, the model with the smallest RMSE and the largest R² is selected and determined as the final functional relationship model.

8. The method according to claim 7, characterized in that The final functional relationship model after verification and optimization is used to predict the anesthetic dose released by the anesthetic bomb with specific outer material parameters under the current pressure conditions, and the elastic modulus of the anesthetic bomb is controlled by adjusting the thickness of the outer material to achieve precise control of the released dose. The predicted dose result is used as a reference value for adaptive control, including: According to the outer material parameters of the anesthetic bomb and the current pressure conditions, the predicted anesthetic release dose is calculated using the optimized functional relationship model; By establishing a mapping relationship between material thickness and elastic modulus, the influence of adjusting material thickness on elastic modulus is determined; According to the predicted release dose, the elastic modulus of the material required to achieve the target release dose is calculated using the mapping relationship; The predicted release dose is used as the reference value of adaptive control, and a feedback control system based on PID control algorithm is established; The sensor is used to monitor the release rate and amount of the anesthetic bullet in real time, and the monitored value is compared with the reference value to calculate the deviation value; According to the deviation value, the PID control algorithm is used to calculate the adjustment amount of material thickness, and the material thickness is dynamically adjusted through the actuator so that the release dose continues to meet the requirements of precise control.

9. The method according to claim 8, characterized in that The method continuously collects real-time data of pressure and released dose, adopts online support vector regression as an incremental learning algorithm, merges newly collected data with the original training data set, dynamically updates and optimizes the parameters of the functional relationship model, gradually adjusts the model parameters according to the deviation between the reference value and the actual dose, improves the generalization ability and prediction accuracy of the model, and realizes adaptive control of anesthetic dose, including: Acquire data collected in real time by a pressure sensor and an anesthesia pump, and associate the pressure data with the dosage data and store them in a time series database; Adopting a gradient boosting decision tree algorithm, retraining the support vector regression model using the merged data set, and dynamically updating the parameters of the model; The optimized support vector regression model is applied to the adaptive control of the anesthesia pump. According to the patient's real-time physiological parameters and surgical requirements, the output dose of the anesthesia pump is dynamically adjusted through the PID control algorithm to achieve precise anesthesia management.

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