Drug delivery system based on MEMS microfluidic anesthetic bullet

Abstract

The present invention relates to the field of data regulation technology. The drug release system based on MEMS microfluidic anesthetic bullets provided by the present invention solves the problem that existing anesthetic bullet drug release systems cannot promptly detect blockages in the drug release path. The system includes modules such as data acquisition, model construction, data collection, drug release detection, and detection result verification. By acquiring drug release task information and matching it with a preset knowledge base, a random forest model is used to simulate the drug release process, and drug release data is collected in real time and compared with the simulation results. Blockages in the drug release path can be promptly detected and warned, thereby improving the stability of drug release. The system can also verify whether the real-time drug release data exceeds the standard.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a drug release system based on a MEMS microfluidic anesthetic bullet. Background Art

[0002] A microfluidic anesthetic cartridge based on MEMS (MEMS stands for Micro-Electro-Mechanical System, also known as micro-electromechanical system in Chinese) is a medical device that integrates microfluidic chip technology. MEMS technology miniaturizes mechanical systems through semiconductor technology to form micro-electromechanical systems. Microfluidics utilizes this characteristic to construct functional components such as channels and reaction chambers for fluids at the micron scale, thereby manipulating the movement of micron-sized fluids in tiny spaces. The MEMS-based microfluidic anesthetic cartridge integrates various functional units, such as microreactors, micropumps, and microvalves, through microfabrication techniques, achieving precise control and release of anesthetic drugs.

[0003] The drug release process of the MEMS microfluidic anesthetic bullet is a controllable miniaturized process. The anesthetic drug is pre-loaded into the micro-liquid storage chamber in the microfluidic chip. The micropump and microvalve system driven by MEMS technology can accurately control the flow path and release rate of the drug according to the preset program or external instructions. The drug flows in the microchannel on demand and is finally delivered to the target area in a trace, continuous or pulsed manner through a micro nozzle or release hole, realizing the timed, quantitative and fixed-point release of the anesthetic drug.

[0004] The drug release system based on MEMS microfluidic anesthetic bullets has the following technical pain points during actual use. Under complex and changeable physiological environments, the drug release path of the anesthetic bullet will be bio-contaminated and blocked. The existing drug release system of the anesthetic bullet cannot detect the blockage of the drug release path of the anesthetic bullet in time through effective data analysis, resulting in reduced safety of the drug release system of the anesthetic bullet. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a drug release system based on MEMS microfluidic anesthetic bullets to solve the problem that the existing drug release systems of anesthetic bullets cannot timely detect blockages in the drug release path of the anesthetic bullets through effective data analysis.

[0006] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0007] The present invention provides a drug release system based on a MEMS microfluidic anesthetic bullet, comprising:

[0008] The data acquisition module is used to obtain drug release task information, match the drug release task information with the preset drug knowledge base of the anesthetic cartridge, and obtain the drug standard information of the anesthetic cartridge. The drug standard information of the anesthetic cartridge includes the drug standard concentration, drug standard flow rate, and drug standard release rate;

[0009] A model building module is used to obtain historical data of the drug release system of the anesthetic bullet, train a random forest model using the historical data of the drug release system of the anesthetic bullet, and obtain a drug release simulation model of the anesthetic bullet;

[0010] a data acquisition module, configured to transmit drug release task information to a drug release terminal of the anesthetic cartridge, and to collect real-time drug release data when the drug release terminal of the anesthetic cartridge performs the drug release task, wherein the real-time drug release data includes real-time drug concentration, fluid flow rate in a real-time biochip microchannel, temperature difference between the real-time internal and external environments of the real-time biochip, real-time drug release rate, pressure distribution at different positions in the real-time microchannel, real-time drug molecular diffusion coefficient, pH value of the real-time internal and external environments of the real-time biochip, real-time ion concentration change value, real-time drug diffusion area, real-time drug effect value, and real-time change value of simulated biological hormones in the experimental environment;

[0011] The drug release detection module is used to substitute the drug release task information into the drug release simulation model of the anesthetic bullet and output the drug release simulation results of the anesthetic bullet. The drug release simulation results of the anesthetic bullet include the drug simulation concentration, the fluid flow rate in the simulated biochip microchannel, the temperature difference between the internal and external environments of the simulated biochip, the drug release simulation rate, the pressure distribution at different positions in the simulated microchannel, the simulated drug molecular diffusion coefficient, the pH value of the simulated internal and external environments of the biochip, the simulated ion concentration change value, the simulated drug diffusion area, the simulated drug effect value and the simulated biological hormone simulation change value in the experimental environment. The drug release simulation results of the anesthetic bullet are compared with the real-time drug release data to obtain the drug release simulation results of the anesthetic bullet. The simulation result is compared with the real-time drug release data, and the comparison result includes whether the comparison result is within a preset error range and whether the comparison result is not within the preset error range. If the comparison result is within the preset error range, the real-time drug release data is transmitted to the detection result verification unit. If the comparison result is not within the preset error range, a drug release path detection task is generated, and the drug release path detection task is sent to the drug release terminal of the anesthetic bullet to obtain a drug release path detection result. The drug release path detection result is matched with a preset drug path detection result knowledge base to obtain a drug release path detection analysis result. The drug release path detection analysis result includes whether the drug release path is blocked and whether the drug release path is not blocked. When the drug release path is blocked, a drug release path blockage warning information is generated;

[0012] The test result verification unit is used to compare the real-time drug release data with the drug standard information of the anesthetic bullet. If the real-time drug release data exceeds the drug standard information of the anesthetic bullet in the comparison result, a drug release exceeding standard warning information is generated.

[0013] Furthermore, in the flow regulation system of the anesthetic bullet of the present invention, the data acquisition module is further used to:

[0014] The drug release task information is parsed to obtain the parsed drug release task information, which includes: drug name, drug type, expected drug standard concentration, expected drug release rate, expected fluid flow rate, expected temperature difference between the internal and external environments of the biochip, expected pH value of the internal and external environments of the biochip, range of changes in simulated biological hormones in the experimental environment, expected pressure distribution at different positions in the microchannel, expected range of drug molecular diffusion coefficient, expected range of changes in ion concentration, expected drug diffusion area, and expected drug effect value.

[0015] Furthermore, in the flow regulation system of the anesthetic bullet of the present invention, the model building module is further used to:

[0016] Feature extraction is performed on the historical data of the drug release system of the anesthetic bullet to obtain the drug release process characteristics. The drug release process characteristics include drug concentration characteristics, flow rate characteristics, temperature difference characteristics, pressure distribution characteristics, pH value characteristics and ion concentration characteristics. The drug release process characteristics are used as the input of the model.

[0017] Initialize the random forest model parameters, including learning rate, number of iterations, and random seed;

[0018] The drug release process characteristics were input into the selected random forest model, and the model parameters were adjusted to minimize the prediction error using the gradient descent algorithm to obtain the drug release simulation model of the anesthetic bullet.

[0019] Furthermore, in the flow regulation system of the anesthetic bullet of the present invention, the data acquisition module is further used to:

[0020] The drug release task information is sent to the drug release terminal of the anesthetic bomb, and the concentration changes of the drug during the release process are monitored in real time by chemical sensors. The real-time flow rate of the fluid in the microchannel is measured using a flow meter or flow sensor. The real-time temperature of the environment inside and outside the biochip is measured using a temperature sensor, and the temperature difference is calculated. By monitoring the working status of the drug release device, the drug release rate is calculated in real time. The pressure sensor is used to measure the pressure at different positions in the microchannel to obtain a real-time pressure distribution diagram. By observing the diffusion behavior of drug molecules in the medium, the diffusion coefficient is calculated in real time using a diffusion model. The pH sensor is used to monitor the pH value of the environment inside and outside the biochip in real time. The concentration changes of ions in the solution are monitored in real time using ion selective electrodes or ion chromatographs. The diffusion area of ​​the drug in the medium is measured in real time through image processing technology or direct observation. The real-time effect of the drug on the experimental subject is monitored through biosensors or physiological indicators.

[0021] Furthermore, the flow regulation system of the anesthetic cartridge of the present invention and the drug release detection module are further used to:

[0022] Receive drug release task information from the data acquisition module, parse the drug release task information, extract drug release task parameters, and obtain drug type, concentration requirement, release rate, and environmental conditions;

[0023] Load the pre-trained anesthetic drug release simulation model, initialize the model parameters, substitute the parsed drug release task information as input parameters into the simulation model, and receive and set the model's boundary conditions and initial conditions;

[0024] Start the drug release simulation model of the anesthetic bullet and begin to simulate the drug release process;

[0025] The drug release simulation model of the anesthetic bullet calculates and outputs the simulation results of the drug release process based on the input parameters and internal algorithms;

[0026] Simulation results of drug release process, including:

[0027] Drug simulation concentration: simulated drug concentration changes during the release process;

[0028] Simulating the flow rate of the fluid in the microchannel of the biochip: the flow rate distribution of the fluid in the microchannel obtained by simulation;

[0029] Simulate the temperature difference between the inside and outside of the biochip: simulate the temperature difference between the inside and outside of the biochip;

[0030] Drug release simulation rate: the simulated drug release rate changes over time;

[0031] Simulate the pressure distribution at different locations in the microchannel: the pressure value of each point in the microchannel obtained by simulation;

[0032] Simulated drug molecule diffusion coefficient: the simulated diffusion coefficient of drug molecules in the medium;

[0033] Simulated pH value of the internal and external environment of the biochip: simulated pH value of the internal and external environment of the biochip;

[0034] Simulated ion concentration change value: the concentration change of ions in the simulated solution;

[0035] Simulated drug diffusion area: the simulated drug diffusion area in the medium;

[0036] Simulated drug effect value: the expected effect of the drug on the experimental subject according to the simulation conditions;

[0037] Simulated changes in biohormones in the experimental environment: If the model includes this function, the simulated changes in biohormones.

[0038] Furthermore, the flow regulation system of the anesthetic cartridge of the present invention and the drug release detection module are further used to:

[0039] Aligning the drug release simulation results and real-time drug release data of the anesthetic bullet based on time, thereby obtaining aligned drug release simulation results and real-time drug release data of the anesthetic bullet;

[0040] The drug release simulation results of the aligned anesthetic bullet and each indicator in the real-time drug release data are used to calculate the error between the simulation results and the real-time data;

[0041] The error between the simulation result and the real-time data includes absolute error and relative error. The absolute error is the difference between the simulation value and the real-time value, and the relative error is the difference between the simulation value and the real-time value divided by the real-time value.

[0042] Comparing the calculated error with a preset error range, where the preset error range is a pre-received error range;

[0043] If the errors of all indicators are within the preset error range, the comparison result is judged to be within the preset error range;

[0044] If the error of any one or more indicators exceeds the preset error range, the comparison result is determined to be outside the preset error range;

[0045] If the comparison result is within the preset error range, the simulation result is consistent with the real-time data, and the real-time drug release data is transmitted to the test result verification unit;

[0046] If the comparison result is not within the preset error range, there is a difference between the simulation result and the real-time data.

[0047] Furthermore, the flow regulation system of the anesthetic cartridge of the present invention and the drug release detection module are further used to:

[0048] Comparing the drug release simulation results of the anesthetic bullet with the real-time drug release data, an error index comparison result is obtained, and the error index comparison result includes an index of error exceeding a preset range;

[0049] Based on the error index comparison results, matching is performed in the preset generated drug release path knowledge base, and the index with errors exceeding the preset range is used as the matching basis to obtain the drug release path detection task;

[0050] The drug release path detection task includes the specific detection path and the expected detection indicators;

[0051] Encapsulate the information of the drug release path detection task into a standard task instruction, which includes the detection path, detection indicators, and detection time. Send the encapsulated task instruction to the drug release terminal of the anesthetic bomb, monitor the execution of the task instruction by the drug release terminal, and receive the drug release path detection results from the drug release terminal. The detection results should include the flow rate, pressure, and temperature along the detection path.

[0052] The received test results are matched with a preset drug path test result knowledge base, and based on the matching results, it is determined whether the drug release path is blocked.

[0053] Beneficial effects of the present invention:

[0054] The present invention uses a data acquisition module to match drug release task information with a pre-set drug knowledge base, obtaining drug standard information and ensuring that parameters such as drug release concentration, flow rate, and rate meet standard requirements. A drug release simulation model constructed using a random forest model can predict changes in various parameters during the drug release process, improving the accuracy of drug release predictions.

[0055] Data from the drug release process is collected in real time and compared with simulation results through the drug release detection module to promptly identify and correct deviations. If a blockage risk is detected in the drug release path, the system generates an early warning message, prompting the user or system administrator to take appropriate measures to avoid potential safety issues. The data acquisition module's high-precision sensors and advanced monitoring technology, combined with comparative verification of simulated and real-time data, enable the system to continuously optimize drug release strategies and improve the stability and consistency of drug release. The system automatically records and analyzes historical data from the drug release process, providing data support for subsequent model training and optimization. Through intelligent data analysis and management, the system continuously learns and improves the efficiency of drug release.

[0056] In summary, the drug release system based on MEMS microfluidic anesthetic bullets has brought significant progress to the medical field by improving the accuracy of drug release, enhancing system safety, improving system reliability, supporting intelligent management and optimization, and promoting the development of medical technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0058] Figure 1 Schematic diagram of a unit module of a drug release system based on a MEMS microfluidic anesthetic bullet provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below in conjunction with the drawings.

[0060] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0061] The present invention provides a drug release system based on a MEMS microfluidic anesthetic bullet, comprising:

[0062] The data acquisition module is used to obtain drug release task information, match the drug release task information with the preset drug knowledge base of the anesthetic cartridge, and obtain the drug standard information of the anesthetic cartridge. The drug standard information of the anesthetic cartridge includes the drug standard concentration, drug standard flow rate, and drug standard release rate;

[0063] The data acquisition module is responsible for acquiring drug release task information and matching it with the preset anesthetic bullet drug knowledge base to obtain the drug standard information of the anesthetic bullet.

[0064] The data acquisition module first receives drug release task information from the user or system, which usually includes the drug name, drug type, expected drug concentration, expected drug release rate, expected fluid flow rate, expected temperature difference between the internal and external environments of the biochip, pH value, range of changes in simulated biological hormones in the experimental environment, expected pressure distribution at different positions in the microchannel, expected range of drug molecular diffusion coefficient, expected range of changes in ion concentration, expected drug diffusion area, and expected drug effect value, etc.

[0065] The data acquisition module matches the drug release task information with the preset anesthetic bullet drug knowledge base. The drug knowledge base is a pre-built and stored database that contains standard information of various anesthetic bullet drugs, such as standard drug concentration, standard drug flow rate, standard drug release rate, etc.

[0066] Through the matching process, the data acquisition module can extract the drug standard information corresponding to the current drug release task. The standard information provides an important reference for subsequent model construction, data collection, drug release detection and test result verification.

[0067] Drug standard information includes but is not limited to the following parameters:

[0068] Standard drug concentration: refers to the standard concentration value that the anesthetic bullet should reach when releasing the drug.

[0069] Standard drug flow rate: refers to the standard flow rate of the drug in the anesthetic bullet during the release process.

[0070] Standard drug release rate: refers to the standard rate of release of anesthetic drugs, usually measured by the amount of drug released per unit time.

[0071] The data acquisition module provides accurate drug standard information for the entire drug release system by acquiring and matching drug release task information. By comparing real-time drug release data with drug standard information, the system can promptly detect and correct deviations, thereby avoiding problems such as blockage of the drug release path.

[0072] A model building module is used to obtain historical data of the drug release system of the anesthetic bullet, train a random forest model using the historical data of the drug release system of the anesthetic bullet, and obtain a drug release simulation model of the anesthetic bullet;

[0073] The model building module is responsible for obtaining the historical data of the drug release system of the anesthetic bullet and using the data to train the random forest model to obtain the drug release simulation model of the anesthetic bullet. The drug release simulation model of the anesthetic bullet can predict and simulate various parameters and states in the drug release process.

[0074] The drug release simulation model of the anesthetic bullet first collects the historical data of the anesthetic bullet drug release system. The historical data of the anesthetic bullet drug release system includes various parameters and status information in the previous drug release process, such as drug concentration, flow rate, temperature difference, pressure distribution, pH value, ion concentration change value, etc.

[0075] The model building module extracts features from historical data to identify characteristic variables closely related to the drug release process. Characteristic variables typically include drug concentration characteristics, flow rate characteristics, temperature difference characteristics, pressure distribution characteristics, pH value characteristics, and ion concentration characteristics.

[0076] Initializing the Random Forest Model Parameters: Before building the model, you need to initialize the parameters of the Random Forest model. Parameters include the learning rate, number of iterations, and random seed, which have a significant impact on the model's training process and final performance.

[0077] Training the random forest model: Using the extracted drug release process features as input, historical data is fed into the random forest model for training. Model parameters are continuously adjusted (e.g., using a gradient descent algorithm) to gradually optimize model performance with the goal of minimizing prediction error.

[0078] Obtaining a simulation model for drug release from an anesthetic bullet: After sufficient training, the model building module will generate a stable simulation model for drug release from an anesthetic bullet. This model can accurately simulate and predict various parameters and states during the drug release process.

[0079] The random forest model improves prediction accuracy by integrating multiple decision trees and is able to handle complex nonlinear relationships.

[0080] The random forest model is highly tolerant to noise and outliers and is less susceptible to overfitting. Although it is a black-box model, it can be used to assess the impact of each feature on the prediction results through metrics such as feature importance.

[0081] The model building module provides powerful prediction and simulation capabilities for the entire drug release system by constructing a simulation model for the drug release of anesthetic cartridges. This enables the system to more accurately predict various parameters and states during the drug release process, thereby promptly identifying and correcting deviations and improving the stability of drug release.

[0082] a data acquisition module, configured to transmit drug release task information to a drug release terminal of the anesthetic cartridge, and to collect real-time drug release data when the drug release terminal of the anesthetic cartridge performs the drug release task, wherein the real-time drug release data includes real-time drug concentration, fluid flow rate in a real-time biochip microchannel, temperature difference between the real-time internal and external environments of the real-time biochip, real-time drug release rate, pressure distribution at different positions in the real-time microchannel, real-time drug molecular diffusion coefficient, pH value of the real-time internal and external environments of the real-time biochip, real-time ion concentration change value, real-time drug diffusion area, real-time drug effect value, and real-time change value of simulated biological hormones in the experimental environment;

[0083] The data acquisition module is responsible for sending the drug release task information to the drug release terminal of the anesthetic bullet and collecting various data of the terminal in real time when performing the drug release task.

[0084] Sending Drug Release Task Information: The data acquisition module first receives drug release task information from the system or user, which details the type of drug to be released, its concentration, rate, and other relevant parameters. The data acquisition module then transmits this information to the drug release terminal on the anesthetic cartridge, directing it to execute the specific drug release operation.

[0085] During the process of the anesthetic bullet drug release terminal performing the drug release task, the data acquisition module begins to collect various data in real time, including but not limited to:

[0086] Real-time drug concentration: Real-time monitoring of drug concentration changes during the release process through chemical sensors.

[0087] Real-time flow rate of fluid in microchannels of biochips: Use a flow meter or flow sensor to measure the real-time flow rate of fluid in microchannels.

[0088] Real-time temperature difference between the internal and external environments of the biochip: Use a temperature sensor to measure the real-time temperature of the internal and external environments of the biochip and calculate the temperature difference.

[0089] Real-time drug release rate: By monitoring the working status of the drug release device, the drug release rate can be calculated in real time.

[0090] Real-time pressure distribution at different locations in the microchannel: Use a pressure sensor to measure pressure at different locations in the microchannel to obtain a real-time pressure distribution map.

[0091] Real-time drug molecule diffusion coefficient: By observing the diffusion behavior of drug molecules in the medium, the diffusion coefficient is calculated in real time using the diffusion model.

[0092] Real-time pH value of the environment inside and outside the biochip: Use pH sensors to monitor the acidity and alkalinity of the environment inside and outside the biochip in real time.

[0093] Real-time ion concentration change value: Real-time monitoring of ion concentration changes in the solution through ion selective electrodes or ion chromatographs.

[0094] Real-time drug diffusion area: Real-time measurement of the drug diffusion area in the medium through image processing technology or direct observation.

[0095] Real-time drug effect value: monitor the real-time effect of drugs on experimental subjects through biosensors or physiological indicators.

[0096] Real-time changes in simulated biological hormones in the experimental environment: If the experimental environment contains settings for simulating changes in biological hormones, their changes are monitored in real time.

[0097] The data acquisition module provides a solid foundation for subsequent simulation, testing and verification by collecting various data during the anesthetic bullet drug release process in real time and accurately. It not only helps the system to control the drug release process more accurately, but also can promptly detect and correct possible deviation problems, such as blockage of the drug release path.

[0098] The drug release detection module is used to substitute the drug release task information into the drug release simulation model of the anesthetic bullet and output the drug release simulation results of the anesthetic bullet. The drug release simulation results of the anesthetic bullet include the drug simulation concentration, the fluid flow rate in the simulated biochip microchannel, the temperature difference between the internal and external environments of the simulated biochip, the drug release simulation rate, the pressure distribution at different positions in the simulated microchannel, the simulated drug molecular diffusion coefficient, the pH value of the simulated internal and external environments of the biochip, the simulated ion concentration change value, the simulated drug diffusion area, the simulated drug effect value and the simulated biological hormone simulation change value in the experimental environment. The drug release simulation results of the anesthetic bullet are compared with the real-time drug release data to obtain the drug release simulation results of the anesthetic bullet. The simulation result is compared with the real-time drug release data, and the comparison result includes whether the comparison result is within a preset error range and whether the comparison result is not within the preset error range. If the comparison result is within the preset error range, the real-time drug release data is transmitted to the detection result verification unit. If the comparison result is not within the preset error range, a drug release path detection task is generated, and the drug release path detection task is sent to the drug release terminal of the anesthetic bullet to obtain a drug release path detection result. The drug release path detection result is matched with a preset drug path detection result knowledge base to obtain a drug release path detection analysis result. The drug release path detection analysis result includes whether the drug release path is blocked and whether the drug release path is not blocked. When the drug release path is blocked, a drug release path blockage warning information is generated;

[0099] The drug release detection module is an integral part of the present invention. It is responsible for substituting drug release task information into the drug release simulation model of the anesthetic cartridge, outputting the simulation results, and comparing the simulation results with the real-time drug release data to determine whether the drug release process is normal. The following are the specific functions and operation process of the drug release detection module:

[0100] The drug release detection module's primary function is to insert drug release task information into a pre-trained anesthetic cartridge drug release simulation model and calculate the simulated drug release results. The module then compares the simulated results with real-time drug release data, analyzing the differences and determining whether there are any blockages or other issues in the drug release pathway.

[0101] The information is fed into the drug release simulation model for the anesthetic cartridge. The model calculates and outputs the simulated drug release results based on the input task information and internal algorithms. These simulation results include simulated drug concentration, fluid flow rate within the simulated biochip microchannel, simulated temperature difference between the internal and external environments of the biochip, simulated drug release rate, simulated pressure distribution at different locations within the microchannel, simulated drug molecular diffusion coefficient, simulated pH value of the internal and external environments of the biochip, simulated ion concentration changes, simulated drug diffusion area, simulated drug effect values, and simulated changes in the experimental environment's biohormones.

[0102] The drug release detection module simultaneously receives the real-time drug release data from the data acquisition module and compares the simulation results with the real-time data, including all the real-time data items corresponding to the above simulation results.

[0103] The comparison results fall into two categories: those within the preset error range and those outside it. The preset error range is set based on system requirements and actual application scenarios to determine whether the deviation between the simulation results and the actual data is within an acceptable range.

[0104] If the comparison result is within the preset error range, it means that the simulation result is consistent with the real-time data and the drug release process is normal. At this time, the drug release detection module transmits the real-time drug release data to the detection result verification unit for further verification.

[0105] If the comparison result is not within the preset error range, it means that there is a difference between the simulation result and the real-time data, and there may be problems such as drug release path blockage. In this case, the drug release detection module will generate a drug release path detection task.

[0106] The drug release detection task includes information such as the specific detection path and the expected detection indicators.

[0107] The mission information is encapsulated into standard mission instructions and sent to the drug release terminal of the anesthetic bullet.

[0108] Monitor the execution of task instructions by the drug release terminal and receive the detection results of the drug release path from the terminal.

[0109] Analyze test results and generate warning information:

[0110] The received test results are matched and analyzed with the preset drug pathway test result knowledge base.

[0111] Based on the matching results, determine whether there are problems such as blockage in the drug release path.

[0112] If there is a blockage problem, an early warning message of drug release path blockage will be generated and relevant personnel will be notified in time to handle it.

[0113] By comparing simulation results with real-time data, the drug release detection module can promptly identify potential problems in the drug release process, such as blockage in the drug release pathway. This helps improve the safety of the drug release system, ensuring that the drug is accurately delivered to the target area according to the intended method and dosage, thereby achieving better therapeutic effects. The module also provides data support for further optimization and improvement of the system.

[0114] The test result verification unit is used to compare the real-time drug release data with the drug standard information of the anesthetic bullet. If the real-time drug release data exceeds the drug standard information of the anesthetic bullet in the comparison result, a drug release exceeding standard warning information is generated.

[0115] The test result verification unit is responsible for comparing and verifying the real-time collected drug release data with the preset anesthetic bullet drug standard information. If it is found that the real-time data exceeds the range specified by the standard information, the system will automatically generate a drug release excess warning information to prompt the operator or the system to automatically take corresponding measures.

[0116] The test result verification unit first receives real-time drug release data from the data acquisition module. This data includes multiple indicators such as real-time drug concentration, real-time fluid flow rate within the biochip microchannel, real-time temperature difference between the biochip's internal and external environments, and real-time drug release rate.

[0117] Next, the test result verification unit will obtain the drug standard information corresponding to the current drug release task from the preset anesthetic drug knowledge base. The standard information usually includes the drug standard concentration, drug standard flow rate, drug standard release rate, etc.

[0118] The real-time drug release data is compared with the standard information of the anesthetic cartridge one by one. During the comparison process, the system checks whether each indicator is within the range specified by the standard information.

[0119] If any real-time drug release data is found to exceed the corresponding drug standard information, the test result verification unit will determine that the drug release exceeds the standard.

[0120] In this case, the system will automatically generate an alert indicating that the drug release exceeds the specified limit. This alert will clearly indicate which indicators have exceeded the specified limit and the extent of the exceedance, allowing operators to quickly understand the problem and take appropriate corrective measures.

[0121] The inclusion of a test result verification unit enhances the stability of the drug release process. Through real-time comparison and verification, the system promptly detects and issues warnings of potential exceedances, thereby avoiding the risks associated with improper drug release. This is crucial for improving the reliability of medical devices and ensuring patient safety. Furthermore, this unit provides robust data support for ongoing system optimization and improvement.

[0122] Specifically, the flow regulation system of the anesthetic bullet of the present invention, the data acquisition module is further used to:

[0123] The drug release task information is parsed to obtain the parsed drug release task information, which includes: drug name, drug type, expected drug standard concentration, expected drug release rate, expected fluid flow rate, expected temperature difference between the internal and external environments of the biochip, expected pH value of the internal and external environments of the biochip, range of changes in simulated biological hormones in the experimental environment, expected pressure distribution at different positions in the microchannel, expected range of drug molecular diffusion coefficient, expected range of changes in ion concentration, expected drug diffusion area, and expected drug effect value.

[0124] The data acquisition module comprehensively analyzes the received drug release task information and extracts more detailed and specific parameters so that subsequent modules can perform related operations more accurately.

[0125] The data acquisition module first receives drug release task information from the user or the system's upper-level module, which usually exists in a structured or unstructured form and contains the basic instructions required to perform the drug release task.

[0126] Next, the data acquisition module conducts in-depth analysis of the received task information. This analysis process aims to convert the task information into a format that can be understood and processed by the system.

[0127] During the parsing process, the data acquisition module extracts the following parameters:

[0128] Drug Name: Clearly indicate the type of drug to be released.

[0129] Drug type: describes the chemical or biological classification of a drug, which helps in systematically understanding the characteristics of the drug.

[0130] Desired drug concentration: Set the target concentration range that should be maintained during drug release.

[0131] Expected Drug Release Rate: Specifies the speed or rate at which the drug is released from the anesthetic cartridge.

[0132] Expected fluid flow rate: The velocity that the fluid flowing in the microchannel should reach.

[0133] Expected temperature difference between the inside and outside environments of the biochip: Set the ideal temperature difference between the inside and outside environments of the biochip.

[0134] Expected pH value of the environment inside and outside the biochip: Specifies the pH range of the environment inside and outside the biochip.

[0135] Range of simulated biohormone variation in experimental settings: The expected range of variation of biohormones under simulated experimental conditions.

[0136] Expected pressure distribution at different locations within the microchannel: Expected pressure distribution at different locations within the microchannel.

[0137] Expected range of drug molecule diffusion coefficients: The expected range of rates or coefficients for drug molecules to diffuse in a medium.

[0138] Expected range of variation in ion concentration: The expected range of variation in ion concentration in solution.

[0139] Expected drug diffusion area: The area over which the drug is expected to diffuse in the medium.

[0140] Expected drug effect value: the expected effect or degree of action of the drug on the experimental subjects.

[0141] By deeply analyzing drug release task information, the data acquisition module provides the system with more detailed and comprehensive parameter support. These parameters not only help the system more accurately simulate the drug release process but also provide a crucial reference for subsequent data collection, model building, and test result verification. This also enhances the system's flexibility and adaptability, enabling it to handle diverse drug release tasks of varying types and specifications.

[0142] Specifically, the flow regulation system of the anesthetic bomb of the present invention, the model building module is further used to:

[0143] Feature extraction is performed on the historical data of the drug release system of the anesthetic bullet to obtain the drug release process characteristics. The drug release process characteristics include drug concentration characteristics, flow rate characteristics, temperature difference characteristics, pressure distribution characteristics, pH value characteristics and ion concentration characteristics. The drug release process characteristics are used as the input of the model.

[0144] Initialize the random forest model parameters, including learning rate, number of iterations, and random seed;

[0145] The drug release process characteristics were input into the selected random forest model, and the model parameters were adjusted to minimize the prediction error using the gradient descent algorithm to obtain the drug release simulation model of the anesthetic bullet.

[0146] In a drug release system based on MEMS microfluidic anesthetic cartridges, the module is responsible for using historical data to train and optimize the drug release simulation model. The following is a detailed description of the specific functions and operations of the model building module:

[0147] The model building module is mainly responsible for extracting features from the historical data of the drug release system of the anesthetic bullet, and using the features to build and train the random forest model to achieve accurate simulation of the drug release process.

[0148] Feature Extraction: The model building module conducts in-depth analysis of the historical data of the drug release system of the anesthetic cartridge to extract features closely related to the drug release process. Features include, but are not limited to, drug concentration, flow rate, temperature difference, pressure distribution, pH value, and ion concentration.

[0149] The extracted features are designed to comprehensively reflect the various physical, chemical, and biological changes during drug release, providing rich input information for subsequent model construction.

[0150] Model Initialization: The Model Building module initializes the parameters of the random forest model. These parameters are crucial to model performance and include the learning rate, number of iterations, and random seed. The learning rate determines the step size for parameter updates during training, the number of iterations controls the number of epochs of model training, and the random seed ensures repeatability of model training.

[0151] Model training: The extracted drug release process features are used as input into the selected random forest model.

[0152] Using optimization methods such as gradient descent, the model parameters were continuously adjusted to minimize prediction error and train the model. After multiple iterations and optimizations, a drug release simulation model for anesthetic bullets was ultimately developed that accurately simulated the drug release process.

[0153] The operation of the model building module is of great significance for improving the safety of drug delivery systems. By using historical data to train and optimize the model, the system can more accurately predict various changes in the drug release process, thus providing more precise guidance for subsequent drug delivery tasks.

[0154] Specifically, the flow regulation system of the anesthetic bullet of the present invention, the data acquisition module is also used to:

[0155] The drug release task information is sent to the drug release terminal of the anesthetic bomb, and the concentration changes of the drug during the release process are monitored in real time by chemical sensors. The real-time flow rate of the fluid in the microchannel is measured using a flow meter or flow sensor. The real-time temperature of the environment inside and outside the biochip is measured using a temperature sensor, and the temperature difference is calculated. By monitoring the working status of the drug release device, the drug release rate is calculated in real time. The pressure sensor is used to measure the pressure at different positions in the microchannel to obtain a real-time pressure distribution diagram. By observing the diffusion behavior of drug molecules in the medium, the diffusion coefficient is calculated in real time using a diffusion model. The pH sensor is used to monitor the pH value of the environment inside and outside the biochip in real time. The concentration changes of ions in the solution are monitored in real time using ion selective electrodes or ion chromatographs. The diffusion area of ​​the drug in the medium is measured in real time through image processing technology or direct observation. The real-time effect of the drug on the experimental subject is monitored through biosensors or physiological indicators.

[0156] In the drug release system based on MEMS microfluidic anesthetic bullets, the data acquisition module is responsible for sending drug release task information to the drug release terminal of the anesthetic bullet. It also undertakes the task of real-time collection and monitoring of various data. The following is a detailed description of the functions of the data acquisition module:

[0157] The data acquisition module integrates multiple high-precision sensors and advanced monitoring technologies to collect various data in real time during the release process of the anesthetic bullet, including but not limited to drug concentration, fluid flow rate, ambient temperature, drug release rate, pressure distribution, drug molecular diffusion coefficient, pH value, ion concentration, drug diffusion area, and drug effect. This data provides a solid foundation for precise system control and subsequent analysis.

[0158] Sending drug release task information, the data acquisition module first receives the drug release task information from other modules of the system, and sends the information to the drug release terminal of the anesthetic bullet to guide the terminal to perform specific drug release operations.

[0159] Real-time monitoring of drug concentration: Chemical sensors are used to monitor drug concentration changes during the release process in real time to ensure that the drug can be released according to the predetermined concentration.

[0160] Measuring fluid flow rate: Use a flow meter or flow sensor to accurately measure the real-time flow rate of the fluid in the microchannel to monitor the speed and stability of drug flow.

[0161] Monitor ambient temperature and calculate temperature difference: Use temperature sensors to measure the real-time temperature of the environment inside and outside the biochip and calculate the temperature difference to evaluate the impact of ambient temperature on the drug release process.

[0162] Calculate drug release rate: By monitoring the working status of the drug release device, calculate the drug release rate in real time to ensure that the drug can be released at the predetermined rate.

[0163] Measuring pressure distribution: Use pressure sensors to measure pressure at different locations in the microchannel to obtain real-time pressure distribution maps to monitor pressure changes during drug flow.

[0164] Calculate drug molecule diffusion coefficient: By observing the diffusion behavior of drug molecules in the medium, the diffusion coefficient is calculated in real time using the diffusion model to evaluate the diffusion ability and stability of the drug.

[0165] pH monitoring: Use a pH sensor to monitor the pH value inside and outside the biochip in real time to ensure that the drug is released within the appropriate pH range.

[0166] Monitoring ion concentration changes: ion concentration changes in the solution can be monitored in real time using ion selective electrodes or ion chromatographs to assess changes in ion balance during drug release.

[0167] Measuring drug diffusion area: Using image processing technology or direct observation, the drug diffusion area in the medium is measured in real time to evaluate the drug coverage and release effect.

[0168] Monitoring drug effects: Monitor the real-time effects of drugs on experimental subjects through biosensors or physiological indicators to evaluate the efficacy and safety of drugs.

[0169] The operation of the data acquisition module is crucial for achieving precise control and real-time monitoring of the drug delivery system based on MEMS microfluidic anesthetic cartridges. By comprehensively collecting and monitoring various data during the drug delivery process, the system can promptly detect and adjust abnormalities, ensuring that the drug is accurately released according to the predetermined plan. This not only improves the reliability and safety of the system but also provides valuable data support for subsequent drug development and optimization.

[0170] Specifically, the flow regulation system of the anesthetic bullet of the present invention and the drug release detection module are further used to:

[0171] Receive drug release task information from the data acquisition module, parse the drug release task information, extract drug release task parameters, and obtain drug type, concentration requirement, release rate, and environmental conditions;

[0172] Load the pre-trained anesthetic drug release simulation model, initialize the model parameters, substitute the parsed drug release task information as input parameters into the simulation model, and receive and set the model's boundary conditions and initial conditions;

[0173] Start the drug release simulation model of the anesthetic bullet and begin to simulate the drug release process;

[0174] The drug release simulation model of the anesthetic bullet calculates and outputs the simulation results of the drug release process based on the input parameters and internal algorithms;

[0175] Simulation results of drug release process, including:

[0176] Drug simulation concentration: simulated drug concentration changes during the release process;

[0177] Simulating the flow rate of the fluid in the microchannel of the biochip: the flow rate distribution of the fluid in the microchannel obtained by simulation;

[0178] Simulate the temperature difference between the inside and outside of the biochip: simulate the temperature difference between the inside and outside of the biochip;

[0179] Drug release simulation rate: the simulated drug release rate changes over time;

[0180] Simulate the pressure distribution at different locations in the microchannel: the pressure value of each point in the microchannel obtained by simulation;

[0181] Simulated drug molecule diffusion coefficient: the simulated diffusion coefficient of drug molecules in the medium;

[0182] Simulated pH value of the internal and external environment of the biochip: simulated pH value of the internal and external environment of the biochip;

[0183] Simulated ion concentration change value: the concentration change of ions in the simulated solution;

[0184] Simulated drug diffusion area: the simulated drug diffusion area in the medium;

[0185] Simulated drug effect value: the expected effect of the drug on the experimental subject according to the simulation conditions;

[0186] Simulated changes in biohormones in the experimental environment: If the model includes this function, the simulated changes in biohormones.

[0187] In the drug release system based on MEMS microfluidic anesthetic cartridges, the module is responsible for verifying and comparing actual drug release data with simulated predicted data to improve the accuracy of drug release. The following is a detailed description of the specific functions of the drug release detection module:

[0188] The drug release detection module receives drug release task information, parses and extracts parameters, and then uses a pre-trained simulation model to simulate the drug release process. The simulation results are compared with the real-time collected drug release data, thereby realizing real-time monitoring and evaluation of the drug release process.

[0189] The drug release detection module first receives the drug release task information from the data acquisition module, which includes the drug name, drug type, expected drug standard concentration, expected drug release rate, expected fluid flow rate, expected temperature difference between the internal and external environments of the biochip, expected pH value, etc.

[0190] The module parses the information and extracts drug release task parameters, such as drug type, concentration requirements, release rate, and environmental conditions.

[0191] The drug release detection module loads a pre-trained anesthetic drug release simulation model. This model, trained using machine learning techniques such as the random forest algorithm and based on historical data from the drug release system, can simulate various changes during the drug release process. The simulation model parameters, including the learning rate, number of iterations, and random seed, are initialized to ensure optimal performance.

[0192] The parsed drug release task information is substituted into the simulation model as input parameters, and the boundary conditions and initial conditions of the model are set, such as the geometry of the microchannel and the physical properties of the fluid.

[0193] The drug release simulation model for the anesthetic cartridge is activated to begin simulating the drug release process. The model calculates and outputs various simulation results for the drug release process based on input parameters and internal algorithms.

[0194] The drug release simulation model of the anesthetic bullet can simulate various parameters in the drug release process, including drug simulation concentration, fluid flow rate in the simulated biochip microchannel, temperature difference between the internal and external environments of the simulated biochip, drug release simulation rate, pressure distribution at different positions in the simulated microchannel, diffusion coefficient of simulated drug molecules, pH value of the simulated environment inside and outside the biochip, change value of simulated ion concentration, simulated drug diffusion area, simulated drug effect value and simulated change value of simulated biological hormones in the experimental environment.

[0195] The operation of the drug release detection module is crucial for ensuring the accuracy and safety of drug delivery systems based on MEMS microfluidic anesthetic cartridges. By simulating the drug release process and comparing it with real-time data, the system can promptly detect and correct potential deviations or anomalies, thereby optimizing drug release strategies, improving therapeutic efficacy, and mitigating potential risks. Furthermore, this module provides strong support for drug development and optimization, helping to advance the advancement and development of medical technology.

[0196] Specifically, the flow regulation system of the anesthetic bullet of the present invention and the drug release detection module are further used to:

[0197] Aligning the drug release simulation results and real-time drug release data of the anesthetic bullet based on time, thereby obtaining aligned drug release simulation results and real-time drug release data of the anesthetic bullet;

[0198] The drug release simulation results of the aligned anesthetic bullet and each indicator in the real-time drug release data are used to calculate the error between the simulation results and the real-time data;

[0199] The error between the simulation result and the real-time data includes absolute error and relative error. The absolute error is the difference between the simulation value and the real-time value, and the relative error is the difference between the simulation value and the real-time value divided by the real-time value.

[0200] Comparing the calculated error with a preset error range, where the preset error range is a pre-received error range;

[0201] If the errors of all indicators are within the preset error range, the comparison result is judged to be within the preset error range;

[0202] If the error of any one or more indicators exceeds the preset error range, the comparison result is determined to be outside the preset error range;

[0203] If the comparison result is within the preset error range, the simulation result is consistent with the real-time data, and the real-time drug release data is transmitted to the test result verification unit;

[0204] If the comparison result is not within the preset error range, there is a difference between the simulation result and the real-time data.

[0205] In a drug release system based on MEMS microfluidic anesthetic cartridges, the drug release simulation results of the anesthetic cartridges are compared with the real-time drug release data. The following is a detailed explanation of this function:

[0206] Time alignment: The drug release detection module first aligns the drug release simulation results of the anesthetic cartridge with the real-time drug release data based on time. This is because the simulation results and real-time data may be collected at different time points. To ensure accurate comparison, they need to be compared at the same or similar time points.

[0207] Error calculation: After alignment, the module calculates the error for each indicator in the drug release simulation results and real-time drug release data from the aligned anesthetic cartridges. Indicators include drug concentration, fluid flow rate, temperature difference, drug release rate, pressure distribution, drug molecular diffusion coefficient, pH value, ion concentration change, drug diffusion area, drug effect value, and simulated biohormone changes in the experimental environment. Error calculation includes absolute error and relative error. Absolute error is the difference between the simulated value and the real-time value, while relative error is the absolute error divided by the real-time value to get the percentage.

[0208] Error range comparison: Compare the calculated error with a preset error range. This preset error range is pre-set by the system to determine whether the difference between the simulation result and the real-time data is within an acceptable range.

[0209] If the errors of all indicators are within the preset error range, then the comparison result is judged to be within the preset error range, which means that the simulation result is basically consistent with the real-time data.

[0210] If the error of any one or more indicators exceeds the preset error range, the comparison result is determined to be outside the preset error range, indicating that there is a difference between the simulation result and the real-time data.

[0211] Result processing: If the comparison result is within the preset error range, the drug release detection module transmits the real-time drug release data to the detection result verification unit for further verification.

[0212] If the comparison result falls outside the preset error range, the module generates a drug release path detection task and sends it to the anesthetic cartridge's drug release terminal. The terminal executes the detection task and returns the drug release path detection results. The drug release detection module analyzes these results to determine whether there are any blockages or other problems in the drug release path and generates appropriate warning information.

[0213] Specifically, the flow regulation system of the anesthetic bullet of the present invention and the drug release detection module are further used to:

[0214] Comparing the drug release simulation results of the anesthetic bullet with the real-time drug release data, an error index comparison result is obtained, and the error index comparison result includes an index of error exceeding a preset range;

[0215] Based on the error index comparison results, matching is performed in the preset generated drug release path knowledge base, and the index with errors exceeding the preset range is used as the matching basis to obtain the drug release path detection task;

[0216] The drug release path detection task includes the specific detection path and the expected detection indicators;

[0217] Encapsulate the information of the drug release path detection task into a standard task instruction, which includes the detection path, detection indicators, and detection time. Send the encapsulated task instruction to the drug release terminal of the anesthetic bomb, monitor the execution of the task instruction by the drug release terminal, and receive the drug release path detection results from the drug release terminal. The detection results should include the flow rate, pressure, and temperature along the detection path.

[0218] The received test results are matched with a preset drug path test result knowledge base, and based on the matching results, it is determined whether the drug release path is blocked.

[0219] In a drug-release system based on a MEMS microfluidic anesthetic cartridge, the drug-release detection module compares the cartridge's simulated drug-release results with real-time drug-release data and then further processes the comparison results to identify and respond to potential release path issues. The following is a description of the drug-release detection module's specific functions in this process:

[0220] The drug release detection module first compares the simulated drug release results from the anesthetic cartridge with the real-time drug release data to generate error index comparison results. The results clearly indicate which indicators (such as drug concentration, fluid flow rate, temperature difference, etc.) have errors outside the preset range.

[0221] Based on the error metric comparison results, the module matches the results against a pre-set knowledge base for generating drug release pathways. This knowledge base contains information linking various possible error metrics with corresponding drug release pathway detection tasks. Based on these matching results, the module generates specific drug release pathway detection tasks based on the metrics that fall outside the pre-set error range. These tasks are designed to further diagnose potential issues through additional testing.

[0222] The generated drug release path detection task includes the specific detection path and expected test indicators. To ensure the task is correctly executed by the drug release terminal of the anesthetic cartridge, the module encapsulates the task information into standard task instructions. Standard task instructions include information such as the detection path, test indicators, and detection time to ensure the accuracy and executable nature of the task. The encapsulated task instructions are then sent to the drug release terminal of the anesthetic cartridge. The drug release detection module monitors the terminal's execution of the task instructions to ensure that the task is carried out as expected.

[0223] The drug release path detection results are received from the drug release terminal of the anesthetic cartridge. These results should include data such as flow rate, pressure, and temperature along the detection path. The drug release detection module analyzes the received detection results and matches them against a pre-set knowledge base of drug path detection results. This knowledge base contains a correspondence between the detection results and the status of the drug release path (e.g., whether there is blockage).

[0224] Based on the matching results, the module can determine whether there is a blockage in the drug release path. If there is a blockage, the module will generate a drug release path blockage warning message to remind the user or system administrator to take appropriate measures to resolve the problem.

[0225] Through the above steps, the drug release detection module can effectively identify and respond to possible path blockage problems during the release of anesthetic bullets, thereby ensuring the safety of the drug release system.

[0226] The technical solution of the present invention effectively solves the problem that the existing anesthetic bullet drug release system cannot detect the blockage of the drug release path in time by constructing a drug release system based on MEMS microfluidic anesthetic bullet. The specific solution is as follows:

[0227] The data acquisition module first obtains drug release task information and matches it with a pre-set anesthetic cartridge knowledge base to obtain standard drug information (such as standard drug concentration, standard drug flow rate, and standard drug release rate). The model construction module uses historical data from the anesthetic cartridge drug release system to train a random forest model to construct an anesthetic cartridge drug release simulation model. This model can simulate various parameters during the drug release process.

[0228] The data acquisition module transmits the drug release task information to the anesthetic cartridge drug release terminal and collects various data during the drug release process in real time (such as real-time drug concentration, fluid flow rate, temperature difference, pressure distribution, pH value, ion concentration change, etc.). The drug release detection module substitutes the drug release task information into the anesthetic cartridge drug release simulation model, outputs the simulation results, and compares the simulation results with the real-time data collected.

[0229] If the comparison result is within the preset error range, the drug release process is normal, and the real-time data will be transmitted to the test result verification unit for further verification. If the comparison result is outside the preset error range, it indicates that there may be an abnormality in the drug release process. In this case, a drug release path detection task will be generated and sent to the anesthetic cartridge drug release terminal for further testing.

[0230] The drug release path test results are matched against a pre-set knowledge base of drug release path test results to determine if the drug release path is blocked. If the test results indicate blockage, the system generates a blockage warning message so that timely action can be taken. The test result verification unit compares the real-time drug release data with the drug standard information of the anesthetic cartridge. If the real-time data exceeds the standard information, a drug release excess warning message is generated.

[0231] Through the above technical solution, the present invention can monitor various parameters in the anesthetic bullet drug release process in real time, and by comparing the simulation results with the real-time data, timely discover the blockage problem of the drug release path, thereby improving the safety of the anesthetic bullet drug release system.

Claims

1. The drug release system based on MEMS microfluidic anesthetic bullet is characterized by: include: The data acquisition module is used to obtain drug release task information, match the drug release task information with the preset drug knowledge base of the anesthetic cartridge, and obtain the drug standard information of the anesthetic cartridge. The drug standard information of the anesthetic cartridge includes the drug standard concentration, drug standard flow rate, and drug standard release rate; A model building module is used to obtain historical data of the drug release system of the anesthetic bullet, train a random forest model using the historical data of the drug release system of the anesthetic bullet, and obtain a drug release simulation model of the anesthetic bullet; a data acquisition module, configured to transmit drug release task information to a drug release terminal of the anesthetic cartridge, and to collect real-time drug release data when the drug release terminal of the anesthetic cartridge performs the drug release task, wherein the real-time drug release data includes real-time drug concentration, fluid flow rate in a real-time biochip microchannel, temperature difference between the real-time internal and external environments of the real-time biochip, real-time drug release rate, pressure distribution at different positions in the real-time microchannel, real-time drug molecular diffusion coefficient, pH value of the real-time internal and external environments of the real-time biochip, real-time ion concentration change value, real-time drug diffusion area, real-time drug effect value, and real-time change value of simulated biological hormones in the experimental environment; The drug release detection module is used to substitute the drug release task information into the drug release simulation model of the anesthetic bullet and output the drug release simulation results of the anesthetic bullet. The drug release simulation results of the anesthetic bullet include the drug simulation concentration, the fluid flow rate in the simulated biochip microchannel, the temperature difference between the internal and external environments of the simulated biochip, the drug release simulation rate, the pressure distribution at different positions in the simulated microchannel, the simulated drug molecular diffusion coefficient, the pH value of the simulated internal and external environments of the biochip, the simulated ion concentration change value, the simulated drug diffusion area, the simulated drug effect value and the simulated biological hormone simulation change value in the experimental environment. The drug release simulation results of the anesthetic bullet are compared with the real-time drug release data to obtain the drug release simulation results of the anesthetic bullet. The simulation result is compared with the real-time drug release data, and the comparison result includes whether the comparison result is within a preset error range and whether the comparison result is not within the preset error range. If the comparison result is within the preset error range, the real-time drug release data is transmitted to the detection result verification unit. If the comparison result is not within the preset error range, a drug release path detection task is generated, and the drug release path detection task is sent to the drug release terminal of the anesthetic bullet to obtain a drug release path detection result. The drug release path detection result is matched with a preset drug path detection result knowledge base to obtain a drug release path detection analysis result. The drug release path detection analysis result includes whether the drug release path is blocked and whether the drug release path is not blocked. When the drug release path is blocked, a drug release path blockage warning information is generated; The test result verification unit is used to compare the real-time drug release data with the drug standard information of the anesthetic bullet. If the real-time drug release data exceeds the drug standard information of the anesthetic bullet in the comparison result, a drug release exceeding standard warning information is generated.

2. The drug release system based on MEMS microfluidic anesthetic bullet according to claim 1, characterized in that: The data acquisition module is further used to: The drug release task information is parsed to obtain the parsed drug release task information, which includes: drug name, drug type, expected drug standard concentration, expected drug release rate, expected fluid flow rate, expected temperature difference between the internal and external environments of the biochip, expected pH value of the internal and external environments of the biochip, range of changes in simulated biological hormones in the experimental environment, expected pressure distribution at different positions in the microchannel, expected range of drug molecular diffusion coefficient, expected range of changes in ion concentration, expected drug diffusion area, and expected drug effect value.

3. The drug release system based on MEMS microfluidic anesthetic bullet according to claim 1, characterized in that: The model building module is further used to: Feature extraction is performed on the historical data of the drug release system of the anesthetic bullet to obtain the drug release process characteristics. The drug release process characteristics include drug concentration characteristics, flow rate characteristics, temperature difference characteristics, pressure distribution characteristics, pH value characteristics and ion concentration characteristics. The drug release process characteristics are used as the input of the model. Initialize the random forest model parameters, including learning rate, number of iterations, and random seed; The drug release process characteristics were input into the selected random forest model, and the model parameters were adjusted to minimize the prediction error using the gradient descent algorithm to obtain the drug release simulation model of the anesthetic bullet.

4. The drug release system based on MEMS microfluidic anesthetic bullet according to claim 1, characterized in that: The data acquisition module is further used for: The drug release task information is sent to the drug release terminal of the anesthetic bomb, and the concentration changes of the drug during the release process are monitored in real time by chemical sensors. The real-time flow rate of the fluid in the microchannel is measured using a flow meter or flow sensor. The real-time temperature of the environment inside and outside the biochip is measured using a temperature sensor, and the temperature difference is calculated. By monitoring the working status of the drug release device, the drug release rate is calculated in real time. The pressure sensor is used to measure the pressure at different positions in the microchannel to obtain a real-time pressure distribution diagram. By observing the diffusion behavior of drug molecules in the medium, the diffusion coefficient is calculated in real time using a diffusion model. The pH sensor is used to monitor the pH value of the environment inside and outside the biochip in real time. The concentration changes of ions in the solution are monitored in real time using an ion selective electrode or an ion chromatograph. The diffusion area of ​​the drug in the medium is measured in real time through image processing technology or direct observation. The real-time effect of the drug on the experimental subject is monitored through biosensors or physiological indicators.

5. The drug release system based on MEMS microfluidic anesthetic bullet according to claim 1, characterized in that: The drug release detection module is further used for: Receive drug release task information from the data acquisition module, parse the drug release task information, extract drug release task parameters, and obtain drug type, concentration requirement, release rate, and environmental conditions; Load the pre-trained anesthetic drug release simulation model, initialize the model parameters, substitute the parsed drug release task information as input parameters into the simulation model, and receive and set the model's boundary conditions and initial conditions; Start the drug release simulation model of the anesthetic bullet and begin to simulate the drug release process; The drug release simulation model of the anesthetic bullet calculates and outputs the simulation results of the drug release process based on the input parameters and internal algorithms; Simulation results of drug release process, including: Drug simulation concentration: simulated drug concentration changes during the release process; Simulating the flow rate of the fluid in the microchannel of the biochip: the flow rate distribution of the fluid in the microchannel obtained by simulation; Simulate the temperature difference between the inside and outside of the biochip: simulate the temperature difference between the inside and outside of the biochip; Drug release simulation rate: the simulated drug release rate changes over time; Simulate the pressure distribution at different locations in the microchannel: the pressure value of each point in the microchannel obtained by simulation; Simulated drug molecule diffusion coefficient: the simulated diffusion coefficient of drug molecules in the medium; Simulated pH value of the internal and external environment of the biochip: simulated pH value of the internal and external environment of the biochip; Simulated ion concentration change value: the concentration change of ions in the simulated solution; Simulated drug diffusion area: the simulated drug diffusion area in the medium; Simulated drug effect value: the expected effect of the drug on the experimental subject according to the simulation conditions; Simulated changes in biohormones in the experimental environment: If the model includes this function, the simulated changes in biohormones.

6. The drug release system based on MEMS microfluidic anesthetic bullet according to claim 5, characterized in that: The drug release detection module is further used for: Aligning the drug release simulation results and real-time drug release data of the anesthetic bullet based on time, thereby obtaining aligned drug release simulation results and real-time drug release data of the anesthetic bullet; The drug release simulation results of the aligned anesthetic bullet and each indicator in the real-time drug release data are used to calculate the error between the simulation results and the real-time data; The error between the simulation result and the real-time data includes absolute error and relative error. The absolute error is the difference between the simulation value and the real-time value, and the relative error is the difference between the simulation value and the real-time value divided by the real-time value. Comparing the calculated error with a preset error range, where the preset error range is a pre-received error range; If the errors of all indicators are within the preset error range, the comparison result is judged to be within the preset error range; If the error of any one or more indicators exceeds the preset error range, the comparison result is determined to be outside the preset error range; If the comparison result is within the preset error range, the simulation result is consistent with the real-time data, and the real-time drug release data is transmitted to the test result verification unit; If the comparison result is not within the preset error range, there is a difference between the simulation result and the real-time data.

7. The drug release system based on MEMS microfluidic anesthetic bullet according to claim 6, characterized in that: The drug release detection module is further used for: Comparing the drug release simulation results of the anesthetic bullet with the real-time drug release data, an error index comparison result is obtained, and the error index comparison result includes an index of error exceeding a preset range; Based on the error index comparison results, matching is performed in the preset generated drug release path knowledge base, and the index with errors exceeding the preset range is used as the matching basis to obtain the drug release path detection task; The drug release path detection task includes the specific detection path and the expected detection indicators; Encapsulate the information of the drug release path detection task into a standard task instruction, which includes the detection path, detection indicators, and detection time. Send the encapsulated task instruction to the drug release terminal of the anesthetic bomb, monitor the execution of the task instruction by the drug release terminal, and receive the drug release path detection results from the drug release terminal. The detection results should include the flow rate, pressure, and temperature along the detection path. The received test results are matched with a preset drug path test result knowledge base, and based on the matching results, it is determined whether the drug release path is blocked.

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