Gastrointestinal decompression system and decompression control method

Abstract

The invention relates to the technical field of gastrointestinal decompression, and discloses a gastrointestinal decompression system and a decompression control method. Setting sampling time, interval and sample number, measuring the pressure in the stomach at multiple points when a valve is closed, calculating the average gas generation rate of each section, constructing a continuous rate function through linear interpolation, and deriving; the valve opening degree is used for calibrating steady-state flow, and a known constant-flow calibration pressure-flow coupling constant is applied; target pressure is obtained, and performance indexes are constructed by combining the pressure deviation, the flow ratio, the gas rate and the coupling coefficient; constructing an augmented Lagrange quantity based on the index, introducing an adjoint quantity, and exporting a coupled second-order ordinary differential equation; a homogeneous solution and a convolution special solution are obtained, an integral constant is solved through initial and final boundaries, and a closed control law is generated; the pressure and the rate are collected in real time, parameters and constants are updated online, control output is calculated, a valve is driven, and closed-loop automatic precise pressure reduction is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of gastrointestinal decompression, and in particular to a gastrointestinal decompression system and a decompression control method. Background Art

[0002] Gastrointestinal decompression, a commonly used adjunctive treatment in clinical practice, aims to reduce intragastric pressure by aspirating gas or fluid from the gastrointestinal cavity, thereby alleviating symptoms such as abdominal distension, nausea, and poor postoperative intestinal function recovery. With the advancement of technologies such as intensive care and enteral nutrition, gastrointestinal decompression equipment has evolved from simple manual suction devices to automated devices with pressure monitoring and flow control capabilities.

[0003] Currently, most automated decompression devices in clinical practice only offer fixed negative pressure or constant flow suction modes. For example, some devices maintain a preset constant pressure level through mechanical springs or pneumatic valves, without dynamic adjustment of valve opening. Others operate in a constant flow mode, controlling decompression through simple timed or quantitative valve opening and closing. However, these methods fail to respond to the patient's actual intragastric pressure fluctuations or gas generation rate in real time. This can lead to intermittent periods of excessive negative pressure during initial suction and ineffective decompression later on, making it difficult to meet individualized needs. Gastrointestinal gas generation is influenced by multiple factors, including diet, intestinal motility, and microbial fermentation, and its generation rate is not constant. Existing technologies often ignore this operational context, failing to continuously measure and predict the gas generation rate, let alone incorporate this dynamic term into the control law. As a result, the pressure recovery caused by the continuous generation of endogenous gas during decompression cannot be accurately compensated, resulting in insufficient decompression during prolonged low-pressure periods and excessive decompression during short high-pressure periods. The pressure-flow coupling relationship is the core of decompression control, reflecting the physical coupling between valve opening, flow rate, and intraluminal pressure. Existing systems often use fixed parameters determined at the factory or through manual experimentation, lacking the ability to dynamically calibrate coupling constants based on actual pressure-flow curves during use. Once changes occur in patient position, gastric content viscosity, or tubing interfaces, the previously calibrated parameters no longer accurately describe the system dynamics, resulting in decreased control accuracy. A few intelligent decompression devices on the market incorporate algorithms such as PID, fuzzy control, or gradient search. However, these methods are typically based on empirical models, and parameter tuning relies on manual trial and error, lacking closed-form analytical control laws. PID and fuzzy control are prone to excessive oscillation when faced with large, sudden changes in intragastric pressure. Furthermore, gradient optimization, due to its high real-time computational complexity, is difficult to implement at high frequencies in clinical settings, hindering rapid, continuous, and automated adjustment. While current devices offer basic pressure feedback or quantitative flow capabilities, the overall decompression process still requires manual intervention by medical personnel based on monitoring curves. Fixed negative pressure or constant flow control can easily lead to excessive suction to ensure rapid pressure recovery, increasing flow consumption. On the one hand, it accelerates the wear of equipment pipelines, and on the other hand, it increases the risk of damage to the gastric mucosa in high negative pressure areas. Overly conservative adjustment will prolong the decompression time and affect treatment efficiency.

[0004] To this end, this case aims to propose a gastrointestinal decompression system and decompression control method. First, a continuous rate model reflecting the dynamics of gas generation is constructed through multi-point real-time pressure measurement; then, system parameter calibration and dimensionless performance index construction are completed online; then, an augmented Lagrangian optimization framework is introduced to derive the optimal necessary conditions and establish a second-order dynamic model including inertia and damping characteristics; then, the general solution of the model is obtained through analytical methods, and the parameters are accurately solved in combination with clinical or experimental boundary conditions; finally, the analytical results are converted into a closed-form control law, and the control strategy is dynamically updated in combination with real-time data to drive the decompression valve to automatically adjust the pressure and flow, thereby achieving the shortest decompression time and the smoothest pressure recovery while ensuring safety constraints. Summary of the Invention

[0005] The present invention provides a gastrointestinal decompression system and a decompression control method, which help solve the problems mentioned in the above background technology.

[0006] The present invention provides the following technical solution: a gastrointestinal decompression control method, comprising: The sampling start time, sampling interval, and number of sampling points were set. With the pressure reducing valve closed, the instantaneous intragastric pressure was measured at each sampling time. The average gas generation rate of each sampling segment was calculated. The gas generation rate function in continuous time was constructed by linear interpolation, and its derivative was used to obtain the instantaneous rate of change of the gas generation rate. The pressure reducing valve is adjusted to a predetermined opening to measure the steady-state flow rate, and after applying a known constant flow rate and maintaining it for a sampling interval, the pressure before and after calibration is recorded, and the approximate pressure change rate is calculated. A kinetic model is constructed based on the pressure-flow data to obtain the pressure-flow coupling constant; Obtain and set the target pressure, calculate the current pressure deviation and flow ratio, and construct performance indicators based on the gas generation rate and the preset coupling coefficient; The time derivatives of the system variables are calculated based on the pressure deviation and flow ratio, the augmented Lagrangian is constructed and the adjoint function is introduced. The adjoint equation and the optimal necessary conditions are derived by setting the stationary point conditions for the control variables. The relationship between the control variable and the state variable is obtained according to the adjoint equation, and the coupled second-order ordinary differential equation describing the dynamic evolution of gas flow and pressure deviation is further constructed; Construct a homogeneous solution based on the second-order ordinary differential equation, and construct a special solution through the convolution integral method, and add the homogeneous solution and the special solution to obtain the total solution of the equation; The dimensionless pressure deviation at the initial moment and the dimensionless pressure deviation at the target moment are set as boundary conditions, and the integral constant is solved according to the boundary conditions; A closed-form control law is constructed based on the obtained total solution of the equation and the boundary conditions. By real-time acquisition of intragastric pressure and gas generation rate, online updating of system parameters and integral constants, calculation of control output and driving of the pressure reducing valve, a continuous and automatic closed-loop pressure reducing control process is formed until the pressure reducing is completed or the control cycle is restarted.

[0007] Optionally, the setting of the sampling start time, sampling interval, and number of sampling points, measuring the instantaneous intragastric pressure at each sampling time with the pressure reducing valve closed, calculating the average gas generation rate of each sampling segment, constructing a gas generation rate function in continuous time by linear interpolation, and derivatizing the function to obtain the instantaneous rate of change of the gas generation rate, specifically includes: Assume that the sampling start time is , the sampling interval is , the number of sample points is ; Calculate the Sampling time , ; Close the pressure reducing valve and Measured instantaneous pressure: ;in, For the The instantaneous pressure is obtained by sampling; For any time Get the intragastric pressure function; Calculate the Time The average gas generation rate ; At any time , use linear interpolation to construct the gas generation rate function under continuous time , specifically: ; Interpolation function Find the time derivative and get the constant form , ;in, is the instantaneous rate of change of gas generation rate over time.

[0008] Optionally, the step of adjusting the pressure reducing valve to a predetermined opening to measure the steady-state flow rate, applying a known constant flow rate and maintaining the flow rate for a sampling interval to record the pressures before and after calibration, calculating the approximate pressure change rate, and constructing a kinetic model based on the pressure-flow data to obtain the pressure-flow coupling constant may specifically include: Adjust the pressure reducing valve to The steady-state flow rate is recorded as ; At the moment Apply a known constant flow maintain After minutes, the pressure before and after calibration was measured. , ; Calculate the approximate pressure change rate ; Building a dynamic model ; The solution is ;in, is the pressure-flow coupling constant.

[0009] Optionally, obtaining and setting the target pressure, calculating the current pressure deviation and flow ratio, and constructing a performance index based on the gas generation rate and a preset coupling coefficient may specifically include: Get the target pressure, recorded as ; Calculating pressure deviation ; Calculate flow ratio ;in, For the moment The actual pressure reduction flow rate; Calculating gas generation rate ; Setting the coupling coefficient , the initial moment Pressure deviation ; Building performance metrics ;in, To optimize duration.

[0010] Optionally, the derivative of the system variable with respect to time is calculated based on the pressure deviation and the flow ratio, an augmented Lagrangian is constructed and an adjoint function is introduced, and the adjoint equation and the optimal necessary condition are derived by setting a stationary point condition for the control variable, specifically including: calculate Derivative with respect to time ; Constructing the augmented Lagrangian ;in, is the adjoint function; Set up The stationary condition ; Constructing the adjoint equation .

[0011] Optionally, the relationship between the control variable and the state variable is obtained according to the adjoint equation, and a coupled second-order ordinary differential equation describing the dynamic evolution of the gas flow and pressure deviation is further constructed, specifically including: From the accompanying relationship : ; Build The second derivative of ,Right now ;in, is the derivative of the gas velocity.

[0012] Optionally, constructing a homogeneous solution based on the second-order ordinary differential equation, constructing a special solution by a convolution integral method, and adding the homogeneous solution and the special solution to obtain the total solution of the equation specifically includes: Constructing a homogeneous solution ;in, and is the integration constant; Constructing a special solution ;in, is the convolution integral time variable; Get the total solution .

[0013] Optionally, setting the dimensionless pressure deviation at the initial moment and the dimensionless pressure deviation at the target moment as boundary conditions and solving the integral constant according to the boundary conditions specifically includes: Assume initial conditions ;in, is the dimensionless pressure deviation at the initial moment; Assume the target condition is: ; The solution is ; Among them, select When .

[0014] Optionally, the closed-form control law is constructed based on the obtained total solution of the equation and the boundary conditions, and a continuous and automatic closed-loop decompression control process is formed by real-time acquisition of intragastric pressure and gas generation rate, online updating of system parameters and integral constants, calculation of control output and driving of the decompression valve until decompression is completed or the control cycle is restarted, specifically including: make ,but: ; By dynamic constraints ,have to ; Calculating Pressure Reducing Valve Flow ; Calculate actual pressure ; Perform the following steps to implement it: S101, real-time acquisition: continuous pressure measurement , build and , and then normalized to and ; S102, parameter calculation: calibration and , and then find the integration constant ; S103, online control: calculate at each moment 、 and ; S104, valve drive: Act on the pressure reducing valve until End or restart the cycle.

[0015] A decompression system for implementing the gastrointestinal decompression control method, comprising: Pressure sensor: used to measure gastric pressure in real time; Gas flow acquisition module: used to calculate the gas generation rate based on pressure data; Controller: used to perform augmented Lagrangian construction, optimal necessary conditions derivation, second-order ordinary differential equation solution and closed-form control law generation; Pressure reducing valve and its driving device: the controller outputs a signal to adjust the gas flow rate according to the generated control law; Display and storage module: used to display decompression process parameters in real time and record key data.

[0016] The present invention has the following beneficial effects: 1. When the valve is closed, the intragastric pressure data is collected through multi-point time series, and the continuous change model of the gas generation rate is constructed in combination with the interpolation derivation method, achieving a high-fidelity mapping of the pressure change and the dynamic characteristics of the gas generation rate. The spatiotemporal resolution of the gas generation process is improved, providing reliable and precise dynamic input for subsequent optimization, and laying the foundation for real-time control by obtaining the instantaneous rate of change. Traditional methods often rely on single-point empirical measurements or static assumptions, which make it difficult to accurately characterize the dynamic process of gas generation, resulting in slow and inaccurate decompression control. This solution significantly improves measurement accuracy and response speed through high-frequency multi-point data and continuous models.

[0017] 2. By obtaining the steady-state flow value at a predetermined opening and recording the pressure changes before and after applying a known constant flow condition, the steady-state and dynamic condition calibrations are combined to achieve high-precision online identification of the pressure-flow coupling coefficient. The model parameters can be automatically updated according to the real-time measurement results, eliminating the uncertainty caused by external interference and individual physiological differences, and ensuring the accuracy and robustness of the subsequent control strategy. Traditional calibration mostly relies on offline experiments or empirical coefficients, which have problems such as severe parameter drift, long calibration cycle and inability to adapt online. This solution shortens the calibration cycle to seconds, achieving rapid response to changes in the body state. Compared with methods that only use empirical values or one-time offline calibration, this solution achieves real-time online adaptive calibration while ensuring high accuracy, improving parameter identification efficiency and control accuracy.

[0018] 3. Multidimensional physical quantities such as pressure deviation and valve flow ratio are converted into dimensionless variables through standardized methods, and a comprehensive performance evaluation function is constructed together with the gas generation rate and coupling coefficient to achieve a unified quantitative evaluation of decompression efficiency and stability. This evaluation function provides a clear goal for the optimization algorithm, reduces the subjectivity of manual weight setting, improves the comparability and versatility of the optimization results, and helps to achieve consistent control performance in different patients and application scenarios. Existing evaluations are often limited to a single physical quantity and lack a comprehensive measure of the overall efficiency and safety of the decompression process, resulting in unclear optimization goals and easy to fall into local optimality. This method breaks through the limitations of traditional absolute value or single indicator evaluation, constructs a unified dimensionless indicator system, and provides a more reasonable, universal and adaptive objective function for subsequent variational optimization.

[0019] 4. The augmented Lagrangian optimization concept is adopted to unify performance indicators and dynamic constraints in a variational framework, and the optimal necessary conditions are systematically derived through adjoint functions and stationary point conditions, providing a theoretical basis for the design of closed-form control laws. This method can achieve the simultaneous optimization of decompression time and pressure recovery smoothness under the premise of meeting physiological safety constraints, significantly enhancing control convergence and system stability. Traditional control can only balance efficiency and safety under empirical rules or simple quadratic optimization frameworks, and it is difficult to take both into account; and the coupling effect of constraints is often ignored. Compared with fixed cost functions or simple quadratic adjustment strategies, this scheme achieves a more rigorous and reliable global optimal control strategy design by introducing the coupling effect of adjoint functions and dynamic constraints.

[0020] 5. Combining the adjoint equation with the system state relationship, a second-order dynamic model containing the pressure deviation and its first-order and second-order derivatives is derived to accurately characterize the inertia and damping characteristics during the decompression process. This second-order model has higher simulation accuracy and interpretability, can accurately predict the pressure recovery curve, and provide reliable dynamic constraints for the rapid convergence of the control law. Existing models mostly adopt first-order or simplified empirical expressions, which are difficult to reflect the inertia effect of the system, resulting in delayed or over-adjusted output response. Compared with traditional first-order or semi-empirical models, this scheme introduces higher-order dynamic characteristics to achieve an explicit description of the system inertia effect, thereby improving the control system's response capability and accuracy to sudden changes.

[0021] 6. Combining the homogeneous solution with the special solution corresponding to the driving term, the analytical expression of the complete dynamic response of the system is obtained through analytical superposition, organically integrating the analytical solution with the convolution principle. This closed-form analytical expression not only improves the efficiency of online calculations, but also makes the control law highly interpretable and directly deployable, avoiding the computational delays caused by traditional numerical iterative solutions. Traditional numerical simulation or empirical fitting methods have large computational complexity and poor real-time performance, making it difficult to meet the needs of clinical applications for rapid response. This solution achieves rapid online calculation of dynamic responses through analytical solutions, improves the real-time performance and maintainability of the system, and facilitates flexible deployment under different clinical conditions.

[0022] 7. The actual clinical or experimental starting pressure deviation and target recovery pressure are directly applied as boundary conditions to the analytical solution to achieve accurate adaptive calculation of the integral constant. This solution can dynamically adjust boundary conditions and parameters according to the patient's individual physiological state, support personalized treatment strategies, and enhance the adaptability and reliability of the model. Existing algorithms often use fixed or empirical parameters, and the parameter determination lacks objective basis, resulting in control accuracy that cannot meet clinical needs. This method breaks the limitations of empirical settings by adaptively calculating the integral constant based on actual boundary conditions, significantly improving the accuracy and consistency of the model in different working conditions and different individuals.

[0023] 8. Directly derive the closed-loop control expression from the mathematical analytical solution, and build a full-process automated closed-loop implementation plan through real-time pressure and gas generation rate monitoring, online parameter update and control output calculation. The real-time adjustment of the entire decompression process can be completed without human intervention, reducing manual adjustment errors, ensuring high precision, high reliability and high safety; at the same time, it has good anti-interference ability. Existing clinical decompression mostly relies on manual switches or simple feedback mechanisms, which are prone to overshoot or oscillation, making it difficult to balance safety and efficiency. Unlike traditional experience-based adjustment or simple open-loop control, this solution provides a closed-loop control strategy with strong adaptability, high real-time performance and good stability, bringing a revolutionary automation solution to the field of gastrointestinal decompression. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] Example, see Figure 1 , a gastrointestinal decompression control method, comprising: The sampling start time, sampling interval, and number of sampling points were set. With the pressure reducing valve closed, the instantaneous intragastric pressure was measured at each sampling time. The average gas generation rate of each sampling segment was calculated. The gas generation rate function in continuous time was constructed by linear interpolation, and its derivative was used to obtain the instantaneous rate of change of the gas generation rate. The pressure reducing valve is adjusted to a predetermined opening to measure the steady-state flow rate, and after applying a known constant flow rate and maintaining it for a sampling interval, the pressure before and after calibration is recorded, and the approximate pressure change rate is calculated. A kinetic model is constructed based on the pressure-flow data to obtain the pressure-flow coupling constant; Obtain and set the target pressure, calculate the current pressure deviation and flow ratio, and construct performance indicators based on the gas generation rate and the preset coupling coefficient; The time derivatives of the system variables are calculated based on the pressure deviation and flow ratio, the augmented Lagrangian is constructed and the adjoint function is introduced. The adjoint equation and the optimal necessary conditions are derived by setting the stationary point conditions for the control variables. The relationship between the control variable and the state variable is obtained according to the adjoint equation, and the coupled second-order ordinary differential equation describing the dynamic evolution of gas flow and pressure deviation is further constructed; Construct a homogeneous solution based on the second-order ordinary differential equation, and construct a special solution through the convolution integral method, and add the homogeneous solution and the special solution to obtain the total solution of the equation; The dimensionless pressure deviation at the initial moment and the dimensionless pressure deviation at the target moment are set as boundary conditions, and the integral constant is solved according to the boundary conditions; A closed-form control law is constructed based on the obtained total solution of the equation and the boundary conditions. By real-time acquisition of intragastric pressure and gas generation rate, online updating of system parameters and integral constants, calculation of control output and driving of the pressure reducing valve, a continuous and automatic closed-loop pressure reducing control process is formed until the pressure reducing is completed or the control cycle is restarted.

[0027] Precise management of gastrointestinal decompression is achieved through a multi-stage closed-loop automatic control process. By measuring intragastric pressure at multiple points in real time and constructing a continuous gas generation rate model, online identifying system dynamic parameters, constructing dimensionless performance evaluation indicators, introducing an augmented Lagrangian variational optimization framework, deriving a coupled dynamics model, analytically solving boundary value problems, and generating closed-form control laws, this method addresses the problems of low control accuracy, slow response speed, and insufficient safety caused by traditional decompression methods that rely on empirical curves and static parameters. It has the beneficial effects of efficient automation, precise stability, personalized adaptation, and online real-time updates. Specifically, this method avoids the pressure overshoot and oscillation phenomena common in traditional control through closed-loop real-time monitoring and dynamic updates, effectively reducing the risk of mechanical stress damage to gastrointestinal tissue. At the same time, the analytical solution avoids the large amount of numerical iteration calculations and significantly improves the real-time response performance of the system. It can also automatically adjust the control strategy according to the physiological differences of patients, improving the reliability and applicability of clinical applications, and providing a safe, efficient, and intelligent overall solution for gastrointestinal decompression.

[0028] The setting of the sampling start time, sampling interval, and number of sampling points, measuring the instantaneous intragastric pressure at each sampling time with the pressure reducing valve closed, calculating the average gas generation rate of each sampling segment, constructing a gas generation rate function in continuous time by linear interpolation, and obtaining the instantaneous rate of change of the gas generation rate by taking its derivative, specifically includes: Assume that the sampling start time is , the sampling interval is , the number of sample points is ; Calculate the Sampling time , ; Determine the time grid and resolution of pressure and velocity data to provide a basis for subsequent data acquisition and interpolation; Close the pressure reducing valve and Measured instantaneous pressure: ;in, For the The instantaneous pressure is obtained by sampling; For any time Obtain the intragastric pressure function; obtain the original pressure sequence as the input of the speed calculation and optimization model; Calculate the Time The average gas generation rate ; Quantify the contribution of gas generation to pressure changes in each sampling interval; At any time , use linear interpolation to construct the gas generation rate function under continuous time , specifically: ; Smooth the discrete rate data into a continuous function to facilitate subsequent differential and integral operations; Interpolation function Find the time derivative and get the constant form , ;in, It is the instantaneous rate of change of gas generation rate with time; it serves as a forced driving term in the second-order dynamic equation and provides input for the special deconvolution.

[0029] In the initial stage, the foundation of the entire control system was laid through refined data collection and model construction. By setting the multi-point sampling time, interval and number of samples in the valve closed state, the instantaneous pressure in the stomach is continuously measured and the average gas generation rate in each time period is calculated. The interpolation method is then used to smoothly generate a continuous rate function and the instantaneous rate of change is obtained by differentiation. This solves the problem that traditional single-point or discrete measurements are difficult to fully reflect the dynamics of gas generation, and has the beneficial effects of high-resolution dynamic modeling and anti-interference capabilities. This step eliminates the model error caused by low measurement frequency or noise interference, and provides reliable and continuous input for subsequent online optimization and precise control; at the same time, without the need for external calibration or static assumptions, the changing trend of the gas generation rate can be captured in real time, thereby ensuring the stability and accuracy of the entire decompression process.

[0030] The method comprises adjusting the pressure reducing valve to a predetermined opening to measure the steady-state flow rate, applying a known constant flow rate and maintaining a sampling interval to record the pressure before and after calibration, calculating the approximate pressure change rate, constructing a kinetic model based on the pressure-flow data, and obtaining the pressure-flow coupling constant, specifically comprising: Adjust the pressure reducing valve to The steady-state flow rate is recorded as ; used to normalize the actual traffic to ; At the moment Apply a known constant flow maintain After minutes, the pressure before and after calibration was measured. , ; Calculate the approximate pressure change rate ; Building a dynamic model ; The solution is ;in, is the pressure-flow coupling constant, which characterizes the intensity of the effect of flow change on pressure; Determine the linear coupling relationship between pressure and flow and eliminate model uncertainties.

[0031] In the system parameter calibration link, an organic combination of steady-state and dynamic calibration is achieved. Through steady-state flow measurement under valve opening and pressure recording under known constant flow conditions, a dynamic model is constructed and the pressure-flow coupling coefficient is identified. Through this step, the problem that traditional calibration relies heavily on offline experiments or empirical coefficients, resulting in inaccurate parameters and difficulty in adapting to physiological differences, is solved. It has the beneficial effects of online adaptive parameter updates and high-precision identification. Specifically, this calibration method can instantly reflect the dynamic characteristics of the system under different working conditions, effectively eliminate the influence of environmental interference and individual differences on model accuracy; and can quickly complete the calibration process in clinical applications, shortening the system deployment time and improving the robustness and reliability of the pressure reduction control strategy.

[0032] The obtaining and setting of the target pressure, calculating the current pressure deviation and flow ratio, and constructing the performance index by combining the gas generation rate and the preset coupling coefficient specifically include: Get the target pressure, recorded as ; Calculating pressure deviation ; Calculate flow ratio ;in, For the moment The actual pressure reduction flow rate; Calculating gas generation rate ; Eliminate dimensional differences in physical quantities and make performance indicators additive; Setting the coupling coefficient , the initial moment Pressure deviation ; Normalize the dynamic equations to dimensionless form; Building performance metrics ;in, To optimize the duration; comprehensively evaluate the pressure recovery accuracy and flow consumption, and provide the objective function for variational optimization.

[0033] By defining dimensionless variables and constructing comprehensive performance indicators, a quantitative evaluation of the decompression process is achieved. By standardizing the pressure deviation and flow ratio, and combining the gas generation rate and coupling coefficient to construct a unified objective function, the problem of traditional single indicator evaluation or weight setting being too subjective is solved, and it has the beneficial effects of objective quantification, multi-dimensional synthesis and strong versatility. This step not only provides clear performance criteria for subsequent optimization, but also enables the control effects of different patients or different application scenarios to be compared under the same dimension, thereby improving the generalizability and consistency of the algorithm; at the same time, by eliminating the difference in physical quantity units, the subjectivity of manual parameter adjustment is reduced, and the stability and reliability of the optimization results are enhanced.

[0034] The time derivative of the system variable is calculated based on the pressure deviation and the flow ratio, the augmented Lagrangian is constructed and the adjoint function is introduced. The adjoint equation and the optimal necessary conditions are derived by setting the stationary point conditions for the control variables, specifically including: calculate Derivative with respect to time ;Describe the instantaneous evolution of pressure deviation with flow rate and gas generation rate; Constructing the augmented Lagrangian ;in, It is the adjoint function; it combines the performance index with the dynamic constraint to provide a unified function for variational solution; Set up The stationary condition ; Establish the adjoint quantity and control quantity The algebraic relationship between Constructing the adjoint equation ; Describe the evolution of the adjoint over time and provide differential conditions for solving the optimal control law.

[0035] The performance indicators and dynamic constraints are integrated into the augmented Lagrangian. By introducing the adjoint function and stationary point conditions, the necessary conditions for optimality are systematically derived. This step solves the problem that traditional control strategies are difficult to take into account both efficiency and safety constraints at the same time and are prone to falling into local optimality. It has the beneficial effects of global optimality, fast convergence speed and strict satisfaction of constraints. Specifically, the augmented Lagrangian framework incorporates dynamic constraints into the objective function, so that performance optimization is automatically carried out under the premise of meeting the clinical safety boundary; the introduction of the adjoint equation provides a deep characterization of the coupling relationship between the control variables and the state variables, realizes the refined management of the control process, and significantly improves the stability and reliability of the system.

[0036] The relationship between the control variable and the state variable is obtained according to the adjoint equation, and a coupled second-order ordinary differential equation is further constructed to describe the dynamic evolution of the gas flow and pressure deviation, specifically including: From the accompanying relationship : ;Convert the derivative information of the adjoint quantity into the derivative relationship of the flow ratio; Build The second derivative of ,Right now ;in, is the derivative of the gas velocity; the standard second-order ordinary differential equation for the dimensionless pressure deviation is obtained, which is convenient for solving analytical solutions.

[0037] Combining the relationship between the adjoint conditions and the system state, a second-order dynamic model including inertia and damping characteristics is derived to describe the dynamic evolution of pressure deviation and gas flow. This step solves the problem that the existing first-order or empirical models cannot accurately reflect the inertia effect and dynamic response characteristics of the system, with the beneficial effect of higher simulation accuracy and interpretability. Specifically, the second-order model can capture the hysteresis behavior and oscillation trend during the decompression process, providing precise physical constraints for subsequent analytical solutions and control law design; at the same time, the model has a stronger predictive ability for sudden changes, thereby ensuring the stability and safety of the control system under complex working conditions.

[0038] The method of constructing a homogeneous solution based on the second-order ordinary differential equation, constructing a special solution by the convolution integral method, and adding the homogeneous solution and the special solution to obtain the total solution of the equation specifically includes: Constructing a homogeneous solution ;in, and is the integral constant, reflecting the intrinsic oscillation behavior of the system without external drive; Constructing a special solution ;in, is the convolution integral time variable; it represents the forced response of the system to the change of gas generation rate; Get the total solution ; Gives the complete time domain response of pressure deviation.

[0039] By using analytical methods to construct the homogeneous solution and special solution of the system respectively, and superimposing the two to obtain the overall solution, the time-domain response of the pressure deviation is fully described. This step solves the problems of large computational complexity and poor online real-time performance of numerical iterative solutions, and has the beneficial effects of efficient online computing and good physical interpretability. Specifically, the analytical solution can not only quickly provide the response trajectory during the implementation of the pressure reduction system, reducing the pressure on hardware performance, but also provide operators or automatic control modules with clear dynamic change patterns, further improving the reliability and maintainability of the system.

[0040] The dimensionless pressure deviation at the initial moment and the dimensionless pressure deviation at the target moment are set as boundary conditions, and the integral constant is solved according to the boundary conditions, specifically including: Assume initial conditions ;in, is the dimensionless pressure deviation at the initial moment; the initial pressure deviation is mapped to an integral constant ; Assume the target condition is: ; The solution is ; Among them, select When ; Determine the integration constant using the end constraint , completing the parameterization of the analytical solution.

[0041] The initial pressure deviation and target pressure recovery deviation set clinically or experimentally are used as boundary conditions to solve the integral constant in the analytical solution and realize the parameterization of the dynamic response of the system. Through this step, the problem of the traditional method of rough treatment of initial and final state conditions or reliance on empirical values is solved, which has the beneficial effects of adaptive boundary value calculation and personalized control strategy generation. Specifically, this boundary condition processing can flexibly adjust the integral constant according to the physiological state of different patients, so that the entire control trajectory strictly meets the safety and performance constraints, ensuring that the decompression process is both fast and smooth, and greatly improving the accuracy and reliability of clinical applications.

[0042] The closed-form control law is constructed based on the obtained total solution of the equation and the boundary conditions, and a continuous and automatic closed-loop decompression control process is formed by real-time acquisition of intragastric pressure and gas generation rate, online updating of system parameters and integral constants, calculation of control output, and driving of the decompression valve until decompression is completed or the control cycle is restarted, specifically including: make ,but: ; Calculate the instantaneous rate of change of the current pressure deviation to provide a basis for flow calculation; By dynamic constraints ,have to ;Determine the required flow ratio directly based on the pressure change rate and gas generation rate; Calculating Pressure Reducing Valve Flow ; Calculate actual pressure ; Mapping dimensionless results back to actual flow rates and pressures; Perform the following steps to implement it: S101, real-time acquisition: continuous pressure measurement , build and , and then normalized to and ; Ensure that the input of the optimization model is updated in real time; S102, parameter calculation: calibration and , and then find the integration constant ; Complete model calibration and initial and final state parameterization; S103, online control: calculate at each moment 、 and ;Generate control instructions in real time to drive the pressure reducing valve; S104, valve drive: Act on the pressure reducing valve until End or restart the cycle; closed-loop implementation ensures smooth return of pressure to target.

[0043] Based on the analytical total solution and boundary conditions, a closed-loop control law is generated, and through real-time pressure and gas generation rate monitoring, online parameter updates, and closed-loop valve actuation, fully automated decompression is achieved. This step solves the problems of traditional decompression, which rely on manual intervention, respond in a timely manner, and are prone to overshoot oscillation. It has the beneficial effects of fully automatic closed-loop control, high stability, excellent efficiency, and strong safety. This closed-loop control law can quickly converge under different operating conditions without offline training or extensive calculations. At the same time, automated implementation avoids human operational errors and provides visual, traceable, and intelligent management for the clinical decompression process.

[0044] This embodiment also provides a gastrointestinal decompression system, comprising: Pressure sensor: used to measure gastric pressure in real time; Gas flow acquisition module: used to calculate the gas generation rate based on pressure data; Controller: used to perform augmented Lagrangian construction, optimal necessary conditions derivation, second-order ordinary differential equation solution and closed-form control law generation; Pressure reducing valve and its driving device: the controller outputs a signal to adjust the gas flow rate according to the generated control law; Display and storage module: used to display decompression process parameters in real time and record key data.

[0045] Through modular system integration, hardware and software coordination of the entire gastrointestinal decompression process is achieved, including units such as pressure sensing, flow acquisition, controller calculation, valve drive, and display storage. This step solves the problems of the existing system's fragmented functional modules, difficult data synchronization, and complex clinical application deployment, with the beneficial effects of high scalability, easy maintenance, and clinical friendliness. Specifically, a unified interface is used between system modules to achieve real-time synchronization of data and rapid response to instructions; the display and storage functions can visually track and historically playback the decompression process, facilitating medical staff's evaluation and adjustment; the overall solution is easy to integrate into existing nursing processes, improving clinical operational efficiency and patient safety.

[0046] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0047] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A gastrointestinal decompression control method, characterized in that: include: The sampling start time, sampling interval, and number of sampling points were set. With the pressure reducing valve closed, the instantaneous intragastric pressure was measured at each sampling time. The average gas generation rate of each sampling segment was calculated. The gas generation rate function in continuous time was constructed by linear interpolation, and its derivative was used to obtain the instantaneous rate of change of the gas generation rate. The pressure reducing valve is adjusted to a predetermined opening to measure the steady-state flow rate, and after applying a known constant flow rate and maintaining it for a sampling interval, the pressure before and after calibration is recorded, and the approximate pressure change rate is calculated. A kinetic model is constructed based on the pressure-flow data to obtain the pressure-flow coupling constant; Obtain and set the target pressure, calculate the current pressure deviation and flow ratio, and construct performance indicators based on the gas generation rate and the preset coupling coefficient; The time derivatives of the system variables are calculated based on the pressure deviation and flow ratio, the augmented Lagrangian is constructed and the adjoint function is introduced. The adjoint equation and the optimal necessary conditions are derived by setting the stationary point conditions for the control variables. The relationship between the control variable and the state variable is obtained according to the adjoint equation, and the coupled second-order ordinary differential equation describing the dynamic evolution of gas flow and pressure deviation is further constructed; Construct a homogeneous solution based on the second-order ordinary differential equation, and construct a special solution through the convolution integral method, and add the homogeneous solution and the special solution to obtain the total solution of the equation; The dimensionless pressure deviation at the initial moment and the dimensionless pressure deviation at the target moment are set as boundary conditions, and the integral constant is solved according to the boundary conditions; A closed-form control law is constructed based on the obtained total solution of the equation and the boundary conditions. By real-time acquisition of intragastric pressure and gas generation rate, online updating of system parameters and integral constants, calculation of control output and driving of the pressure reducing valve, a continuous and automatic closed-loop pressure reducing control process is formed until the pressure reducing is completed or the control cycle is restarted.

2. A gastrointestinal decompression control method according to claim 1, characterized in that: The setting of the sampling start time, sampling interval, and number of sampling points, measuring the instantaneous intragastric pressure at each sampling time with the pressure reducing valve closed, calculating the average gas generation rate of each sampling segment, constructing a gas generation rate function in continuous time by linear interpolation, and obtaining the instantaneous rate of change of the gas generation rate by taking its derivative, specifically includes: Assume that the sampling start time is , the sampling interval is , the number of sample points is ; Calculate the Sampling time , ; Close the pressure reducing valve and Measured instantaneous pressure: ;in, For the The instantaneous pressure is obtained by sampling; For any time Get the intragastric pressure function; Calculate the Time The average gas generation rate ; At any time , use linear interpolation to construct the gas generation rate function under continuous time , specifically: ; Interpolation function Find the time derivative and get the constant form , ;in, is the instantaneous rate of change of gas generation rate over time.

3. A gastrointestinal decompression control method according to claim 2, characterized in that: The method comprises adjusting the pressure reducing valve to a predetermined opening to measure the steady-state flow rate, applying a known constant flow rate and maintaining a sampling interval to record the pressure before and after calibration, calculating the approximate pressure change rate, constructing a kinetic model based on the pressure-flow data, and obtaining the pressure-flow coupling constant, specifically comprising: Adjust the pressure reducing valve to The steady-state flow rate is recorded as ; At the moment Apply a known constant flow maintain After minutes, the pressure before and after calibration was measured. , ; Calculate the approximate pressure change rate ; Building a dynamic model ; The solution is ;in, is the pressure-flow coupling constant.

4. A gastrointestinal decompression control method according to claim 3, characterized in that: The obtaining and setting of the target pressure, calculating the current pressure deviation and flow ratio, and constructing the performance index by combining the gas generation rate and the preset coupling coefficient specifically include: Get the target pressure, recorded as ; Calculating pressure deviation ; Calculate flow ratio ;in, For the moment The actual pressure reduction flow rate; Calculating gas generation rate ; Setting the coupling coefficient , the initial moment Pressure deviation ; Building performance metrics ;in, To optimize duration.

5. A gastrointestinal decompression control method according to claim 4, characterized in that: The time derivative of the system variable is calculated based on the pressure deviation and the flow ratio, the augmented Lagrangian is constructed and the adjoint function is introduced. The adjoint equation and the optimal necessary conditions are derived by setting the stationary point conditions for the control variables, specifically including: calculate Derivative with respect to time ; Constructing the augmented Lagrangian ;in, is the adjoint function; Set up The stationary condition ; Constructing the adjoint equation .

6. A gastrointestinal decompression control method according to claim 5, characterized in that: The relationship between the control variable and the state variable is obtained according to the adjoint equation, and a coupled second-order ordinary differential equation is further constructed to describe the dynamic evolution of the gas flow and pressure deviation, specifically including: By accompanying relationship : ; Build The second derivative of ,Right now ;in, is the derivative of the gas velocity.

7. A gastrointestinal decompression control method according to claim 6, characterized in that: The method of constructing a homogeneous solution based on the second-order ordinary differential equation, constructing a special solution by the convolution integral method, and adding the homogeneous solution and the special solution to obtain the total solution of the equation specifically includes: Constructing a homogeneous solution ;in, and is the integration constant; Constructing a special solution ;in, is the convolution integral time variable; Get the total solution .

8. A gastrointestinal decompression control method according to claim 7, characterized in that: The dimensionless pressure deviation at the initial moment and the dimensionless pressure deviation at the target moment are set as boundary conditions, and the integral constant is solved according to the boundary conditions, specifically including: Assume initial conditions ;in, is the dimensionless pressure deviation at the initial moment; Assume the target condition is: ； The solution is ; Among them, select When .

9. A gastrointestinal decompression control method according to claim 8, characterized in that: The closed-form control law is constructed based on the obtained total solution of the equation and the boundary conditions, and a continuous and automatic closed-loop decompression control process is formed by real-time acquisition of intragastric pressure and gas generation rate, online updating of system parameters and integral constants, calculation of control output, and driving of the decompression valve until decompression is completed or the control cycle is restarted, specifically including: make ,but: ； By dynamic constraints ,have to ; Calculating Pressure Reducing Valve Flow ; Calculate actual pressure ; Perform the following steps to implement it: S101, real-time acquisition: continuous pressure measurement , build and , and then normalized to and ; S102, parameter calculation: calibration and , and then find the integration constant ; S103, online control: calculate at each moment 、 and ; S104, valve drive: Act on the pressure reducing valve until End or restart the cycle.

10. A decompression system using the gastrointestinal decompression control method according to claim 9, characterized in that: include: Pressure sensor: used to measure gastric pressure in real time; Gas flow acquisition module: used to calculate the gas generation rate based on pressure data; Controller: used to perform augmented Lagrangian construction, optimal necessary conditions derivation, second-order ordinary differential equation solution and closed-form control law generation; Pressure reducing valve and its driving device: the controller outputs a signal to adjust the gas flow rate according to the generated control law; Display and storage module: used to display decompression process parameters in real time and record key data.

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