A method for negative pressure control optimization for an electric suction device

Abstract

The present application relates to the technical field of negative pressure regulation, and discloses a negative pressure control optimization method for an electric suction device. The method comprises the following steps: constructing a suction negative pressure system model, covering motor driving, pump body flow, cavity pressure change and terminal negative pressure output; obtaining a set negative pressure and a real-time negative pressure output value and calculating an error signal, inputting the error signal into a PID controller to generate a control signal; the control signal drives the motor to adjust the pump body suction flow, the real-time output negative pressure value is estimated in combination with the cavity change and the channel impedance, and a closed-loop control path is formed. In order to improve the control precision, an improved spring optimization algorithm is further introduced to adaptively set the PID parameters, so that the response speed and the steady-state precision of the controller under different loads and environmental changes are improved, and the method has the advantages of high control precision, strong output stability and high intelligent regulation degree.

Description

Technical Field

[0001] This invention belongs to the field of negative pressure control optimization, and in particular relates to a negative pressure control optimization method for electric suction devices. Background Technology

[0002] Existing electric aspirators are widely used in the medical field to remove excess fluid or secretions from the body, playing a vital role in surgery, emergency care, and nursing procedures. However, the accuracy of negative pressure regulation in electric aspirators directly affects the effectiveness of the suction process and patient safety. Current negative pressure control mostly employs traditional PID controllers, which, through simple proportional, integral, and derivative adjustments, are insufficient to adapt to complex practical needs. Furthermore, the negative pressure regulation process of electric aspirators involves complex factors such as nonlinearity, delay, and dynamic interference. Traditional PID controllers struggle to ensure accurate and rapid tracking of the target negative pressure, easily leading to negative pressure overshoot or instability, thus affecting suction efficiency and safety. In addition, the individualized needs of different patients and the physical limitations of the equipment itself further increase the complexity of negative pressure regulation. Therefore, optimizing the parameters of the negative pressure PID controller to enhance its adaptability and robustness has become an important research direction for improving the performance of electric aspirators.

[0003] PID control is a classic control method that achieves precise control of a system through three adjustment modes: proportional, integral, and derivative. Proportional control responds quickly to system demands based on the current deviation, but may introduce steady-state errors. Integral control corrects the output by accumulating historical deviations, eliminating steady-state errors, but may cause overshoot. Derivative control uses the rate of change of deviation to predict trends and adjusts in advance to reduce oscillations and overshoot. Combining these three methods enables fast response, high precision, and stable control. Although the PID algorithm is simple and robust, and widely used in industrial control, its performance is limited when dealing with complex nonlinear or multivariable systems. Therefore, by combining intelligent optimization algorithms to dynamically adjust PID parameters, its adaptability and control effect on complex systems can be further improved, meeting the diverse needs of modern industry.

[0004] The Spring Search Algorithm (SSA) is an optimization algorithm based on the physical behavior of springs. This algorithm models the search agent as a mass block connected to a spring, using the spring force described by Hooke's Law to guide the search process. The advantages of the Spring Search Algorithm are that it intuitively describes the optimization problem using physical mechanisms, achieving a natural convergence path through the mechanical properties of springs, and possessing high adaptability and physical interpretability. However, this algorithm also has drawbacks, such as being prone to getting trapped in local optima and potentially experiencing a decrease in convergence speed due to parameter sensitivity. Summary of the Invention

[0005] The purpose of this invention is to provide an optimized negative pressure control method for electric suction devices. This method dynamically adjusts the parameters of the negative pressure PID controller using an improved spring search algorithm, achieving high-precision, fast-response, and stable control of the negative pressure. Addressing the problems of poor adaptability, significant overshoot, and large steady-state error in traditional PID control within complex nonlinear systems, this invention constructs a real-time negative pressure demand model based on the environmental conditions of the electric suction device. By optimizing the controller parameters, it improves the robustness and dynamic response performance of the system. Simultaneously, by introducing an improved spring search algorithm, the negative pressure PID controller can adaptively adjust under different usage scenarios, improving the accuracy and stability of negative pressure regulation, avoiding overshoot and hysteresis, and ensuring the efficiency and safety of the suction process.

[0006] To achieve the above objectives, the present invention employs a negative pressure control optimization method for electric suction devices, the specific steps of which are as follows:

[0007] S1. Construct a suction negative pressure system model, the system model including a motor drive module, a pump body drive flow output module, dynamic change of cavity pressure, and a terminal negative pressure output module;

[0008] S2. Obtain the current system negative pressure setpoint and the real-time terminal negative pressure output negative pressure value, calculate the difference between the two to form an error signal, input the error signal to the PID controller of the suction negative pressure system, and output the control signal u(t);

[0009] S3. The control signal acts on the motor drive module, driving the pump body to form a suction flow and control the pressure change inside the suction cavity. Combined with the impedance change of the suction channel, the final output negative pressure is dynamically estimated to obtain the final negative pressure value.

[0010] S4. Feedback the final negative pressure value to the PID controller of the suction negative pressure system to form a closed-loop control path and optimize the negative pressure control of the electric suction device.

[0011] According to the method of the present invention, the suction negative pressure system model includes a complete response model from the control signal u(t) to the final negative pressure. Each link model is modeled according to the physical characteristics of the equipment, forming a precise control chain and combined with a real-time feedback compensation mechanism to achieve fast negative pressure output response speed, stability under blockage / impedance fluctuation conditions, and output value close to the set target value.

[0012] According to the method of the present invention, the core task of the electric suction device is to output the target negative pressure quickly and stably according to the set negative pressure value, and to maintain the control accuracy unchanged under impedance disturbance. Its working process essentially involves a multi-link coupled system from electrical signal control to mechanical drive and then to gas dynamics change. Among them, the PID controller of the suction negative pressure system outputs an electrical signal, and the control signal output is converted into mechanical speed. In the electric suction device of the present invention, the motor is the actuator, and its dynamic response will affect the response speed of the entire system. The present invention establishes a first-order response relationship model between the control signal u(t) and the speed ω(t). The motor drive module converts the control signal into the motor speed through the relationship model. The first-order response relationship model helps to predict the drive response delay, thereby optimizing the controller feedforward and hysteresis compensation design.

[0013] According to the method of the present invention, the pump body drive flow output module establishes a proportional relationship between mechanical rotation speed ω(t) and flow rate Q(t), mapping the rotation speed to gas suction speed, thereby laying the foundation for constructing the subsequent pressure model. In this process, the suction pump converts the rotation speed into suction flow rate. Due to the internal structure of the electric suction device, the gas pressure in the cavity is affected by the flow input, structural volume, leakage, and external gas supply factors. Its change process has lag and buffering characteristics. To avoid the influence of these factors, the present invention introduces the cavity volume and leakage impedance to construct a pressure dynamic differential equation. The main purpose is to accurately describe the inertial characteristics of the suction negative pressure system in order to adjust the stability of the suction negative pressure system and reduce overshoot. Because the final negative pressure output point is not equal to the cavity pressure in the actual process, and there is also suction path impedance, especially when there is blockage or adsorption of tissue, the impedance changes drastically. The present invention proposes a terminal negative pressure output module to estimate the actual terminal negative pressure value, thereby achieving precise control of the actual action area.

[0014] According to the method of the present invention, in the negative pressure control optimization method of the present invention, the target negative pressure value is not a fixed preset value, but is dynamically corrected based on the system setting benchmark value and combined with the current environmental state, so as to improve the adaptability and stability of the control system of the present invention under different operating conditions, and characterize the output setting level on which the current pressure regulation strategy of the system is based. When the ambient temperature rises, the gas density decreases and the flow resistance decreases, which may lead to an increase in suction efficiency. Considering that this may lead to an increase in suction efficiency, the method can automatically increase the target negative pressure value to avoid excessive suction. When the ambient temperature decreases, the fluid viscosity increases and the flow resistance increases, which may lead to insufficient negative pressure. Considering that this may lead to insufficient negative pressure, the method can automatically decrease the target negative pressure value to improve suction stability. The real-time negative pressure demand model is as follows:

[0015] ;

[0016] In the formula, It is the target negative pressure value at the current moment. To set the negative pressure reference value, This is the temperature regulation coefficient. The current ambient temperature. This is a reference value for ambient temperature. For time.

[0017] According to the method of the present invention, the negative pressure setpoint of the system at the current moment and the negative pressure output value of the real-time terminal are calculated, and their error value is input into the PID controller of the suction negative pressure system. The PID controller of the suction negative pressure system calculates the output control signal u(t) by introducing proportional parameters, integral parameters and derivative parameters, wherein the calculation method adopts the positional PID algorithm. Because the electric suction system is a control object with large inertia and slow change of disturbance, the design of the present invention adopts the motor-pump-cavity-negative pressure output mode, which has significant dynamic delay. The state change of the suction negative pressure system is slowly affected by impedance and cavity volume factors.

[0018] According to the method of this invention, the PID controller of the suction negative pressure system uses an intelligent optimization algorithm to tune and optimize the proportional, integral, and derivative parameters of its positional PID algorithm, enabling the suction negative pressure system to adapt to errors caused by random factors during the control process. Specifically, this invention optimizes the tuning of the proportional, integral, and derivative parameters of the positional PID algorithm through an independently designed improved spring search algorithm. In the mechanical equilibrium stage of the existing spring search algorithm, it proposes to introduce the distance between the initial equilibrium position and the historical optimal position, and normalize it with the distance between the global optimal position and the historical optimal position. Based on the normalized distance ratio and step size adjustment factor, the difference between the historical optimal position and the initial equilibrium position is used as a guiding term to improve the position update mathematical model. Secondly, it proposes a local development strategy based on the quantum decoherent spring multibody field configuration perturbation mechanism. By constructing a local quantum spring subsystem, and combining coupled state evolution behavior and configuration transition operations, the dynamic update of individual positions is achieved.

[0019] According to the method of the present invention, the agent individual position of the improved spring search algorithm of the present invention is a three-dimensional spatial vector. Each dimension of the vector corresponds to the proportional parameter, integral parameter and differential parameter of the positional PID algorithm. The three-dimensional value of the agent individual position is updated by the improved spring search algorithm, thereby updating the proportional parameter, integral parameter and differential parameter values. In this process, the fitness function, i.e. the objective function, guides the agent individual position to evolve in the direction of reducing error based on the error between the current negative pressure setpoint of the system and the real-time terminal negative pressure output value.

[0020] According to the method of the present invention, the improved position update mathematical model in the mechanical equilibrium stage is as follows:

[0021] ;

[0022] in, ;

[0023] In the formula, A random number between 0 and 1. This is the step size adjustment factor. This is the best historical position. To be the globally optimal position - This represents the distance between the initial equilibrium position and the historical best position. This represents the maximum distance between the initial equilibrium position and the historical best position.

[0024] According to the method of the present invention, the original position update mathematical model of the basic spring search algorithm in the mechanical equilibrium stage combines the initial equilibrium position with the displacement. This position update method fails to fully consider the guiding role of historical information and the global optimal solution, resulting in insufficient search efficiency, especially in complex search spaces where it may get stuck in local optima or have a slow convergence speed. To address this deficiency, the improved model introduces an additional term into the original position update mathematical model. This improvement first dynamically adjusts the guiding weight of historical optimal solutions by normalizing the distance, making the guiding effect stronger when the distance is far from the historical optimal solution; while the guiding strength gradually weakens when the distance is close to the historical optimal solution, thus avoiding over-reliance on historical information and promoting local exploitation. This dynamic weight adjustment mechanism enables the algorithm to achieve a good balance between global exploration and local exploitation. The step size adjustment factor 'a' further optimizes this process, and its value depends on... The inverse relationship between the initial position and the historical optimal solution ensures that the step size dynamically changes according to the distance between the initial position and the historical optimal solution. When the object is far from the historical optimal solution, the step size is larger, which helps to speed up the search; when the object gradually approaches the historical optimal solution, the step size decreases, which helps to refine the optimization. Through this design, the algorithm avoids the limitations of premature convergence or inability to effectively search for the global optimum that may be caused by a single fixed step size. Secondly, through... The term describes the displacement direction between the initial equilibrium position and the historical optimal solution. This term guides the search to move closer to the historical optimal solution, and improves the global search capability by utilizing known historical information. In summary, the improved position update mathematical model not only enhances the algorithm's global search capability, ensuring that it can jump out of the local optimum faster in the initial stage, but also achieves precise optimization in the later stage by dynamically adjusting the step size and guiding weight.

[0025] According to the method of the present invention, the mechanical equilibrium stage is based on a global search strategy driven by a spring system. The present invention proposes a local development strategy based on the quantum decoherent spring multibody field configuration perturbation mechanism, which solves the problem of the lack of local development in the original spring search algorithm and improves the performance of positional PID optimization accuracy for negative pressure systems. The principle of the local development strategy based on the quantum decoherent spring multibody field configuration perturbation mechanism is as follows: In a quantum elastic system, the spring can be regarded as a collective oscillator of the coupling potential well between microscopic particles. The behavior of these oscillators is dominated by coherent states and decoherent processes. When multiple particles are in non-locally coupled states, small perturbations will trigger configuration transitions due to phase fluctuations, ultimately forming a structural transformation of the system.

[0026] According to the method of the present invention, the search accuracy and perturbation response capability of local neighborhoods in the solution space are improved based on the quantum decoherent spring multibody field configuration perturbation mechanism. This mechanism achieves dynamic updating of individual positions by constructing a local quantum spring subsystem and combining coupled state evolution behavior and configuration transition operation. Specifically, a local spring field model is constructed around the current candidate solution. The spring field model consists of multiple neighboring nodes, and local coupled state representation units are generated by defining a quantum correlation structure. A local perturbation factor is introduced to simulate the phase evolution process in the spring subsystem, and the interference phase parameters in the local coupled state are dynamically adjusted based on the perturbation driving method. According to the state information distribution in the perturbation evolution process, it is identified whether the current local spring field is in the decoherent transition critical region. If the transition condition is met, a multi-point position adjustment operation based on local configuration perturbation is performed. When the local transition condition is not triggered, the position of the current solution is updated using a correction method based on the nonlinear elastic response function, and the correction method keeps the local spring field structure unchanged. By cyclically executing the interference perturbation detection and position correction operation, the iterative advancement of the local development process of the target region in the search space is realized.

[0027] According to the method of the present invention, the specific method for optimizing the parameters of the negative pressure PID controller using the improved spring search algorithm is as follows:

[0028] S301. Randomly initialize N candidate solutions, denoted as surrogate individuals. Each candidate solution is a three-dimensional vector representing the parameters Kp, Ki, and Kd of the PID controller of the negative pressure system.

[0029] S302. Set the parameters of the improved spring search algorithm, including population size N, problem dimension D, maximum number of iterations T, upper bound of search space ub, lower bound of search space lb, and initialize the search space of the population agent individuals.

[0030] S303. During the mechanical equilibrium stage, an improved global search strategy is introduced to update the agent individual's position, and the agent individual's position update is further corrected by introducing simulated sliding perturbation.

[0031] S304. A local development strategy based on the quantum decoherent spring multibody field configuration perturbation mechanism, which achieves dynamic updating of individual positions by constructing a local quantum spring subsystem and combining coupled state evolution behavior with configuration transition operation;

[0032] S305. Calculate the fitness value of each agent individual and record the position of the individual corresponding to the current minimum fitness value;

[0033] S306. When the number of iterations reaches the predetermined maximum value, the optimization process terminates, outputs the position of the agent individual corresponding to the minimum fitness value, and obtains the parameters Kp, Ki, and Kd of the PID controller of the suction negative pressure system by inverse mapping; otherwise, it returns to execute S303.

[0034] According to the method of the present invention, the fitness function is used to quantitatively evaluate the search effect of the agent individual and to construct a fitness function model to characterize the system performance. The fitness function is based on the deviation between the target negative pressure output value and the reference target value, and measures the degree of influence of the control parameters on the performance of the control system of the present invention, thereby providing an optimization basis for the improved spring-driven search mechanism of the present invention.

[0035] By adopting the above technical solution, the beneficial effects of this invention are as follows: First, by constructing a linkage system model covering motor drive, pump output, cavity pressure regulation, and terminal negative pressure feedback, a fine modeling of multiple physical processes of the electric aspirator is achieved, ensuring a high degree of consistency between the control logic and the equipment's operating state. Second, an intelligent optimization strategy based on a combination of a spring search mechanism and a quantum decoherence configuration disturbance mechanism is proposed to perform global-local collaborative tuning of PID control parameters, effectively improving control accuracy and system robustness. Among these, spring coupling and non-local interference modeling enable the algorithm to have stronger local exploration capabilities, avoiding early convergence; the phase disturbance and information entropy transition identification mechanism enhances the ability to identify and respond to unstable regions of the system, ensuring the controllability and effectiveness of disturbance behavior. In actual negative pressure control, the method of this invention can significantly improve the system's adaptability to load mutations and channel impedance disturbances, achieving rapid response and stable output, reducing the reliance on traditional manual tuning, and possessing good clinical practical value and engineering promotion prospects. Attached Figure Description

[0036] Figure 1 A flowchart outlining the steps for optimizing the negative pressure control of an electric suction device.

[0037] Figure 2 Flowchart of a negative pressure system model.

[0038] Figure 3 This is a curve comparing the fitness values ​​of the basic spring search algorithm and the improved spring search algorithm.

[0039] Figure 4 A flowchart illustrating the process of optimizing controller parameters for an existing algorithm.

[0040] Figure 5 This is a diagram illustrating the process of optimizing controller parameters using the algorithm of this invention.

[0041] Figure 6 A comparison chart showing the control effect of existing methods and the method of the present invention on optimizing the negative pressure of the electric suction device. Detailed Implementation

[0042] In this embodiment, a negative pressure control optimization method for an electric aspirator is provided. This method is based on the PID control parameter tuning mechanism driven by a spring system, relies on an improved spring search algorithm to construct an optimization framework, and integrates a multi-body configuration perturbation strategy of quantum decoherent spring field on this basis to achieve adaptive optimization tuning of the proportional, integral and derivative parameters of the PID controller. Combined with the dynamic characteristics of negative pressure regulation and multi-stage structural modeling, it aims to improve the control accuracy and response stability of the system in complex environments.

[0043] This invention provides a method for optimizing negative pressure control in an electric suction device. The following are specific implementation methods according to... Figure 1 implement;

[0044] S1. Construct a suction negative pressure system model, the system model including a motor drive module, a pump body drive flow output module, dynamic change of cavity pressure, and a terminal negative pressure output module;

[0045] S2. Obtain the current system negative pressure setpoint and the real-time terminal negative pressure output negative pressure value, calculate the difference between the two to form an error signal, input the error signal to the PID controller of the suction negative pressure system, and output the control signal u(t);

[0046] S3. The control signal acts on the motor drive module, driving the pump body to form a suction flow and control the pressure change inside the suction cavity. Combined with the impedance change of the suction channel, the final output negative pressure is dynamically estimated to obtain the final negative pressure value.

[0047] S4. Feedback the final negative pressure value to the PID controller of the suction negative pressure system to form a closed-loop control path and optimize the negative pressure control of the electric suction device.

[0048] Furthermore, r such Figure 2As shown, a motor drive module, a pump body drive flow output module, a cavity pressure dynamic change module, and a terminal negative pressure output module are designed to construct a suction negative pressure system model. First, the motor drive module is designed, using the control signal u(t) output by the PID controller of the suction negative pressure system as input to the motor. The motor response is the output speed ω(t). The mathematical model of the relationship adopts a first-order inertial response model, specifically:

[0049] ;

[0050] In the formula, The gain constant of the motor represents the ratio of the steady-state speed that the motor can achieve under a unit control signal. is the motor inertia time constant, used to describe the response delay of the motor from standstill to steady speed;

[0051] Convert the above time-domain mode to a complex frequency-domain mode: .

[0052] Furthermore, the negative pressure pump is driven to rotate by the motor output speed ω(t), which draws the gas fluid out of the cavity, forming a suction flow rate Q(t). In this invention, a pump flow output module is constructed to simulate a linear relationship between the flow rate and the rotation speed, and the modeling is implemented as follows: , The pump flow gain constant depends on the pump structure and the pump cavity volume at a unit rotational speed. Furthermore, a dynamic change module for cavity negative pressure is constructed. This invention considers the combined effects of the cavity internal pressure P(t), pump flow rate Q(t), cavity volume C, system leakage impedance, and atmospheric pressure, establishing a first-order differential equation to describe the change in cavity pressure over time. The modeling is as follows: ;

[0053] In the formula, The effective volume of the cavity is expressed in liters. The pressure is atmospheric pressure, and the standard value is taken as 101325 Pa. To attract the leakage resistance of the negative pressure system, a smaller value indicates that the system is easier to depressurize; therefore, a value of 5.0 × 10 is set. 6 Pa·s / m³.

[0054] Furthermore, considering the instantaneous impedance changes in the suction channel caused by sudden liquid adsorption or blockage, a time-dependent impedance function is defined that varies with the rate of pressure change. A channel impedance perturbation module is constructed, and the implementation model is as follows: In the formula, For the channel's basic impedance, Let be the channel impedance disturbance value at time t, in units of 300 Pa·s / m³. The sensitivity factor, in units of 0.02 s / Pa, reflects the degree of impedance response to the rate of pressure change. Finally, a terminal negative pressure output module is constructed. The actual output negative pressure at the final suction end is affected by the cavity pressure, suction flow rate, and channel impedance. The calculation relationship is as follows:

[0055] ;

[0056] In the formula, The actual negative pressure value at the terminal suction port is determined by a combined modeling of the motor drive module, pump drive flow output module, dynamic changes in cavity pressure, and terminal negative pressure output module, forming a control input signal... Negative pressure is output to the actual terminal. Complete dynamic output adjustment.

[0057] Furthermore, in this embodiment, to achieve real-time control of the negative pressure output accuracy, a set negative pressure reference value is established. The real-time negative pressure demand model, which is dynamically adjusted based on the system's baseline value and the current environmental conditions, is as follows:

[0058] ;

[0059] In the formula, It is the target negative pressure value at the current moment. To set the negative pressure reference value, This is the temperature regulation coefficient. The current ambient temperature. This is a reference value for ambient temperature. The time is used; and the actual negative pressure output value of the terminal at the current moment is obtained from the sensor. The error signal is calculated based on the difference between the two. The mathematical model is as follows: .

[0060] Furthermore, the aforementioned error signal e(t) is input to the PID controller of the suction negative pressure system. Using a positional PID control algorithm, the control signal u(t) is calculated based on the optimal proportional, integral, and derivative parameters obtained through optimization. This control signal u(t) is then input to the motor drive module to achieve closed-loop control. The calculation model for the control signal u(t) is as follows: .

[0061] Furthermore, in this embodiment, the optimal proportional, integral, and derivative parameters are obtained by optimizing the parameters of the negative pressure PID controller using an improved spring search algorithm. The specific implementation method is as follows:

[0062] S301. Randomly initialize N candidate solutions, denoted as surrogate individuals. Each candidate solution is a three-dimensional vector representing the parameters Kp, Ki, and Kd of the PID controller of the negative pressure system.

[0063] S302. Set the parameters of the improved spring search algorithm, including population size N, problem dimension D, maximum number of iterations T, upper bound of search space ub, lower bound of search space lb, and initialize the search space of the population agent individuals.

[0064] S303. During the mechanical equilibrium stage, an improved global search strategy is introduced to update the agent individual's position, and the agent individual's position update is further corrected by introducing simulated sliding perturbation.

[0065] S304. A local development strategy based on the quantum decoherent spring multibody field configuration perturbation mechanism, which achieves dynamic updating of individual positions by constructing a local quantum spring subsystem and combining coupled state evolution behavior with configuration transition operation;

[0066] S305. Calculate the fitness value of each agent individual and record the position of the individual corresponding to the current minimum fitness value;

[0067] S306. When the number of iterations reaches the predetermined maximum value, the optimization process terminates, outputs the position of the agent individual corresponding to the minimum fitness value, and obtains the parameters Kp, Ki, and Kd of the PID controller of the suction negative pressure system by inverse mapping; otherwise, it returns to execute S303.

[0068] Furthermore, each candidate solution represents the parameters Kp, Ki, and Kd of the negative pressure PID controller. As the algorithm iterates, the updated population individual positions update the candidate solutions in the algorithm's search space, which in turn updates the parameters of the negative pressure PID controller. The relationship between the candidate solutions and the parameters of the negative pressure PID controller is expressed as follows:

[0069] ;

[0070] In the formula, x is a candidate solution, and Kp, Ki, and Kd are the proportional, integral, and derivative parameters of the negative pressure PID controller, respectively. Then, N candidate solutions, i.e., the positions of the agent individuals, are randomly initialized within the search space [lb, ub] of the initial population agent individuals. First, the position states of multiple agent individuals are initialized in the preset search space [lb, ub]. Specifically, if the dimension of the search space is D and the number of agent individuals is N, then an N×D position matrix is ​​constructed to represent the positions of the agent individuals. For the initial position of any i-th agent individual in the j-th dimension, the formula is:

[0071] Where i = 1, 2, ..., N; j = 1, 2, ..., D; Let be the lower bound of the search space in the j-th dimension. Let j be the upper bound of the search space in the j-th dimension. is a random value for the i-th agent in the j-th dimension, ranging from 0 to 1.

[0072] Furthermore, the force modeling method of the search individual in a single dimension based on the standard spring optimization algorithm and the spring stiffness model (Hooke's law) confirm that the total displacement of the individual serves as the update increment of the current position, participates in the construction of the overall position vector, and promotes the evolution of the search individual towards a better solution space;

[0073] ;in, For the total displacement of an individual, Let be the rightward displacement of the vector at the current position of individual i. Let be the displacement of individual i to the left of its current position vector.

[0074] Furthermore, in this embodiment, an improved global search strategy is introduced to update the agent's position, and the agent's position update is further corrected by introducing simulated sliding perturbation. The improved position update execution mathematical model during the mechanical equilibrium stage is as follows:

[0075] ;

[0076] in, ;

[0077] In the formula, A random number between 0 and 1. This is the step size adjustment factor. This is the best historical position. To be the globally optimal position - This represents the distance between the initial equilibrium position and the historical best position. This represents the maximum distance between the initial equilibrium position and the historical best position.

[0078] Furthermore, in this embodiment of the invention, the set negative pressure sequence of the system within T sampling times is assumed to be... The terminal negative pressure response sequence calculated from the control parameters encoded by the current agent individual is as follows: The fitness function aims to minimize the sum of squared errors, and its computational implementation model is as follows:

[0079] ;

[0080] In the formula, Let be the fitness value of the i-th agent. This represents the terminal negative pressure response value obtained at time t under the control parameters of the i-th agent individual. The target setting value for the corresponding time.

[0081] Furthermore, in this embodiment, the local development strategy based on the quantum decoherent spring multibody field configuration perturbation mechanism achieves dynamic updating of individual positions by constructing a local quantum spring subsystem and combining coupled state evolution behavior with configuration transition operations. The implementation steps are as follows:

[0082] S31. Construct a local spring field model around the current individual's position. Within the neighborhood of the current individual's position, select 10 candidate solution points connected by ranking their fitness values, centered on the current individual's position. Establish coupling relationships based on the idea that individuals only interact with spatially adjacent individuals and that interactions between individuals are not limited by spatial distance. Then, each adjacent node constitutes a local spring subsystem. Specifically, the spring field model of individual i at time t is defined as: ,in, For individual i, the local spring subsystem state, The current complex weight coefficients satisfy the normalization constraint. , Let i be the position vector of the current individual i in the search space. Let be the position vector of individual j in the search space;

[0083] S32. Construct the interference evolution process in the local spring subsystem. First, initialize the initial phase values ​​between each pair of individuals. Subsequently, in each iteration, based on the set disturbance intensity coefficient... By introducing a random perturbation signal under a standard normal distribution, the phase value of the previous round is slightly perturbed and adjusted, thereby realizing the dynamic evolution of the phase variable. The implementation process is as follows: After phase update, the complex weight coefficients of each coupling path are calculated. These coefficients consider both the spatial distance attenuation effect between individuals and the phase modulation term, and their amplitudes are standardized through normalization. These complex weight coefficients will serve as adjustment factors for the proportion of each coupling channel in the local spring subsystem. The updated complex weight coefficients are used in the next iteration S31. The implementation process of the complex weight coefficients is as follows:

[0084] Where λ is the distance decay factor, which decreases linearly with the number of iterations. For normalization operations, specifically: ;

[0085] S33. For the current individual, calculate the corresponding quantum state information entropy index based on its neighborhood coupling coefficient. The entropy value reflects the degree of order of the coupling structure inside the system. The calculation of information entropy is based on the weighted sum of the modulus square of the coupling coefficient at the current moment and its logarithmic transformation result.

[0086] ;

[0087] Subsequently, the magnitude of the information entropy change of an individual between the current moment and the previous moment is recorded. If this magnitude of change exceeds a preset transition threshold... If the system has entered the configurational imbalance region induced by quantum decoherence, a local perturbation update operation needs to be triggered; among which, a preset transition threshold is set. This represents the average value of the change in information entropy of each individual.

[0088] Finally, after the configuration transition is triggered, based on the spatial relative position difference between the current individual and its neighboring individuals, and combined with the real part strength of the coupling coefficient as the perturbation direction adjustment factor, the step size of the perturbation displacement is set by the system parameters to ensure that the local perturbation has controllable amplitude and direction adjustment capability in the structure; a perturbation transition operation is performed on the current individual position, and the implementation process is as follows:

[0089] Where step is the step size of the perturbation displacement, ranging from 0 to 1, with an initial value of 0.5, which changes randomly during the iteration; R[{c}_{i,j}\left ( {t} \right )] The real part of the complex weight coefficients determines the intensity of the disturbance direction;

[0090] S34. If the local spring system does not meet the transition triggering condition, it enters the normal perturbation stage. In this stage, for the current individual, it traverses its neighborhood set and, based on the fitness difference with each neighbor,... A nonlinearly adjusted spring tension coefficient is constructed, which is nonlinearly compressed using a hyperbolic tangent function to adapt to the flexible adjustment requirements under different fitness gradients. Subsequently, based on the spring deformation amount constituted by the difference between the tension factor and the current position, and combined with the unit direction vector for normalization guidance, the final perturbation update amount is constructed to update the individual position. The implementation process is as follows:

[0091] ;

[0092] in, Let this be the position of the agent in the (t+1)th iteration. Let be the position of the agent in the t-th iteration. The regularization term for division by zero error is set to 0.0001; The local perturbation step size decreases dynamically with the number of iterations. The mathematical model for this is: The initial step size for local perturbation is set to 0.5. The mathematical model for the spring tension coefficient is as follows: .

[0093] Furthermore, in a specific embodiment of the present invention, simulation experiments were conducted using Matlab and Simulink. First, the original position update mathematical model for the mechanical equilibrium stage and slip effect stage of the basic spring search algorithm was improved using Matlab, and a negative pressure PID control system model for the electric suction device was built using Simulink. Second, in the main function of the algorithm program, the parameters of the basic spring search algorithm and the improved spring search algorithm were set, including population size N=80, problem dimension D=3, maximum number of iterations T=30, upper limit of the search space ub=[1,1,1], lower limit of the search space lb=[0,0,0], and the search space of each individual in the population was initialized. The algorithm program was then run, and the algorithm began iterative updates. After the program finished running, Figure 3 To obtain a comparison curve of the fitness values ​​of the basic spring search algorithm and the improved spring search algorithm, from... Figure 3 It can be seen that the improved spring search algorithm converged in the 21st iteration, obtaining the optimal fitness value of 139.676, while the basic spring search algorithm failed to converge at the end of the iteration. Secondly, the basic spring search algorithm has significant shortcomings in the fitness value convergence process; the basic algorithm's descent rate is relatively slow in the initial stage, and the decrease in fitness value tends to level off after about 10 iterations, indicating insufficient global search ability and a tendency to get trapped in local optima. Furthermore, from the 20th iteration to the end, the fitness value of the basic algorithm reaches a relatively high value, failing to effectively find the global optimum. In contrast, the improved spring search algorithm's fitness value decreases significantly faster than the basic algorithm in the initial stage, completing convergence in less than 5 iterations. The significant decrease indicates an enhanced global search capability. Around 15 iterations, the fitness value of the improved algorithm stabilizes, demonstrating that the algorithm is more accurate in searching local regions during the development phase and possesses stronger local exploitation capabilities. From the perspective of the final convergence level of the fitness value, the improved algorithm's fitness value is significantly better than the basic algorithm, indicating that the improved algorithm can find the global optimum more effectively. The advantages shown by the improved spring search algorithm are mainly due to the mechanism introduced in the position update process, which links the initial equilibrium position, the historical optimal position, and the global optimal position, as well as the difference in the objective function value. These improvements enhance the diversity and accuracy of the search process, avoid getting trapped in local optima, and strengthen the algorithm's global search capability and local exploitation capability.

[0094] Furthermore, Figure 4 and Figure 5The diagrams show the process of optimizing controller parameters using existing algorithms and the algorithm of this invention, respectively. Figure 4 and Figure 5 The optimal parameters for the existing algorithm's optimization controller are Kp=0.09, Ki=0.12, and Kd=0.01, while the optimal parameters for the algorithm's optimization controller in this invention are Kp=0.17, Ki=0.15, and Kd=0.14. (Analysis) Figure 4 There are obvious shortcomings in optimizing the parameters of the negative pressure PID controller. First, the initial optimization process is slow in the first five iterations, making it difficult to quickly find a high-quality solution. Second, the controller parameter values ​​fluctuate significantly, especially in the middle of the iteration process (from the 10th to the 20th iteration), exhibiting obvious instability. This fluctuation may lead to a decrease in control performance and affect the stability of the system. Furthermore, the number of iterations required for the parameters to converge to a stable value is relatively large, resulting in low optimization efficiency. Figure 5 In the first five iterations, the parameter optimization speed is accelerated, and the values ​​of Kp, Ki, and Kd quickly approach the target values, demonstrating a stronger global search capability. In the later stages, after the 15th iteration, parameter fluctuations are significantly reduced, and the convergence speed is faster, indicating that the algorithm's local development capability has been enhanced. At the same time, the final convergence value is smoother and more stable than the basic algorithm, and the optimization result is more accurate. Overall, the algorithm of this invention is superior to existing algorithms in both global search efficiency and local convergence accuracy, which helps to improve the dynamic response and steady-state control performance of the negative pressure PID controller.

[0095] Furthermore, the optimal controller parameters obtained by the existing algorithm and the algorithm of this invention are input into the PID control system model of the electric suction device for negative pressure. The target value of the negative pressure of the electric suction device is set to 5 units, resulting in... Figure 6 ;analyze Figure 6As can be seen from the data in the figure, the method of the present invention reaches the target value of 5 in approximately 150 milliseconds in the initial stage, while the existing method only requires approximately 130 milliseconds to reach the target negative pressure value, reducing the response time by approximately 13%. Regarding overshoot, the existing method reaches a negative pressure peak of approximately 5.3, with an overshoot of 6%, while the method of the present invention achieves a negative pressure peak closer to the target value, at only approximately 5.1, with an overshoot reduced to 2%. This optimization can prevent tissue damage to patients caused by negative pressure overshoot in actual use, providing a safer treatment environment for patients. In terms of stability, the existing method still exhibits significant oscillations of approximately 0.5 after reaching the target negative pressure value, requiring approximately 300 milliseconds to gradually stabilize, while the method of the present invention stabilizes within only 200 milliseconds. A stable state with an oscillation amplitude of less than 0.1 can be achieved. This indicates that the method of the present invention can quickly converge to the target value and maintain stability, which is particularly crucial in surgical suction processes that require maintaining stable negative pressure for a long time, and can significantly improve the accuracy and reliability of the treatment process. In addition, in terms of steady-state error, the existing method still has a deviation of about 0.1 in the system output at 330 milliseconds, while the steady-state error of the method of the present invention is less than 0.05, and the accuracy is improved by about 2 times. In summary, the method of the present invention provides a more efficient, safe and accurate optimization scheme in the negative pressure PID control scenario of electric suction devices by reducing response time, overshoot and oscillation amplitude, and improving steady-state accuracy, which helps to significantly improve the quality of patient treatment in clinical applications.

Claims

1. A method for optimizing negative pressure control in an electric suction device, characterized in that, The specific steps are as follows: S1. Construct a suction negative pressure system model, which includes a motor drive module, a pump drive flow output module, a dynamic change module for cavity pressure, and a terminal negative pressure output module. The steps for constructing the terminal negative pressure output module include: dynamically modeling the instantaneous impedance changes in the suction channel caused by sudden liquid adsorption or blockage by constructing a channel impedance disturbance module; constructing an impedance function model related to the time derivative based on the influence of the impedance change rate to obtain the channel impedance. Based on cavity pressure, suction flow rate, and the channel impedance. A terminal negative pressure output module is constructed to calculate the actual output negative pressure value of the terminal suction port; S2. Obtain the current system negative pressure setpoint and the real-time terminal negative pressure output negative pressure value, calculate the difference between the two to form an error signal, input the error signal to the PID controller of the suction negative pressure system, and output the control signal u(t); S3. The control signal acts on the motor drive module, driving the pump body to generate suction flow and controlling the pressure change inside the suction chamber. Combined with the impedance change of the suction channel, the final output negative pressure is dynamically estimated to obtain the final negative pressure value. The suction negative pressure system PID controller uses an improved spring search algorithm to optimize the parameters of the negative pressure PID controller. The specific method is as follows: S301. Randomly initialize N candidate solutions, denoted as surrogate individuals. Each candidate solution is a three-dimensional vector representing the parameters Kp, Ki, and Kd of the PID controller of the negative pressure system. S302. Set the parameters of the improved spring search algorithm, including population size N, problem dimension D, maximum number of iterations T, upper bound of search space ub, lower bound of search space lb, and initialize the search space of the population agent individuals. S303. During the mechanical equilibrium stage, an improved global search strategy is introduced to update the agent individual's position, and the agent individual's position update is further corrected by introducing simulated sliding perturbation. S304. A local development strategy based on the quantum decoherent spring multibody field configuration perturbation mechanism, which achieves dynamic updating of individual positions by constructing a local quantum spring subsystem and combining coupled state evolution behavior with configuration transition operation; S305. Calculate the fitness value of each agent individual and record the position of the individual corresponding to the current minimum fitness value; S306. When the number of iterations reaches the predetermined maximum value, the optimization process terminates, outputs the position of the agent individual corresponding to the minimum fitness value, and obtains the parameters Kp, Ki, and Kd of the PID controller of the negative pressure system by inverse mapping; otherwise, it returns to execute S303. S4. Feedback the final negative pressure value to the PID controller of the suction negative pressure system to form a closed-loop control path and optimize the negative pressure control of the electric suction device.

2. The method for optimizing negative pressure control in an electric suction device according to claim 1, characterized in that, The construction of the suction negative pressure system model first involves designing a motor drive module. The control signal u(t) output by the PID of the suction negative pressure system is used as the input to the motor, and the motor response is the output speed ω(t).

3. The method for optimizing negative pressure control in an electric suction device according to claim 2, characterized in that, The pump body drive flow output module drives the negative pressure pump body to rotate according to the instantaneous speed output by the motor, and establishes a linear gain relationship between the gas flow rate Q(t) attracted by the pump body and the speed.

4. The method for optimizing negative pressure control in an electric suction device according to claim 3, characterized in that, The dynamic change of cavity pressure is established based on the gas flow rate Q(t) drawn by the pump, cavity volume, atmospheric pressure, and system leakage impedance, and a dynamic change model of the internal pressure of the cavity is established.

5. The negative pressure control optimization method for an electric suction device according to claim 4, characterized in that, The improved global search strategy includes introducing the distance between the initial equilibrium position and the historical best position, normalizing it to the distance between the global best position and the historical best position, and using the difference between the historical best position and the initial equilibrium position as a guiding term based on the normalized distance ratio and step size adjustment factor to improve the position update mathematical model. Specifically: ； in, ; In the formula, Let this be the position of the agent in the (t+1)th iteration. A random number between 0 and 1. For the total displacement of an individual, This is the step size adjustment factor. This is the best historical position. To be the globally optimal position - This represents the distance between the initial equilibrium position and the historical best position. This represents the maximum distance between the initial equilibrium position and the historical best position.

6. The method for optimizing negative pressure control in an electric suction device according to claim 5, characterized in that, The local exploitation strategy based on the quantum decoherent spring multibody field configuration perturbation mechanism includes: A local spring field model is constructed around the current individual's location. This spring field model is located within the neighborhood of the current individual's location, with N centered on the current individual's location. i Candidate solution points are connected by fitness values. The coupling relationship is established based on the idea that individuals only interact with spatially adjacent individuals and that the interaction between individuals is not limited by spatial distance. Each adjacent node is formed into a local spring subsystem. In the local spring subsystem, the initial phase values ​​between each pair of individuals are initialized. Subsequently, in each iteration, based on the set disturbance intensity coefficient... A random perturbation signal under a standard normal distribution is introduced to make a perturbation adjustment to the phase value of the previous round, so as to realize the dynamic update of the phase variable. After the phase update is completed, the complex weight coefficient of each coupling path is calculated. The complex weight coefficient takes into account the spatial distance attenuation effect between individuals and the phase modulation term. The amplitude of the complex weight coefficient is uniformly standardized by normalization operation. The complex weight coefficient is used as an adjustment factor for the proportion of each coupling channel in the local spring subsystem and is used for the next iteration. For the current individual, the corresponding quantum state information entropy index is calculated based on its neighborhood coupling coefficient. The entropy value reflects the degree of order in the coupling structure within the system. The information entropy is calculated based on the weighted sum of the modulus square of the coupling coefficient at the current moment and its logarithmic transformation result. Subsequently, the magnitude of the information entropy change of the individual between the current moment and the previous moment is recorded. If the magnitude of the change exceeds a preset transition threshold... If the system has entered the configuration imbalance region induced by quantum decoherence, a local perturbation update operation needs to be triggered. Based on the spatial relative position difference between the current individual and the neighboring individuals, and combined with the real part strength of the coupling coefficient as the perturbation direction adjustment factor, a perturbation jump operation is performed on the current individual position. If the local spring system does not meet the transition triggering condition, it enters the normal perturbation stage. In this stage, for the current individual, it traverses its neighborhood set and constructs a nonlinearly adjusted spring tension coefficient based on the fitness difference with each neighbor. The tension coefficient is nonlinearly compressed through the hyperbolic tangent function. Subsequently, based on the spring deformation amount formed by the difference between the tension factor and the current position, it is normalized by combining the unit direction vector to construct the final perturbation update amount and update the individual position.

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